Implant fit analysis

Abstract

To provide systems and methods for intraoperative implant fit analysis and longevity prediction for a prosthetic implant to be integrated with the physiological tissue of a patient.SOLUTION: A method comprises the steps of: collection of data via a plurality of sensors situated in proximity to tissue and an implant, and a plurality of data sources; determining tissue and implant state and morphology based on the collected data; generating compatibility information between the tissue and implant based on the determined tissue and implant state and morphology; processing compatibility information into a form adapted for evaluation against a predetermined comparator; generating means of predicting postoperative implant performance and longevity utilizing the historical dataset of comparison information and postoperative results; and generating and providing corrective information for alteration of the tissue state and morphology for improved postoperative implant performance and longevity.SELECTED DRAWING: None

Description

[Technical Field]

[0001] The present invention relates to systems and methods for surgical bioimplantation, and more particularly to orthopedic hardware systems during surgical procedures, such as total knee replacement, total hip replacement, or hip resurfacing surgery.

[0002] The present invention has been developed primarily for use in a method and system for quality analysis of the implantation process and predicted service life of orthopedic implants within an intraoperative environment, and will be described hereinafter with reference to this application, however, it should be understood that the invention is not limited to this particular field of use. [Background technology]

[0003] Any discussion of background art throughout the specification should not be taken in any way as an admission that such background art is prior art or that such background art is widely known or forms part of the common general knowledge in the field in Australia or throughout the world.

[0004] All references cited herein, including any patents or patent applications, are incorporated herein by reference. No admission is made that any reference constitutes prior art. The discussion of a reference states what its author asserts, and the applicant reserves the right to challenge the accuracy and pertinence of the cited documents. Although several prior art publications are referenced herein, it should be expressly understood that this reference does not constitute an admission that any of these documents form part of the common general knowledge in the art in Australia or any other country.

[0005] Understanding the quality and details of the implant allows various modifications and precautions to be made during the surgical procedure, which can result in numerous patient benefits, including extending the life or useful life of the implant and increasing its success and recovery rate.

[0006] This is particularly evident in surgeries involving the musculoskeletal system, e.g., the knee or hip, where implants are generally subjected to significant levels of stress. Any medical error made during the implantation process can exacerbate or negatively react to this stress, which can lead to physical effects on bodily movement, likely accompanied by some degree of pain for the patient. Articular tissues, such as cartilage, muscle, and bone, make up the joints within this system that allow it to function, and the performance of the joint naturally deteriorates as they deteriorate. By replacing a certain amount of this deteriorated tissue with a prosthetic implant, it is possible to restore some of the lost performance.

[0007] Total knee arthroplasty is a prominent form of orthopedic surgery in which a predetermined amount of hard tissue must be removed from the bones involved in the knee joint using osteotomy. A prosthetic implant is then secured to the remaining bone to replace the removed tissue. This surgery is typically required when articular tissue, such as the cartilage surrounding the femur, tibia, and patella, begins to wear down. This causes the patient's bones in the affected joint to rub against each other during normal movement, enduring increased levels of stress that would normally be absorbed by cartilage. By inserting prosthetic implants onto these bones, designed to absorb stress in the patient's original bone location, the painful effects of the weakened articular tissue can be significantly reduced.

[0008] According to the National Center for Health Statistics, more than 700,000 total knee arthroplasty procedures are performed annually in the United States alone, and that number is expected to rise to 3,480,000 by 2030. The majority of these procedures are initially successful in patients (whose average age is 66.2 years), who report significant pain reduction and increased mobility. However, after a period of postoperative time, problems can arise that require total knee revision surgery. This revision surgery is currently required for approximately 8% of all knee replacement procedures, and the total annual revision surgery rate will begin to increase in tandem with the number of annual procedures by 2030.

[0009] A total knee revision involves the removal of an existing implant from the joint and its subsequent replacement with a new implant. This type or procedure is generally considered to be significantly more complex than a primary joint (e.g., knee or hip) arthroplasty surgery. This is in part because the implant may be well fixed and bone loss may occur with implant removal.

[0010] Prosthetic implants can be fixed to articular hard tissue using one of two different methods. The first is by directly attaching to the hard tissue (an "interference fit") and relying on osseointegration, which refers to the hard tissue naturally growing into / over the prosthetic implant and stabilizing it. The second is through a fixative such as bone cement, which forms a strong bond between the prosthetic implant and the hard tissue. When the implant needs to be removed, the natural bone growth or inserted fixative and any other articular tissue that prevents removal must be destroyed.

[0011] The remaining tissue, including hard tissue such as bone, can then be sculpted using multiple osteotomies to provide dimensions that match the new prosthetic implant. However, for subsequent arthroplasty surgery, depending on the amount of hard tissue lost during the process of removing the previous implant, the amount of remaining hard tissue may be insufficient for further tissue sculpting processes. Bone graft may be required in this scenario, which is hard tissue extracted from a different area of the patient and transplanted into the implantation area. This requires preoperative planning, specialized equipment, and increased surgical skill. The longevity and overall satisfaction of revision surgery are inferior to those of primary replacement surgery, and there is typically a significantly increased risk of complications and adverse issues.

[0012] With respect to total knee arthroplasty, the need for total knee revision surgery is the result of one or more distinct causes. These include aseptic loosening, infection, polyethylene wear, instability, pain, osteolysis, and malposition, which are responsible for 23.1%, 18.4%, 18.1%, 17.7%, 9.3%, and 4.5% to 2.9% of all revisions, respectively. These causes are interdependent, and the occurrence of one can potentially be precipitated or influenced by the onset of another.

[0013] Aseptic loosening is the leading cause of revision surgery and refers to the breakdown of fixation at the implant and articular tissue interface, leading to increased levels of pain and joint instability for patients. The etiology of aseptic loosening involves four major causes. One such cause is a biological response to wear particles released from the prosthetic implant during use. When sufficient stress is applied, it is possible that small particles within a critical range of 0.3 to 10 micrometers can break away from the implant. Depending on the health and genetic makeup of the patient's articular tissue, this can then generate a phagocyte-based inflammatory response, leading to osteolysis.

[0014] Another cause of aseptic loosening can be the buildup of intra-articular fluid pressure, which is the result of excessive production of synovial fluid due to exposed hard tissue or wear particles surrounding the joint. This excess synovial fluid creates additional pressure, which can result in abnormal bone perfusion or ischemia, leading to necrosis and osteolysis.

[0015] Another cause of aseptic loosening may be the physical design of the implant; the surface pattern and contour influence the rate and likelihood of osseointegration. If this influence is negative, the amount of ingrowth may not be sufficient to stabilize and fixate the prosthesis.

[0016] An additional cause of aseptic loosening may be the patient's individual biological makeup, including characteristics unique to the patient, such as the patient's age and habits, any pre-existing infections or diseases that may affect the joints, and their genetics. The risk may be increased if the patient regularly participates in physical exercise, such as running, or if they have naturally weak joints.

[0017] Once aseptic loosening begins to occur due to any one or more of the above causes, problems such as infection and malposition will worsen with continued loosening of the joint prosthesis, further progressing the patient towards revision surgery.

[0018] These causes can be attributed to prosthetic fit, which is defined as the correlation between the condition and morphology of the implant and the underlying articular tissue.

[0019] Conditions determine the degree to which the implant and joint tissue can coexist and the potential for problems, which can arise immediately or postoperatively. The implant material and tissue health generally determine adequate fixation.

[0020] The morphology determines the degree to which the implant will physically attach and affect the tissue and its shape. If the implant or tissue have different connecting surfaces or shapes, the distribution of contact between them may be irregular or minimal, leading to potential problems after surgery. This may be the result of the connecting geometry of either the implant itself or the implant receiving site being altered due to stresses caused by the insertion of the implant, causing a once compatible morphology to no longer be so.

[0021] The importance of this fit and the risk of revision is further increased by the patient's physiological condition: if they are relatively young or maintain an active lifestyle where the implant is under constant stress, potential new problems can be created while existing ones will be exacerbated.

[0022] This means that the quality and longevity of an implantation procedure are determined, at least in part, by the condition and morphology of the connecting components at the time of the implant surgery and the degree to which these attributes allow them to physically fit together. If these attributes are poor, the risk of revision due to the previously mentioned issues is relatively high, while if not, it is typically not applicable or is much less likely to occur. The skill and precision required by the procedure is likely to be the biggest reason for variations in quality and typically depends on the experience and ability of the primary surgeon.

[0023] Common approaches to measuring the quality of implantation procedures generally involve reliance on either existing instrumentation or manual observation. Existing instrumentation typically defines the desired hard tissue morphology and different bone resections from a fixed set of possible options required to achieve it. They operate under the assumption that after the bone resections are performed, the remaining hard tissue will be a perfect fit for the implant.

[0024] Most of the measurements used in defining these osteotomies are calculated based on intrinsic properties that may exist for a particular group of hard tissues, such as the mechanical axis for the knee joint. This means that they depend on the accuracy of these intrinsic properties and the assumption that the structures of all relevant hard tissues will be identical or highly comparable. However, given the variability between hard tissues in different patients, such dependence does not necessarily lead to accurate results.

[0025] Instrumentation measurements are also typically performed independently of the actual procedure. This may provide quantification of measurable attributes, but cannot, in itself, ensure that the procedure was completed successfully. This means that inconsistencies such as the straightness of a particular cut or the degree to which the instrumentation can be aligned and positioned can further affect its accuracy. Thus, it is not uncommon for the final hard tissue morphology to have various imperfections.

[0026] The surgeon or other surgical personnel generally use manual observation techniques to determine whether the implant fit is adequate or whether additional modifications are required. This judgment is built up over time and based on experience, using intraoperative stimuli and feedback. This may include the resistance felt in response to implant insertion, the visible area free of contact with the inserted implant, and the range and freedom of movement the implant offers when manipulated. Because most of these observations are subjective, cannot be verified, and depend primarily on the personnel involved, their overall contribution to the implantation procedure is difficult to determine and may not be positive.

[0027] The invention disclosed herein provides a method for performing implant fit analysis and life expectancy prediction. Summary of the Invention [Means for solving the problem]

[0028] It is an object of the present invention to overcome or ameliorate at least one or more of the disadvantages of the prior art, or to provide a useful alternative.

[0029] One embodiment provides a computer program product for carrying out the methods as described herein.

[0030] One embodiment provides a non-transitory carrier medium for carrying computer executable code that, when executed on a processor, causes the processor to perform a method as described herein.

[0031] One embodiment provides a system configured to perform the methods as described herein.

[0032] The present invention provides systems and methods for implant fitness analysis and life expectancy prediction. In particular, the present invention provides methods for the collection of data from different sensors, the processing and subsequent interpretation of that data, and the generation of fitness information based on these results.

[0033] In one aspect, the present invention provides a system for collecting different types of data that can describe various attributes related to the quality of fit and immediate fit that exists between an implant and specific hard tissue, based on a method of data generation. A preferred system comprises a plurality of different sensors and possible acquisition tools within a typical surgical environment, where the sensors work collectively in an automated manner to assist the surgeon.

[0034] Sensor selection includes at least one sensor that can exist independently or as part of a sensor system or set of sensors. Individual sensors can be operable to monitor, sense, and collect data regarding various attributes, characteristics, events, or measurements from different angles, positions, proximity, placement, or orientations with, within, or directed at their objects, which can be joint tissues, implants, interfaces between tissues and implants, the surrounding environment, the results of a procedure or interaction, individual or collective systems or devices, and any other advantageous source or set of sources associated therewith. Sensors can be completely self-contained or can require additional devices, services, platforms, or conditions to interface with, configure, or operate properly. For example, devices capable of creating controlled illumination conditions, such as LED lights, may be required for some sensors. Similarly, motion platforms or otherwise maneuverable attachments capable of moving or repositioning sensors may also be required.

[0035] Selected sensors may include those based on Raman spectroscopy, spectral imaging, hyperspectral imaging, optical imaging, thermal imaging, fluorescence spectroscopy, microscopy, acoustics, 3D metrology, optical coherence tomography, position, movement, balance, laser power, and any other single, combined, or sequential sensing modality.

[0036] Sensed attributes include the state or morphology of the tissue or implant. State attributes may include composition, hydration, density, necrosis, discoloration, reflectance, thermal consistency, degradation, particle dissolution, and any other single, combination, or series of state descriptors. Morphological attributes may include shape, flatness, parallelism, roughness, waviness, peak distribution, porosity, stiffness, and any other single, combination, or series of morphological descriptors.

[0037] In one embodiment, sensing may occur during a surgical procedure, and the sensing process may pause, omit, or otherwise ignore sensed data in scenarios where any disturbance occurs that results in a change to viable sensing conditions, which may include sensor obstruction by personnel, excessive light or noise, unwanted movement of the subject, or any other single, combination, or sequence of adverse sensing conditions.

[0038] In another embodiment, sensing may interrupt the natural progression of the surgical procedure for a predetermined or intraoperatively determined duration to provide an environment conducive to the sensing procedure. This interruption may include changes to the surgical environment, which may include temporary removal of personnel, lighting modifications or dimming, surrounding alterations, subject repositioning, or any other single, combination, or sequence of environmental changes.

[0039] In one embodiment, sensed data may be interpreted based on the individual sensor that provided it, without regard to other sensors that may surround or operate in conjunction with it.

[0040] In another embodiment, sensed data may be interpreted based on a system, set, or sets of sensors, and inclusion may be consistent with or defined by an attribute, similarity, condition, state, or any other single, combination, or sequence of grouping factors.

[0041] In another embodiment, sensed data interpreted based on a collection of sensors may be compiled to provide increasingly accurate information, to be used as a fault tolerance means for determining operational efficiency, or in any other single, combination, or manner in which integration of the sensors involved may be beneficial.

[0042] In further embodiments, interpretation of sensed data resulting from a single, system, set, or multiple sets of sensors may occur regardless of or with respect to environmental or internal conditions and physical sensor placement, which may include temperature, humidity, pressure, variable amounts of illumination and its direction, different positions, angles, proximity, or placement, or any other single, combination, or series of contributing factors.

[0043] In another aspect, the present invention provides methods for processing sensed data into at least one different sequential form that can increase its usability or evaluability. Preferred methods include cleaning the data to remove noise or redundancy, changing the format or arrangement of the data, sampling the data to isolate portions or areas that may be considered useful, normalizing the data and limiting it to have comparable ranges, decomposing the data and defining its component elements, or aggregating them into entities of significant utility.

[0044] The sensed data provided by any particular selection of sensors will depend on those sensors and may include specific wavelengths, signals, arbitrary numbers, formulas, coordinates, models, or any other single, combination, or sequence of directly or indirectly interpretable data forms.

[0045] Processing of sensed data may involve a variety of different, similar, or identical methods in the same or alternating order to produce single or multiple subsequent forms leading to a final form. Some algorithms or methods may not be available for every form of data or sensor type, which may vary, subject to appropriate modifications. Each individual form may beneficially contribute to subsequent forms that are not necessarily included in the final form.

[0046] Constituent elements represent individual or grouped components present in the original data. The number and type of constituent elements resulting depend on the data format, any previously performed processing methods, the context or environment in which sensing occurred, or any other single, combined, or sequence of conditions that may result in multiple components being present in the data. Constituent elements of joint tissue typically consist of cancellous bone, cortical bone, cartilage, fat, ligament, muscle, capsule, or meniscus. Additional constituent elements may exist as differentiations of these, which may include composition, hydration, density, necrosis, reflectance, temperature, or any other single, combined, or sequence of elements that may potentially describe the state of joint tissue.

[0047] In one embodiment, large segments or sets of incorrect or erroneous data whose added value does not significantly affect the conclusions drawn from the remaining data may be removed. This may include data consisting of outliers, in which an event or aspect of interest that may support it does not occur, or any other single, combination, or sequence of conditions from which extracting utility is impractical or negligible.

[0048] In another embodiment, sets or series of data corresponding to identical, similar, or different events with structural similarity may be averaged or otherwise combined to outline portions or areas where variation may exist, including noise or erroneous data, which can then be removed from either the single or combined sets or series of data.

[0049] In another embodiment, similar data that have little value individually may be grouped or combined into a single or multiple representative data sets, reducing the sheer volume of data without significantly affecting any conclusions drawn.

[0050] In one embodiment, the format, form, or structure of the data may be rearranged, altered, or changed through methods that may include flattening the data or changing the locations or relationships between specific or ordered values to provide additional or alternative utility.

[0051] In one embodiment, the data may be sampled to extract areas or portions deemed more advantageous, or to create a series or set of data samples that can be processed or manipulated separately for purposes such as cross-validation or testing.

[0052] In one embodiment, the data may be normalized through algorithms and methods such as constant shift, smoothing, scaling, standard normal variate, baseline correction, continuum removal, or any other single, combination, or sequence of algorithms and methods capable of improving data consistency.

[0053] In one embodiment, the data may be decomposed or deconvolved into its constituent elements or features, which may be accomplished through algorithms and methods including an automated target generation process, pixel purity index, N-FINDR, independent component analysis, nonlinear least squares, fuzzy k-means, or any other single, combined, or sequence of algorithms and methods capable of decomposition. Some of these algorithms and methods may not be possible without potential modification, depending on the format of the data provided and its purpose.

[0054] In another embodiment, constituent elements may be identified prior to initiating their extraction and determining what is present in the data and any indicators that may aid in their extraction.

[0055] In another embodiment, the constituent elements of a series or set may be averaged or combined in a beneficial manner, provided they share or do not share any similar patterns or other elements that can be used as a means of grouping. This may occur when the number of constituent elements exceeds the expected number.

[0056] In further embodiments, the decomposition may include the removal of data dimensionality to reduce complexity or computational burden, which may be performed through algorithms and methods including decision trees, random forests, highly correlated filters, backward feature elimination, factor analysis, principal component analysis, linear discriminant analysis, generalized discriminant analysis, or any other single, combination, or sequence of algorithms and methods capable of removing dimensions.

[0057] In one embodiment, representative, component, or otherwise single sets of data, elements, or features may be aggregated together into a single entity or fewer entities that may be more easily processed while maintaining similar or increased usefulness.

[0058] In another aspect, the present invention provides methods for interpreting the processed data into at least one different subsequent form that may increase its usefulness. Preferred methods include calculating custom or standardized mathematical or statistical measures, provided by an external party or an internal controller, and training and implementing machine learning, data science, and mathematical algorithms and methods.

[0059] Interpretation of the processed data may involve a variety of different, similar, or identical methods, in the same or alternating order, to yield single or multiple subsequent forms leading to the final form. Some algorithms or methods may not be available for every form of data or sensor type, which may vary, subject to appropriate modifications. Each individual form may beneficially contribute to subsequent forms that are not necessarily included in the final form.

[0060] In one embodiment, measurements defined by mathematical or statistical formulas, theories, or concepts, such as mean, standard deviation, and variance, may be calculated based on the processed data to gain insight into the summarized information.

[0061] In another embodiment, measurements defined by a standardization body such as the International Organization for Standardization (ISO) or customized to a specific subject or environment related to the processed data may be calculated to gain insight into specific attributes or characteristics such as surface flatness and roughness.

[0062] In one embodiment, prior medical records or medical history directly or indirectly related to a specific patient may be provided.

[0063] In another embodiment, explicit information relating to the implant or other fixed or manufactured entity may be provided by the company directly involved in its production or manufacturing.

[0064] In another embodiment, pre-operative scans, investigations, or pre-operative procedures intended to develop further information related to a specific problem or issue may be provided.

[0065] In further embodiments, trained medical staff or otherwise personnel with collation capabilities can provide observations or implicit conclusions surrounding or relating to the subject, directly or indirectly, based on currently accessible knowledge and prior knowledge.

[0066] In one embodiment, the control unit responsible for managing a particular sensor or collection of sensors may provide analytical results from data collected and processed internally.

[0067] In one embodiment, the processed data may require additional processing or manipulation prior to being provided to one or more machine learning, data science, or mathematical algorithms and methods.

[0068] In another embodiment, the processed data or sets of processed data may be used to train one or more machine learning, data science, or mathematical algorithms and methods.

[0069] In further embodiments, the processed data may be provided to a single or set of trained machine learning, data science, or mathematical algorithms and methods to produce a corresponding output.

[0070] In another aspect, the present invention provides a method for generating fitness information based on interpreted data of tissue, associated prosthetic implants, and the interfaces therebetween. A preferred method includes generating a fitness measure, analyzing the effects from implant insertion or fixation, assessing implant fitness, and predicting implant life and performance.

[0071] The generation of compatibility information may involve a variety of different, similar, or identical methods in the same or alternating order to yield single or multiple subsequent forms leading to the final form. Some algorithms or methods may not be available for every form of data or sensor type, which may vary, subject to appropriate modifications. Each individual form may beneficially contribute to subsequent forms that are not necessarily included in the final form.

[0072] Compatibility information includes any analysis or conclusion that may describe the quality of the interface between the implant and a specific tissue before implantation, immediately after implantation, and after a duration of time. This includes the state of the tissue or biology and the way the implant may interact with one another, the physical connectivity of the two in terms of their morphology, the accuracy of insertion, and the longevity of the interface when these factors and the nature of the individual undergoing the procedure are taken into account.

[0073] The interface between the prosthetic implant and the tissue can either rely on bone ingrowth through a process known as osseointegration, or can be created artificially through a fixative such as bone cement.

[0074] In one embodiment, the health of the tissue and the patient may be considered to determine the fixability and subsequent viability of the connection interface.

[0075] In one embodiment, the materials comprising the prosthetic implant can be compared against the tissue condition and any required fixatives to determine if any adverse reactions may occur both intra- and post-operatively.

[0076] In another embodiment, the patient's lifestyle, including their activity level and daily routine, can be considered to determine the stresses that the prosthetic implant and connection interface can withstand.

[0077] In one embodiment, the shape and form of the implant can be compared to that of the tissue to determine the feasibility and difficulty of insertion.

[0078] In another embodiment, the degree and distribution of contact that the implant will make with tissue upon insertion can be determined to gauge the fixability and longevity of the connection interface.

[0079] In one embodiment, any surface disruption, density loss, or other effects to the tissue or implant upon insertion can be determined to inform other measurements and comparisons so that compatibility information can be adjusted accordingly.

[0080] In another embodiment, the diffusion or translocation of any fixative applied to the implant or tissue upon insertion can be determined to ensure that sufficient distribution remains to achieve adequate fixation.

[0081] In one embodiment, the ideal fit of the implant to the tissue may be calculated and compared to its actual fit to determine the amount of deviation.

[0082] In another embodiment, changes in the position and rotation of the inserted implant can be applied that increase the quality of implantation and result in less deviation when compared to the calculated ideal fit.

[0083] In one embodiment, the generated conformance and verified embedded life and performance data may be used to train machine learning, data science, or mathematical algorithms and methods.

[0084] In another embodiment, verified implant life and performance data can be retrieved from previous consenting patients who have had the implant for a set duration under specific conditions.

[0085] In another embodiment, the machine learning, data science, and mathematical algorithms or methods may be based on supervised approaches, which may include linear and polynomial regression, logistic regression, naive Bayes networks, Bayes networks, support vector machines, decision trees, random forests, k-nearest neighbor classifiers, neural networks, and any other single, combined, or sequential supervised approaches.

[0086] In one embodiment, the relevance data may need to be processed to achieve a more assessable form using algorithms and methods that may include those described in the second aspect of the invention.

[0087] In one embodiment, the validated data pool is divided into at least two divisions, which divisions are not necessarily uniform or proportional.

[0088] In another embodiment, single or sets of partitioned validated data may be provided to single or multiple machine learning, data science, or mathematical algorithms or methods in a sequential, simultaneous, or cyclical manner.

[0089] In another embodiment, all or a portion of the single or set of remaining partitioned validated data may be provided to a single or multiple previously trained machine learning, data science, or mathematical algorithms and methods to measure the accuracy of the corresponding outputs against externally validated outputs.

[0090] In another embodiment, the accuracy of a particular trained machine learning, data science, or mathematical algorithm and method may be measured as sufficient according to its statistical significance, which may be affected or defined by its predictive or inferential application.

[0091] In further embodiments, if accuracy does not prove sufficient, selected validated data, its input procedures, single or multiple machine learning, data science, or mathematical algorithms and methods, and any other single, combination, or chain of causality may be modified, removed, rearranged, or added, potentially resulting in increased accuracy.

[0092] In one embodiment, the fitness data may be provided to a single or set of trained machine learning, data science, or mathematical algorithms or methods to produce corresponding outputs.

[0093] In another embodiment, corresponding outputs from at least two machine learning, data science, or mathematical algorithms or methods may be averaged, combined, or compared, potentially to arrive at an increasingly conclusive conclusion.

[0094] In one embodiment, a simulation can be constructed to test the entire or set of available data under various conditions, which can provide insight into phenomena such as the effects of implant insertion and variable levels of stress applied to the connection interface.

[0095] In one embodiment, correction information for modifying tissue morphology is generated to inform the surgeon of the desired set of procedures to improve implant performance and longevity.

[0096] In one embodiment, the correction information samples a set of different possible treatments for predicted post-operative implant performance.

[0097] In another embodiment, the corrective information includes a numerical quantification of implant performance and longevity relative to the currently existing and subsequent resulting tissue morphology after the proposed set of procedures is performed.

[0098] In another embodiment, the correction information has a pre-configured threshold above which the correction procedure may be identified as infeasible given the surgical cutting technique being applied and its inherent imprecision.

[0099] It is therefore apparent that current methods used to measure and ensure implant quality are suboptimal yet still frequently used. Accordingly, a need exists for improved systems and methods for measuring critical parameters and assessing prosthesis viability for orthopedic surgical procedures, and for use by surgeons during orthopedic prosthetic implantation procedures to maximize prosthesis integration and viability for the long-term benefit of the patient.

[0100] According to a first aspect of the present invention, a method for intraoperative implant fit analysis and life prediction for a prosthetic implant to be integrated with a patient's physiological tissue is provided. The method may include collecting data via multiple sensors and multiple data sources mounted proximate the tissue and the implant. The method may include the further step of determining the condition and morphology of the tissue and the implant based on the collected data. The method may further include generating compatibility information between the tissue and the implant based on the determined tissue and implant condition and morphology. The method may further include processing the compatibility information into a format adapted for evaluation against a predetermined comparator. The method may further include generating a means for predicting postoperative implant performance and life using the comparison information and a historical dataset of postoperative results. The method may further include generating and providing correction information for altering the tissue condition and morphology for improved postoperative implant performance and life.

[0101] According to a particular arrangement of the first aspect, there is provided a method for intraoperative implant fit analysis and life prediction for a prosthetic implant to be integrated with a patient's physiological tissue, the method including the steps of collecting data via a plurality of sensors and a plurality of data sources mounted in proximity to the tissue and the implant; determining tissue and implant condition and morphology based on the collected data; generating compatibility information between the tissue and the implant based on the determined tissue and implant condition and morphology; processing the compatibility information into a format adapted for evaluation against a predetermined comparator; utilizing the comparison information and a historical dataset of post-operative results to generate a means for predicting post-operative implant performance and life; and generating and providing correction information for altering tissue condition and morphology for improved post-operative implant performance and life.

[0102] The tissue may include biological tissue, including bone. The prosthetic implant may include a knee prosthesis or a hip prosthesis. The prosthetic implant may include one or more features, including kerfs or patterns on one or more surfaces, to promote osseointegration and / or increase the rigidity of fixation to the tissue.

[0103] The sensors may include at least one sensor that exists independently or as part of a sensor system or set of sensors. The sensors may include at least one sensor that is completely self-contained.

[0104] The sensors may include at least one sensor that requires additional devices, services, conditions, platforms, or any other single, combination, or set of requirements in order to be properly interfaced, configured, or operated.

[0105] The sensors may include at least one sensor individually configured to monitor, sense, collect, and provide data based on various characteristics, attributes, events, or measures from different angles, positions, proximity, nearness, movement, speed, placement, or arrangement present in, within, or oriented by, those objects.

[0106] The subject may include one or more of a tissue, an implant, a connection interface, the surrounding environment, the result of a procedure or interaction, an individual or collective system or device, and any other source or set of sources.

[0107] An object may be treated, altered, or prepared to affect its original, initial, or current state for purposes of preservation, identification, uniformity, fixation, or any other single, combined, or sequential purpose.

[0108] The object may be modified structurally, chemically, or through any intraoperative procedure, surgery, or any other single, combined, or sequence of approaches that can change its shape as part of, or independently of, any other single, combined, or sequence of medical operations.

[0109] The sensor may be configured to operate in an automated manner through manual triggering, or through any combination or sequence of manual and automatic triggering.

[0110] The manual trigger may include a button, a voice command, a gesture control, or a manual trigger including an alternative physical actuation.

[0111] The sensors may be configured to engage in sensing indefinitely, periodically, once, or in any other single, combination, or sequence of sensing approaches as influenced by conditions, environment, user control, sensor configuration, and any other single, combination, or sequence of variables that can have a direct or indirect influence.

[0112] Sensing can be configured to operate in real time, near real time, through some form of delayed processing, or in any other single, combined, or sequence of processing approaches that can be influenced by conditions, environment, user control, sensor configuration, and any other single, combined, or sequence of variables that can have a direct or indirect influence.

[0113] A sensor may require external intervention to operate correctly, including changes in its position, angle, proximity, proximity, configuration, illumination, timing, or any other single, combination, or sequence of sensor, situation, or environmental change.

[0114] Data sources may include records, files, databases, systems, or any other single, combination, or series of internal or external data sources, which may be verified or validated.

[0115] The tissue condition may include one or more of composition, hydration, density, necrosis, discoloration, reflectivity, and temperature. The implant condition may include one or more of composition, degradation, density, and particle dissolution. The tissue and implant morphology may include one or more of shape, flatness, parallelism, roughness, waviness, peak distribution, porosity, and stiffness. Determining the tissue and implant condition and morphology may include at least one operation related to processing the sensed data.

[0116] Processing the sensed data may include cleaning the data, including removing or repairing any noisy, erroneous, or redundant data, and any other single, combination, or sequence of processes adapted to remove unnecessary data and increase the overall usefulness of the remaining data.

[0117] Processing the sensed data may include formatting the data, including rearranging the data into a more appropriate structure or form, flattening the data, or extracting it from its current storage device.

[0118] Processing the sensed data may include sampling the data, which may include selecting or dividing up a portion of the data.

[0119] Processing of the sensed data may include scaling or aligning the data so that their values are within comparable ranges or to achieve some additional level of comparability.

[0120] Processing of sensed data may include decomposition or deconvolution of the data such that representative or particular features or portions of the data may be separated into component elements or components that individually provide more utility.

[0121] Processing of sensed data may include aggregating the data such that individual features, constituent elements, sections, or portions of the data may be combined into a single entity.

[0122] Processing of sensed data may include at least one action related to any other single, combined, or sequence of processes, manipulations, creations, modifications, or any other function that may better prepare the data for use.

[0123] The processing of the sensed data may either not be performed or may be partially performed, where additional entities such as sensor controllers or bridge devices perform this processing individually or independently.

[0124] Determining the condition and morphology of the tissue and implant may include at least one procedure involving the interpretation of the processed data.

[0125] Interpretation of processed data may include at least one operation relating to any general or specific mathematical formula, theory, calculation, concept, or any other single, combination, or sequence of mathematical functions.

[0126] Interpretation of processed data may include at least one act of performing a process or function that calculates custom or standardized geometric, morphological, structural, or any other single, combination, or series of relevant measures.

[0127] Interpretation of the processed data may include at least one act of machine learning, data science, or work related to the execution of a mathematical algorithm or method.

[0128] The interpretation of the processed data may either not be performed or may be partially performed if additional entities such as sensor controllers or bridge devices perform this interpretation individually or independently.

[0129] The interpretation of the processed data may include at least one act of operation related to any observations or implicit conclusions provided by verified personnel. The interpretation may be provided explicitly through medical records or medical history, preoperative procedures, or any other single, combined, or sequential form that may be independent of any generated or processed data. The interpretation of the processed data may include at least one act of operation related to any other single, combined, or sequential process, formula, generation, modification, or any other form of interpretation.

[0130] According to certain aspects and embodiments as disclosed herein, generating the compatibility information may be based on data interpreted from tissue having a receiving surface, an associated implant having an engaging surface, and an interface therebetween, the interface including contact between the receiving surface and the engaging surface, in accordance with any one of claims 1 to 5. Generating the compatibility information may include generating a degree of compatibility of the interface with one or both of the receiving surface and the engaging surface, analyzing the effects of implant insertion or fixation, assessing implant compatibility, and predicting implant life and performance.

[0131] Generating the degree of compatibility may include at least one procedure involving a comparison of the determined tissue condition and morphology with the implant condition and morphology.

[0132] The comparison of the determined tissue and implant condition and morphology may include at least one procedure related to measuring the compatibility of the tissue condition with the implant condition.

[0133] Measuring the compatibility of the tissue condition with the implant condition can include determining whether the implant material is suitable for the tissue.

[0134] The suitability of an implant material may include the possibility of adverse reactions occurring at any time and duration, including during or after surgery.

[0135] The suitability of the implant material may include intended or possible fixation materials, substances, processes, or any other single, combination, or sequence of fixatives or fixation approaches.

[0136] The suitability of an implant material may include the possible stresses, pressures the implant may withstand post-operatively, the intended use scenario, and any other single, combination, or sequence of events or circumstances.

[0137] Measuring the compatibility of tissue condition with implant condition may include checking tissue health and measuring fixation potential and viability.

[0138] The comparison of the determined tissue and implant condition and morphology may include at least one procedure related to measuring the compatibility of the tissue morphology and the implant morphology.

[0139] Measuring the compatibility of tissue morphology with implant morphology can include determining whether the shape and form of the tissue will allow the implant to be inserted and the difficulties associated therewith.

[0140] Measuring the compatibility of tissue morphology with implant morphology can include determining the degree of contact the implant will make with the tissue when inserted and the distribution this will have.

[0141] Measuring the compatibility of tissue morphology with implant morphology can include determining the degree to which the tissue surface occupies the kerf of the implant and the degree to which the tissue distribution pattern within the kerf is similar in comparison.

[0142] Analyzing the effects of implant insertion or fixation can include determining the likely effects that inserting the implant will have on the tissue or implant.

[0143] The effect on tissue by inserting an implant may include surface disruption, density reduction, or any other single, combination, or series of surface or condition modifications.

[0144] Any surface modification may affect the process or results of at least one other single, combination, or sequence of methods or techniques that measure fitness, including but not limited to those expressly stated.

[0145] The effect on tissue by inserting the implant may include directly or indirectly diffusing, dispersing, or otherwise affecting any applied fixative or combinations thereof that may be present.

[0146] Evaluating implant fit can include comparing its current placement to a calculated ideal placement. Placement can be defined by the degree of contact between tissue and implant, the tissue occupancy and pattern within the implant's kerfs, the stress distribution in the implant, and any other single, combination, or series of qualitative or quantitative measures, characteristics, or attributes of surface contact. Ideal placement can be defined by beneficial or advantageous values of the characteristics or attributes used to describe implant placement.

[0147] The quality of implant fit can be affected by the qualitative or quantitative measures, characteristics, or attributes of the condition and morphology of the implant and tissue, the situation and environment, the intended use scenario and stresses the implant will endure, and any other single, combined, or sequence of mechanical or structural forces.

[0148] The results of the assessment may not be explicit and may provide a quantitative or qualitative measure based on all available information adapted to allow for an informed decision.

[0149] Various recommendations, critiques, indicators, prompts, or any other single, combination, or sequence of approaches may be used to inform the entity about necessary changes required to bring the current location closer to the calculated ideal location.

[0150] In the event of a repositioning, translation, rotation, or any other single, combination, or sequence of changes to the current position of the implant that results in a change in the degree of fit, additional analysis is performed.

[0151] Predicting implant life and performance may include at least one procedure involving consideration of generated compatibility information, tissue and implant condition and morphology, fixation approach, previous medical history or records, intended use, implant stress levels, and any other single, combination, or sequence of information adapted to aid or assist in the prediction.

[0152] Implant life and performance may include quantitative and qualitative measures of the time associated with the ease of performing a task, and any other single, combination, or series of measures adapted to provide additional insight.

[0153] The generated implant life and performance information can be used directly or can be interpreted to provide recommendations based on the patient's use or current lifestyle.

[0154] Predicting the useful life and performance of an implant may include at least one act of machine learning, data science or work related to the implementation of a mathematical entity, concept, model, formula, or any other single, combination, or sequence of embodiments.

[0155] At least one simulation or any other computational method or entity may be used to predict, generate, calculate, verify, validate, or any other single, combined, or sequential use adapted to provide information or utility.

[0156] Processing of the compatibility information or data may include at least one act related to converting the compatibility information or data into an evaluable form.

[0157] Transformation of data may involve at least one action involving single, multiple, combined, or a series of pre-processing steps.

[0158] 68. The method of claim 67, further comprising a pre-processing step including cleaning the data including removing or correcting any noisy, erroneous, or redundant data, and any other single, combination, or sequence of processes adapted to increase the usefulness of the remaining data. The pre-processing step may include formatting the data including rearranging the data into a more suitable structure or form, flattening the data, or extracting it from its current storage, and any other single, combination, or sequence of formatting adapted to increase the usability of the data.

[0159] Pre-processing steps may include sampling the data, including selecting or partitioning portions of the data, and any other single, combined, or sequence of processes adapted to yield more representative or advantageous data.

[0160] Transformation of data may include at least one action involving single, multiple, combined, or a series of operations involving manipulation of raw or pre-processed data.

[0161] Manipulation of raw or pre-processed data may include scaling or aligning the data so that their values are either within comparable ranges or to achieve additional levels of intercomparability.

[0162] Manipulation of raw or pre-processed data may include decomposition of the data to separate representative or particular features or portions of the data into component elements or components that provide improved utility over the individual elements.

[0163] Manipulation of raw or pre-processed data may include aggregating the data to combine individual features, constituent elements, segments, or portions of the data into a single entity.

[0164] Transformation of data may include at least one action of any other single, combined, or sequence of processes, manipulations, creations, modifications, or any other functionally related operations adapted to prepare the data for use or evaluation.

[0165] The comparator may contain sets of data in similar or otherwise comparable form belonging to a single, combined, or series of comparison information.

[0166] The post-operative results may be received from the patient after a duration of time has occurred. The received post-operative results may have undergone at least one procedure of the procedure, as described above.

[0167] Generating a means to predict post-operative implant performance may include training machine learning, data science or mathematical entities, concepts, models, formulas, or any other single, combination, or sequence of embodiments configured to provide a performance prediction.

[0168] Any machine learning, data science or mathematical entity, concept, model, formula, or any other single, combined, or sequence of embodiments can be extended by the inclusion of new data.

[0169] Generating correction information to modify tissue morphology and provide generated correction information including a set of treatments can be tailored to improve implant performance and longevity for the surgeon.

[0170] The correction information may include a sample set of different possible treatments for predicted post-operative implant performance.

[0171] The corrective information may include a numerical quantification of implant performance and longevity relative to what is currently existing, and subsequent resulting tissue morphology after the proposed set of procedures is performed.

[0172] The correction information may include pre-configured thresholds above which a correction procedure may be identified as infeasible given the surgical cutting technique being applied and its inherent imprecision.

[0173] 10. The method of any one of claims 1 to 9, wherein the sensed, raw, pre-processed, manipulated, processed, interpreted, usable, evaluable, or any other single, combination, or series of generated, derived, or received data is stored electronically, either offline, online, or through a combination of the two, for later retrieval, processing, or use in any other single, combination, or series form.

[0174] Any one or more of the procedures may be influenced, brought about, adjusted, or directed by patient-specific deformities or problems, including one or more of valgus or varus errors, mechanical alignment errors, or any other errors adapted to cause the patient's physiology to differ from that considered normal or ideal.

[0175] At least one act of the work can occur within an intraoperative environment. At least one act of the work can occur in the same, different, or alternating sequence and can be adapted to produce the same, similar, or different end results. At least one act of the work can occur in real time, near real time, through a delayed processing procedure, or any other single, combined, or sequential processing approach.

[0176] The required data processing or data storage may occur on centralized, distributed, or otherwise online entities, either internally, externally, or with any other single, combined, or sequential computational approach.

[0177] According to a second aspect of the present invention, a system for assisting a surgical biological implantation procedure for integrating a prosthetic device with a patient's tissue is provided. The system may include one or more sensors for sensing characteristics of the patient's tissue morphology, collecting at least condition and morphology data, and generating collected data. The system may further include one or more processors. The one or more processors may be adapted to pre-process and manipulate the collected data and generate processed data, the processed data having a format suitable for interpretation. The one or more processors may be further adapted for interpreting the processed data to extract a data representation of the structure of the patient's tissue and the prosthetic device. The one or more processors may be further adapted for determining compatibility data between the data representation of the patient's tissue and the data representation of the prosthetic device, and determining the compatibility of the condition of the connecting surface of the implant and the receiving surface of the patient's tissue. The one or more processors may be further adapted for predicting the service life and performance of the prosthetic device using the compatibility data. The one or more processors may be further adapted for generating correction data for modification of the receiving surface of the patient's tissue for improved prediction of the service life and performance of the prosthetic device.

[0178] According to a particular arrangement of the second aspect, there is provided a system for assisting a surgical biological implantation procedure for integration of a prosthetic device with a patient's tissue, the system comprising one or more processors adapted for: pre-processing and manipulating collected data to generate processed data, the processed data having a format suitable for interpretation; interpreting the processed data to extract a data representation of the patient's tissue and the structure of the prosthetic device; determining compatibility data between the data representation of the patient's tissue and the data representation of the prosthetic device to determine the compatibility of the condition of the implant's connecting surface and the receiving surface of the patient's tissue; predicting the service life and performance of the prosthetic device using the compatibility data; and generating correction data for modification of the receiving surface of the patient's tissue for improved prediction of the service life and performance of the prosthetic device; and one or more sensors for sensing characteristics of the patient's tissue morphology, collecting at least condition and morphology data, and generating the collected data.

[0179] The one or more sensors may be selected from the group comprising Raman spectroscopy, spectral imaging, hyperspectral imaging, optical imaging, thermal imaging, fluorescence spectroscopy, microscopy, acoustics, 3D metrology, optical coherence tomography, position, movement, or balance sensors.

[0180] The one or more sensors may be adapted to sense attributes of the condition and / or morphology of the patient's tissue and / or prosthetic implant.

[0181] The sensed condition attributes of the patient's tissue and / or prosthetic implant may be selected from one or more of the following group: composition, hydration, density, necrosis, discoloration, reflectivity, thermal consistency, degradation, particle dissolution, and any other single, combination, or sequence of condition descriptors.

[0182] The sensed morphological attributes of the patient's tissue and / or prosthetic implant may be selected from one or more of the group of shape, flatness, parallelism, roughness, waviness, peak distribution, porosity, stiffness, and any other single, combination, or sequence of morphological descriptors.

[0183] The system may further include means for outputting a prediction of the life and performance of the prosthetic device.

[0184] The system may further include means for outputting the generated correction data for modification of the receiving surface of the patient's tissue for improved prediction of the useful life and performance of the prosthetic device.

[0185] The collected data may further include historical data, including historical surgical procedure record data and / or historical patient data.

[0186] Pre-processing and manipulation of the collected data may include one or more of the following: removing noise, erroneous, or redundant data; formatting the data into an appropriate data format; sampling the collected data into one or more representative segments; scaling or aligning the data; decomposing the data into component elements; aggregating the data and creating statistically significant data structures.

[0187] According to a third aspect of the present invention, there is provided a system for intraoperative implant fit analysis and life expectancy prediction for a prosthetic implant to be integrated with a patient's physiological tissue, comprising: one or more processors; a memory coupled to one or more processors and configured to store instructions that, when executed by the one or more processors, cause the processors to: collecting data via a plurality of sensors and a plurality of data sources mounted in proximity to the tissue and the implant; determining the condition and morphology of the tissue and implant based on the collected data; generating compatibility information between the tissue and the implant based on the determined tissue and implant conditions and morphologies; processing the compatibility information into a format adapted for evaluation against a predetermined comparator; Utilizing comparative information and historical data sets of postoperative outcomes to generate a means of predicting postoperative implant performance and lifespan; Generating and providing correction information for altering tissue condition and morphology for improved post-operative implant performance and lifespan; a memory for causing the memory to perform operations including A system is provided comprising:

[0188] According to a fourth aspect of the present invention, there is provided a non-transitory computer readable storage device having instructions stored thereon that, when executed by a processor, cause the processor to perform operations for intraoperative implant fit analysis and life expectancy prediction for a prosthetic implant to be integrated with a patient's physiological tissue, the operations comprising: collecting data via a plurality of sensors and a plurality of data sources mounted in proximity to the tissue and the implant; determining the condition and morphology of the tissue and implant based on the collected data; generating compatibility information between the tissue and the implant based on the determined tissue and implant conditions and morphologies; processing the compatibility information into a format adapted for evaluation against a predetermined comparator; Utilizing comparative information and historical data sets of postoperative outcomes to generate a means of predicting postoperative implant performance and lifespan; Generating and providing correction information for altering tissue condition and morphology for improved post-operative implant performance and lifespan; A non-transitory computer readable storage device is provided, comprising:

[0189] According to a fifth aspect of the present invention there is provided a computer program element comprising computer program code means for causing a computer to perform a procedure, the computer program code means comprising: collecting data via a plurality of sensors and a plurality of data sources mounted in proximity to the tissue and the implant; determining the condition and morphology of the tissue and implant based on the collected data; generating compatibility information between the tissue and the implant based on the determined tissue and implant conditions and morphologies; processing the compatibility information into a format adapted for evaluation against a predetermined comparator; Utilizing comparative information and historical data sets of postoperative outcomes to generate a means of predicting postoperative implant performance and lifespan; Generating and providing correction information for altering tissue condition and morphology for improved post-operative implant performance and lifespan; A computer program element is provided, comprising:

[0190] According to a sixth aspect of the present invention, there is provided a computer-readable medium having a program recorded thereon, the program comprising: collecting data via a plurality of sensors and a plurality of data sources mounted in proximity to the tissue and the implant; determining the condition and morphology of the tissue and implant based on the collected data; generating compatibility information between the tissue and the implant based on the determined tissue and implant conditions and morphologies; processing the compatibility information into a format adapted for evaluation against a predetermined comparator; Utilizing comparative information and historical data sets of postoperative outcomes to generate a means of predicting postoperative implant performance and lifespan; Generating and providing correction information for altering tissue condition and morphology for improved post-operative implant performance and lifespan; A computer-readable medium configured to cause a computer to perform a procedure including: The present invention provides, for example, the following. (Item 1) 1. A method for intraoperative implant fit analysis and life expectancy prediction for a prosthetic implant to be integrated with a patient's physiological tissue, said method comprising: collecting data via a plurality of sensors and a plurality of data sources mounted in proximity to the tissue and the implant; determining the condition and morphology of the tissue and implant based on the collected data; generating compatibility information between the tissue and the implant based on the determined tissue and implant conditions and morphologies; processing the compatibility information into a format adapted for evaluation against a predetermined comparator; Utilizing the comparative information and historical data sets of post-operative outcomes to generate a means of predicting post-operative implant performance and lifespan; generating and providing correction information for modifying the tissue condition and morphology for improved post-operative implant performance and lifespan; A method comprising: (Item 2) Item 10. The method of item 1, wherein the tissue comprises a biological tissue comprising bone. (Item 3) 3. The method of either item 1 or item 2, wherein the prosthetic implant comprises a knee prosthesis or a hip prosthesis. (Item 4) 4. The method of any one of items 1-3, wherein the prosthetic implant comprises one or more features, the one or more features comprising kerfs or patterns on one or more surfaces to promote osseointegration and / or increase the rigidity of fixation to the tissue. (Item 5) 5. The method of any one of items 1-4, wherein the sensor comprises at least one sensor, the at least one sensor existing independently or as part of a sensor system or set of sensors. (Item 6) 6. The method of any one of items 1-5, wherein the sensor comprises at least one sensor that is completely self-contained. (Item 7) 7. The method of any one of items 1-6, wherein the sensor comprises at least one sensor, and the at least one sensor requires additional devices, services, conditions, platforms, or any other single, combination, or set of requirements in order to be properly interfaced, configured, or operated. (Item 8) 8. The method of any one of items 1-7, wherein the sensor comprises at least one sensor, each configured to monitor, sense, collect, and provide data based on various characteristics, attributes, events, or measures from different angles, positions, proximity, nearness, movement, speed, placement, or orientation present with, in, or oriented by the object. (Item 9) 9. The method of claim 8, wherein the subject includes one or more of the tissue, the implant, a connection interface, the surrounding environment, the result of a procedure or interaction, an individual or collective system or device, and any other source or set of sources involving them. (Item 10) 10. The method of claim 9, wherein the object is treated, altered, or prepared to affect its original, initial, or current state for purposes of preservation, identification, uniformity, fixation, or any other single, combined, or series of purposes. (Item 11) 10. The method of item 9, wherein the object is modified structurally, chemically, or through any intraoperative procedure, surgery, or any other single, combined, or sequence of approaches that can change its shape as part of, or independently of, any other single, combined, or sequence of medical operations. (Item 12) 12. The method of any one of items 1-11, wherein the sensor is configured to operate in an automated manner through a manual trigger or through any combination or sequence of manual and automatic triggers. (Item 13) Item 13. The method of item 12, wherein the manual trigger comprises a manual trigger including a button, a voice command, a gesture control, or an alternative physical actuation. (Item 14) The sensor may be affected by conditions, environment, user control, sensor configuration, and any other single, combination, or series of variables that may have a direct or indirect influence on the sensor, such as indefinitely, periodically, once, or in any other single, combination, or series of variables. 14. The method of any one of items 1-13, configured to engage in sensing in a sensing approach. (Item 15) Item 15. The method of item 14, wherein the sensing is configured to operate in real time, near real time, through some form of delayed processing, or in any other single, combined, or sequential processing approach that can be influenced by conditions, environment, user control, sensor configuration, and any other single, combined, or sequential variables that can have a direct or indirect influence. (Item 16) Item 17. The method of item 14, wherein the sensor requires external intervention to operate properly, the external intervention including changes in its position, angle, proximity, proximity, configuration, illumination, timing, or any other single, combination, or sequence of sensor, situation, or environment changes. 17. The method of any one of items 1-16, wherein the data source comprises a record, a file, a database, a system, or any other single, combination, or series of internal or external data sources, which may have been verified or validated. (Item 18) 18. The method of any one of items 1-17, wherein the tissue state comprises one or more of composition, hydration, density, necrosis, discoloration, reflectance, and temperature. (Item 19) 19. The method of any one of items 1-18, wherein the implant condition comprises one or more of composition, degradation, density, and particle dissolution. (Item 20) 20. The method of any one of items 1-19, wherein the morphology of the tissue and implant comprises one or more of the following: shape, flatness, parallelism, roughness, waviness, peak distribution, porosity, stiffness. (Item 21) 22. The method according to claim 1, wherein determining the state and morphology of the tissue and implant includes performing at least one operation related to processing the sensed data. 21. The method of claim 20, wherein processing the sensed data includes cleaning the data, including removing or repairing any noisy, erroneous, or redundant data, and any other single, combined, or sequence of processes adapted to remove unnecessary data or increase the overall usefulness of the remaining data. (Item 23) 21. The method of claim 20, wherein processing the sensed data includes formatting the data, including rearranging the data into a more appropriate structure or format, flattening the data, or extracting it from its current storage device. (Item 24) 21. The method of claim 20, wherein processing the sensed data includes sampling the data, which includes selecting or dividing a portion of the data. (Item 25) 21. The method of claim 20, wherein processing the sensed data includes scaling or aligning the data so that its values are within a comparable range or to achieve some additional level of intercomparability. (Item 26) 21. The method of claim 20, wherein processing the sensed data includes decomposing or deconvolving the data, whereby representative or particular features or portions of the data can be separated into component elements or elements that individually provide more utility. (Item 27) 21. The method of claim 20, wherein processing the sensed data includes aggregating the data, whereby individual features, components, segments, or portions of data may be combined into a single entity. (Item 28) 21. The method of claim 20, wherein processing the sensed data includes at least one action related to any other single, combined, or sequence of processes, manipulations, creations, modifications, or any other function that may better prepare the data for use. (Item 29) 21. The method of claim 20, wherein the processing of the sensed data is not performed or is partially performed if an additional entity, such as a sensor controller or a bridge device, performs this processing individually or independently. (Item 30) 30. The method according to any one of items 1-29, wherein the determination of the state and morphology of the tissue and implant comprises performing at least one of the tasks related to the interpretation of the processed data. (Item 31) Item 31. The method according to item 30, wherein the interpretation of the processed data comprises at least one operation relating to any general or specific mathematical formula, theory, calculation, concept, or any other single, combination, or sequence of mathematical functions. (Item 32) 31. The method of claim 30, wherein the interpretation of the processed data comprises at least one operation related to the execution of a process or function that calculates a custom or standardized geometric, morphological, structural, or any other single, combination, or series of relevant measures. (Item 33) Item 34. The method of item 30, wherein interpreting the processed data includes at least one operation related to machine learning, data science, or the implementation of a mathematical algorithm or method. Item 31. The method according to item 30, wherein the interpretation of the processed data is not performed or is partially performed if an additional entity, such as a sensor controller or a bridge device, individually or independently performs this interpretation. (Item 35) 31. The method according to claim 30, wherein the interpretation of the processed data includes at least one disposition of work related to any observations or implicit conclusions provided by the verified personnel. (Item 36) 31. The method of claim 30, wherein the interpretation is provided explicitly through medical records or medical history, preoperative procedures, or any other single, combined, or sequential form that may be independent of any generated or processed data. (Item 37) 31. The method of claim 30, wherein the interpretation of the processed data comprises at least one action of any other single, combination, or sequence of processes, formulas, generation, modification, or any other form of interpretation-related operation. (Item 38)

[0023] wherein generating the compatibility information is based on data interpreted from tissue comprising a receiving surface, an associated implant comprising an engaging surface, and an interface therebetween, said interface comprising contact points between said receiving surface and said engaging surface; and according to any one of items 1-37, the method further comprises: generating a degree of conformance of the interface with one or both of the receiving surface and the engaging surface; Analyzing the impact of implant insertion or fixation; assessing the implant fit; predicting the life and performance of said implant; 38. The method according to any one of items 1-37, comprising: (Item 39) 39. The method of claim 38, wherein generating the degree of compatibility comprises at least one step of performing an operation related to comparing the determined tissue condition and morphology with the condition and morphology of an implant. (Item 40) 40. The method according to any one of items 1-39, wherein the comparison of the determined tissue condition and morphology with the implant condition and morphology comprises at least one step of a task related to measuring the compatibility of the tissue condition with the implant condition. (Item 41) 41. The method of claim 40, wherein measuring the compatibility of the tissue condition with the implant condition includes determining whether the implant material is suitable for the tissue. (Item 42) 41. The method according to item 40, wherein the suitability of the implant material includes the possibility of adverse reactions occurring at any time and duration, including during or after surgery. (Item 43) The method of item 40, wherein the suitability of the implant material includes intended or possible fixation materials, substances, processes, or any other single, combination, or sequence of fixation agents or fixation approaches. (Item 44) 41. The method according to item 40, wherein the suitability of an implant material includes the possible stresses, pressures that the implant can withstand post-operatively, the intended use scenario, and any other single, combination, or sequence of events or circumstances. (Item 45) 40. The method according to claim 39, wherein measuring the compatibility of the tissue condition with the implant condition includes checking the health of the tissue and measuring fixation potential and viability. (Item 46) 46. The method according to any one of items 1-45, wherein the comparison of the determined tissue condition and morphology with the implant condition and morphology comprises at least one step of a task related to measuring the compatibility of the tissue morphology with the implant morphology. (Item 47) Item 46. The method of item 45, wherein measuring the compatibility of the tissue morphology with the implant morphology includes determining whether the shape and form of the tissue will allow the implant to be inserted and any difficulties associated therewith. (Item 48) 46. The method of claim 45, wherein measuring the compatibility of the tissue morphology with the implant morphology comprises determining the degree of contact the implant will make with the tissue when inserted and the distribution this will have. (Item 49) 46. The method of claim 45, wherein measuring the compatibility of the tissue morphology with the implant morphology comprises determining the degree to which the tissue surface occupies the kerf of the implant and the degree of similarity in comparing the distribution patterns of the tissue within the kerf. (Item 50) 38. The method of claim 37, wherein analyzing the effects of implant insertion or fixation comprises determining the likely effects that inserting the implant will have on the tissue or implant. (Item 51) 50. The method of claim 49, wherein the effect of inserting the implant into the tissue comprises surface disruption, density reduction, or any other single, combination, or series of surface or condition modifications. (Item 52) Item 51. The method according to item 50, wherein any surface modification can affect the process or results of at least one other single, combination, or series of methods or techniques for measuring the degree of compatibility, including but not limited to those expressly stated. (Item 53) 50. The method of claim 49, wherein the effect of inserting the implant into the tissue comprises directly or indirectly diffusing, dispersing, or affecting any applied fixative or combination thereof that may be present. (Item 54) 38. The method of claim 37, wherein assessing the implant fit comprises comparing its current placement with a calculated ideal placement. (Item 55) 54. The method of claim 53, wherein placement is defined by the degree of contact between the tissue and implant, the occupancy and pattern of tissue within the implant's kerfs, the stress distribution in the implant, and any other single, combination, or sequence of qualitative or quantitative measures, characteristics, or attributes of surface contact. (Item 56) 56. The method according to any one of items 1-55, wherein the ideal placement is defined by beneficial or advantageous values of characteristics or attributes used to describe the implant placement. (Item 57) 55. The method of any of items 53 and 54, wherein the quality of the implant fit is influenced by qualitative or quantitative measures, characteristics, or attributes of the condition and morphology of the implant and tissue, the situation and environment, the intended use scenario and stresses the implant will withstand, and any other single, combination, or sequence of mechanical or structural forces. (Item 58) 54. The method according to item 53, wherein the results of the assessment are not explicit but provide a quantitative or qualitative measure based on all available information adapted to allow an informed decision. (Item 59) Item 58. The method of item 57, wherein various recommendations, critiques, indicators, prompts, or any other single, combination, or sequence of approaches are used to inform the entity about necessary changes required to bring the current location closer to the calculated ideal location. (Item 60) 60. The method of any one of items 1-59, comprising performing additional analysis in the event of a repositioning, movement, rotation, or any other single, combination, or sequence of changes to the current position of the implant that results in a change in the degree of compatibility. (Item 61) 38. The method of claim 37, wherein predicting the implant's useful life and performance comprises at least one of the following actions related to consideration of generated compatibility information, tissue and implant condition and morphology, fixation approach, previous medical history or records, intended use, implant stress levels, and any other single, combination, or sequence of information adapted to aid or assist in the prediction. (Item 62) Implant life and performance is a defined period of time related to the ease with which a task can be performed. 61. The method of item 60, including quantitative and qualitative measures, and any other single, combination, or series of measures adapted to provide additional insight. (Item 63) 61. The method of item 60, wherein the generated implant life and performance information is used directly or interpreted to provide recommendations based on the patient's use or current lifestyle. (Item 64) Item 61. The method of item 60, wherein predicting the implant's useful life and performance comprises at least one act of machine learning, data science, or mathematical entity, concept, model, formula, or any other single, combination, or sequence of embodiments. (Item 65) Item 61. The method of item 60, wherein at least one simulation or any other computational method or entity is used for prediction, generation, calculation, verification, validation, or any other single, combination, or sequence of uses adapted to provide information or utility. (Item 66) 61. The method of any of items 1 and 60, wherein processing the compatibility information or data includes at least one action related to converting the compatibility information or data into an evaluable format. (Item 67) Item 66. The method according to item 65, wherein the transformation of the data comprises at least one action involving a single, multiple, combined, or series of pre-processing steps. (Item 68) Item 67. The method of item 66, further comprising a pre-processing step including cleaning the data, including removing or correcting any noisy, erroneous, or redundant data, and any other single, combined, or sequential process adapted to increase the usefulness of the remaining data. (Item 69) Item 68. The method of item 67, wherein the pre-processing step includes formatting the data, including rearranging the data into a more appropriate structure or form, flattening the data, or extracting it from its current storage device, and any other single, combination, or sequence of formattings adapted to increase the usability of the data. (Item 70) Item 67. The method of item 66, wherein the pre-processing step includes sampling the data, including selecting or partitioning a portion of the data, and any other single, combined, or sequential process adapted to yield more representative or advantageous data. (Item 71) Item 67. The method of item 66, wherein the transformation of the data comprises at least one action involving single, multiple, combined, or a series of operations involving manipulation of raw or pre-processed data. (Item 72) Item 72. The method of item 71, wherein the manipulation of the raw or pre-processed data includes scaling or aligning the data so that their values are either within comparable ranges or to achieve an additional level of inter-comparability. (Item 73) 71. The method of claim 70, wherein the manipulation of the raw or pre-processed data includes decomposition of the data to separate representative or particular features or portions of the data into component elements or components that provide improved utility over the individual elements. (Item 74) The manipulation of said raw or pre-processed data involves the extraction of individual features, components, or segments of the data. 71. The method of claim 70, including aggregating the data to combine minutes or portions into a single entity. (Item 75) Item 66. The method of item 65, wherein the transformation of the data includes at least one action of any other single, combined, or sequence of processes, manipulations, generation, modification, or any other functionally related operations adapted to prepare the data for use or evaluation. (Item 76) Item 76. The method of any one of items 1-75, wherein the comparator comprises sets of data in similar or comparable form belonging to a single, combined or series of comparison information. (Item 77) 77. The method of any one of items 1-76, wherein the post-operative results are received from the patient after a duration of time has occurred. (Item 78) Item 77. The method according to item 76, wherein the received postoperative results are treated with at least one of the procedures described in items 18-29. (Item 79) 61. The method of any of items 1 and 60, wherein generating a means for predicting post-operative implant performance comprises training a machine learning, data science or mathematical entity, concept, model, formula, or any other single, combination, or sequence of embodiments configured to provide a performance prediction. (Item 80) Item 79. The method of item 78, wherein any machine learning, data science or mathematical entity, concept, model, formula, or any other single, combination, or sequence of embodiments is extended by the inclusion of new data. (Item 81) 81. The method of any one of items 1-80, further comprising generating correction information for modifying the tissue morphology and providing the generated correction information to the surgeon, the correction information including a set of procedures adapted to improve the implant performance and service life. (Item 82) 82. The method of claim 81, wherein the correction information comprises a sample set of different possible treatments for predicted post-operative implant performance. (Item 83) Item 81. The method of item 80, wherein the correction information includes a numerical quantification of implant performance and lifespan relative to currently existing and subsequent resulting tissue morphology after the proposed set of procedures is performed. (Item 84) Item 83. The method of item 82, wherein the correction information includes a preconfigured threshold given the surgical cutting technique being applied and its inherent imprecision, above which correction action is identified as infeasible. (Item 85) 85. The method of any one of items 1-84, wherein the sensed, raw, pre-processed, manipulated, processed, interpreted, usable, evaluable, or any other single, combination, or series of generated, derived, or received data is stored electronically, offline, online, or through a combination of the two, for later retrieval, processing, or use in any other single, combination, or series form. (Item 86) Any one or more of the procedures may be influenced, brought about, controlled, or directed by a patient-specific deformity or problem, such as a valgus or varus error, a mechanical alignment error, or a condition that is normal or unreasonable to the patient's physiology. 86. The method of any one of items 1-85, including one or more of any other errors adapted to make it different from what is considered reasonable. (Item 87) 87. The method of any one of items 1-86, wherein at least one procedure of the task occurs within an intraoperative environment. (Item 88) 88. The method of any one of items 1-87, wherein at least one action of the operations occurs in the same, different, or alternating order and is adapted to produce the same, similar, or different end result. (Item 89) Item 84. The method of item 83, wherein at least one disposition of the operation occurs in real time, near real time, through a delayed processing procedure, or any other single, combined, or sequential processing approach. (Item 90) Item 84. The method of item 83, wherein the required data processing or data storage occurs internally or externally, on a centralized, distributed, or online entity, or in any other single, combined, or sequential computational approach. (Item 91) 1. A system for assisting a surgical biological implantation procedure for integration of a prosthetic device with a patient's tissue, the system comprising: one or more sensors for sensing characteristics of the patient's tissue morphology, collecting at least status and morphology data, and generating collected data, the one or more sensors comprising one or more processors; The one or more processors: pre-processing and manipulating the collected data to generate processed data, the processed data having a form suitable for interpretation; interpreting the processed data to extract a data representation of the patient tissue and the structure of the prosthetic device; determining compatibility data between the data representation of the patient tissue and the data representation of the prosthetic device to determine the compatibility of a connecting surface of the implant with a condition of a receiving surface of the patient tissue; using the fitness data to predict the useful life and performance of the prosthetic device; generating correction data for modification of the receiving surface of the patient's tissue for improved prediction of the service life and performance of the prosthetic device; A system adapted to perform (Item 92) Item 92. The system of item 91, wherein the one or more sensors are selected from the group comprising Raman spectroscopy, spectral imaging, hyperspectral imaging, optical imaging, thermal imaging, fluorescence spectroscopy, microscopy, acoustics, 3D metrology, optical coherence tomography, position, movement, or balance sensors. (Item 93) Item 93. The system of item 92, wherein the one or more sensors are adapted to sense attributes of the condition and / or morphology of the patient's tissue and / or the prosthetic implant. (Item 94) Item 93. The system of item 92, wherein the sensed condition attribute of the patient's tissue and / or the prosthetic implant is selected from one or more of the following group: composition, hydration, density, necrosis, discoloration, reflectivity, thermal consistency, degradation, particle dissolution, and any other single, combination, or sequence of condition descriptors. (Item 95) Item 93. The system of item 92, wherein the sensed morphological attributes of the patient's tissue and / or the prosthetic implant are selected from one or more of the group of shape, flatness, parallelism, roughness, waviness, peak distribution, porosity, stiffness, and any other single, combination, or sequence of morphological descriptors. (Item 96) 96. The system of any one of items 91-95, further comprising outputting the prediction of the service life and performance of the prosthetic device. (Item 97) 97. The system of any one of items 91-96, further comprising outputting the generated correction data for modification of the receiving surface of the patient's tissue for improved prediction of the service life and performance of the prosthetic device. (Item 98) 98. The system of any one of items 91-97, wherein the collected data further includes historical data including historical surgical procedure record data and / or historical patient data. (Item 99) 99. The system of any one of items 91-98, wherein pre-processing and manipulating the collected data includes one or more of removing noisy, erroneous, or redundant data, formatting the data into an appropriate data format, sampling the collected data into one or more representative segments, scaling or aligning the data, decomposing the data into component elements, aggregating the data, and creating statistically significant data structures. (Item 100) 1. A system for intraoperative implant fit analysis and life expectancy prediction for a prosthetic implant to be integrated with a patient's physiological tissue, said system comprising: one or more processors; a memory coupled to the one or more processors; Equipped with The memory is configured to store instructions that, when executed by the one or more processors, cause the processors to perform operations, such as: collecting data via a plurality of sensors and a plurality of data sources mounted in proximity to the tissue and the implant; determining the condition and morphology of the tissue and implant based on the collected data; generating compatibility information between the tissue and the implant based on the determined tissue and implant conditions and morphologies; processing the compatibility information into a format adapted for evaluation against a predetermined comparator; Utilizing comparative information and historical data sets of postoperative outcomes to generate a means of predicting postoperative implant performance and lifespan; generating and providing correction information for modifying the condition and morphology of said tissue for improved post-operative implant performance and lifespan; Including, the system. (Item 101) 1. A non-transitory computer-readable storage device storing instructions that, when executed by a processor, cause the processor to perform operations for intraoperative implant fit analysis and life expectancy prediction for a prosthetic implant to be integrated with a patient's physiological tissue, the operations comprising: collecting data via a plurality of sensors and a plurality of data sources mounted in proximity to the tissue and implant; Based on the collected data, the condition and morphology of the tissue and implant are determined. And, generating compatibility information between the tissue and the implant based on the determined tissue and implant conditions and morphologies; processing the compatibility information into a format adapted for evaluation against a predetermined comparator; Utilizing comparative information and historical data sets of postoperative outcomes to generate a means of predicting postoperative implant performance and lifespan; generating and providing correction information for modifying the condition and morphology of said tissue for improved post-operative implant performance and lifespan; a non-transitory computer readable storage device, (Item 102) A computer program element comprising computer program code means for causing a computer to perform a procedure, said procedure comprising: collecting data via a plurality of sensors and a plurality of data sources mounted in proximity to the tissue and the implant; determining the condition and morphology of the tissue and implant based on the collected data; generating compatibility information between the tissue and the implant based on the determined tissue and implant conditions and morphologies; processing the compatibility information into a format adapted for evaluation against a predetermined comparator; Utilizing comparative information and historical data sets of postoperative outcomes to generate a means of predicting postoperative implant performance and lifespan; generating and providing correction information for modifying the condition and morphology of said tissue for improved post-operative implant performance and lifespan; A computer program element, including: (Item 103) A computer-readable medium having a program recorded thereon, the program being configured to cause a computer to execute a procedure, the procedure comprising: collecting data via a plurality of sensors and a plurality of data sources mounted in proximity to the tissue and the implant; determining the condition and morphology of the tissue and implant based on the collected data; generating compatibility information between the tissue and the implant based on the determined tissue and implant conditions and morphologies; processing the compatibility information into a format adapted for evaluation against a predetermined comparator; Utilizing comparative information and historical data sets of postoperative outcomes to generate a means of predicting postoperative implant performance and lifespan; generating and providing correction information for modifying the condition and morphology of said tissue for improved post-operative implant performance and lifespan; 1. A computer-readable medium comprising: [Brief explanation of the drawings]

[0191] Notwithstanding any other forms which may fall within the scope of the present invention, a preferred embodiment / preferred embodiments of the present invention will now be described, by way of example only, with reference to the accompanying drawings, in which: [Figure 1] FIG. 1 is a schematic flow diagram depicting the implant fit analysis process including the steps required for the complete implementation of the preferred embodiment. [Figure 2]FIG. 2 is a detailed schematic flow diagram depicting the data sources and associated procedures involved in the collection and accessibility of data as introduced in the exemplary data collection steps in FIG. [Figure 3] FIG. 3 is a detailed schematic flow diagram depicting the processes and operations involved in preparing data as introduced in the exemplary data processing steps in FIG. [Figure 4] FIG. 4 is a detailed schematic flow diagram depicting the algorithms, methods, and calculations involved in analyzing the processed data as introduced in the exemplary data interpretation step in FIG. [Figure 5] FIG. 5 illustrates possible properties that generally describe conditions that may exist with respect to implants and hard tissue. [Figure 6] FIG. 6 generally illustrates possible properties that describe the morphology that may exist for both the implant and the hard tissue. [Figure 7] FIG. 7 generally illustrates possible properties describing the quality of a potential connection interface that can be derived from condition and morphological information associated with a specific implant and hard tissue. [Figure 8] FIG. 8 generally illustrates the effect of implant insertion on specific hard tissue and any pre-existing anchorage. [Figure 9] FIG. 9 generally illustrates an internal visualization of the calculated perfect connection interface and properties used to derive the association quality indicators based on existing implant and hard tissue pairs. [Figure 10] FIG. 10 generally illustrates the types of recommended modifications to an existing physical connection interface that can be derived from a comparison between itself and a virtual version of equal or superior quality. [Figure 11] FIG. 11 is a detailed schematic flow diagram depicting the pre-processing and manipulations required to transform the data into a more assessable form for further use within predictive algorithms and methods. [Figure 12]FIG. 12 is a detailed schematic flow diagram depicting the types of predictive algorithms and methods that can yield information and properties related to the service life and performance of a specific connection interface based on existing processed data. [Figure 13] FIG. 13 is a detailed schematic flow diagram depicting the process by which a set of corrective actions to modify tissue morphology is determined. [Figure 14] FIG. 14 illustrates a computing device in which various embodiments described herein may be implemented, according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0192] (definition) The following definitions are provided as general definitions and should not in any way limit the scope of the present invention to only those terms, but are set forth for a better understanding of the following description.

[0193] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this invention belongs. It should be further understood that terms used herein should be interpreted as having a meaning consistent with their meaning in the context of this specification and related art, and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein. For purposes of the present invention, additional terms are defined below. Furthermore, all definitions as defined and used herein should be understood to supersede dictionary definitions, definitions in documents incorporated by reference, and / or ordinary meanings of the defined terms, unless there is a doubt as to the meaning of a particular term (in which case the general dictionary definition and / or common usage of the term will prevail).

[0194] For purposes of the present invention, the following terms are defined below.

[0195] The articles "a" and "an" are used herein to refer to one or to more than one (i.e., to at least one) of the grammatical object of the article. By way of example, "an element" refers to one or more elements.

[0196] The term "about" or "approximately" is used herein to refer to a quantity that varies by about 30%, preferably about 20%, and more preferably about 10% of the reference quantity in the positive and negative direction of its subject, unless otherwise stated or specified. The use of the term "about" or "approximately" to modify a number is simply an explicit indication that the number should not be interpreted as an exact value.

[0197] Throughout this specification, unless the context requires otherwise, the words "comprise", "comprises", and "comprising" will be understood to imply the inclusion of a stated step or element or group of steps or elements but not to exclude any other step or element or group of steps or elements.

[0198] Any one of the terms "including" or "which includes" or "that includes" as used herein is an open-ended term and means including at least the elements / features that follow the term, but not excluding others. Thus, "including" is synonymous with and means "comprising."

[0199] In the claims and in the summary above and description below, all transitional phrases such as "comprising," "including," "carrying," "having," "containing," "involving," "holding," "composed of," etc., are to be understood to be open-ended, i.e., meaning "including but not limited to." Only the transitional phrases "consisting of" and "consisting essentially of" shall be closed or semi-closed transitional phrases, respectively.

[0200] The term "real-time," e.g., "displaying data in real time," refers to the display of data without any intentional delay, subject to the processing limitations of the system and the time required to accurately measure the data.

[0201] The term "near real-time," e.g., "capturing real-time or near-real-time data," refers to capturing data either without intentional delay ("real-time") or as close to real-time as practical (i.e., with slight but minimal delay, whether intentional or not, within the constraints and processing limits of the system for capturing and recording or transmitting the data).

[0202] Although any methods and materials similar or comparable to those described herein can be used in the practice or testing of the present invention, the preferred methods and materials are described. It should be understood that the methods, devices, and systems described herein can be implemented in a variety of ways and for a variety of purposes. The description herein is by way of example only.

[0203] As used herein, the term "exemplary" is used in the sense of providing an example as opposed to indicating a quality. That is, an "exemplary embodiment" is an embodiment provided as an example, as opposed to necessarily being an embodiment of exemplary quality that serves as, for example, a desirable model or represents the best of its kind.

[0204] The various methods or processes outlined herein may be coded as software executable on one or more processors employing any one of a variety of operating systems or platforms. In addition, such software may be written using any of a number of suitable programming languages and / or programming or scripting tools, and may be compiled as executable machine language code or intermediate code that runs on a framework or virtual machine.

[0205] In this regard, various concepts of the present invention may be embodied as a computer-readable storage medium (or multiple computer-readable storage media) (e.g., computer memory, one or more floppy disks, compact disks, optical disks, magnetic tapes, flash memories, circuitry within field programmable gate arrays or other semiconductor devices, or other non-transitory or tangible computer storage media) encoded with one or more programs that, when executed on one or more computers or other processors, perform methods that implement the various detailed embodiments discussed above. The computer-readable medium or media may be transportable, such that the program or programs stored thereon may be loaded onto one or more different computers or other processors to implement various aspects of the present invention as discussed above.

[0206] The terms "program" or "software" are used herein in a generic sense to refer to any type of computer code or set of computer-executable instructions that can be employed to program a computer or other processor to implement various aspects of the embodiments as discussed above. Additionally, it should be understood that, according to one aspect, one or more computer programs that, when executed, perform the methods of the present invention need not reside on a single computer or processor, but may be distributed in a modular manner among several different computers or processors to implement various aspects of the present invention.

[0207] Computer-executable instructions may be in many forms, such as program modules, executed by one or more computers or other devices. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. Typically, the functionality of the program modules may be combined or distributed as desired in various embodiments.

[0208] Data structures may also be stored in computer-readable media in any suitable format. For convenience of illustration, data structures may be shown as having fields that are related through locations within the data structure. Such relationships may similarly be achieved by assigning storage for the fields to locations within the computer-readable media that convey the relationships between the fields. However, any suitable mechanism (such as through the use of pointers, tags, or other mechanisms that establish relationships between data elements) may be used to establish relationships between information within fields of a data structure.

[0209] Various concepts of the present invention may also be embodied as one or more methods, examples of which are provided. The acts performed as part of a method may be ordered in any suitable manner. Thus, embodiments may be constructed such that the acts are performed in an order different from that shown, which may include performing some acts simultaneously, even if shown as a sequence of acts in the illustrative embodiments.

[0210] The term "and / or," as used herein in the specification and claims, should be understood to mean "one or both" of the elements so coordinating, i.e., elements that are conjunctively present in some cases and disjunctively present in other cases. Multiple elements listed with "and / or" should be construed in the same manner, i.e., "one or more" of the elements so coordinating. Other elements other than the elements specifically identified by the "and / or" clause may optionally be present, whether or not related to those elements specifically identified. Thus, as a non-limiting example, a reference to "A and / or B," when used in conjunction with open-ended language such as "comprising," may refer in one embodiment to A only (optionally including elements other than B); in another embodiment, to B only (optionally including elements other than A); in yet another embodiment, to both A and B (optionally including other elements); etc.

[0211] As used herein in the specification and claims, "or" should be understood to have the same meaning as "and / or," as defined above. For example, when separating items in a list, "or" or "and / or" shall be interpreted as inclusive, i.e., including not only the inclusion of at least one, but also two or more of several elements or a list of elements, and, optionally, additional unlisted items. Only terms clearly indicated to the contrary, such as "only one of" or "exactly one of," or, when used in the claims, "consisting of," will refer to the inclusion of exactly one element of several elements or a list of elements. In general, the term "or," as used herein, shall be interpreted to indicate exclusive alternatives (i.e., "one or the other, but not both") only when preceded by exclusive terms, such as "either," "one of," "only one of," or "exactly one of." "Consisting essentially of," when used in the claims, shall have its ordinary meaning as used in the field of patent law.

[0212] As used herein in the specification and claims, the phrase "at least one" in reference to a list of one or more elements should be understood to mean at least one element selected from any one or more of the elements in the list of elements, including, but not necessarily, at least one of every element specifically recited in the list of elements, and not excluding any combination of elements in the list of elements. This definition also allows for elements other than those specifically identified in the list of elements to which the phrase "at least one" refers, optionally, to be present, whether related to those specifically identified elements or not. Thus, as a non-limiting example, "at least one of A and B" (or, equivalently, "at least one of A or B" or, equivalently, "at least one of A and / or B") can refer, in one embodiment, to at least one A (optionally including elements other than B) that optionally includes two or more things in the absence of B; in another embodiment, to at least one B (optionally including elements other than A) that optionally includes two or more things in the absence of A; in yet another embodiment, to at least one A that optionally includes two or more things and at least one B (optionally including other elements) that optionally includes two or more things; etc.

[0213] For purposes of this specification, when method steps are described in a sequence, the sequence does not necessarily mean that the steps should be performed in chronological order in the sequence, unless there is another logical way of interpreting the sequence.

[0214] Additionally, where features or aspects of the invention are described in terms of a Markush group, those skilled in the art will recognize that the invention is also thereby described in terms of any individual component or subgroup of components of the Markush group.

[0215] (Detailed explanation) In the following description, it should be noted that like or identical reference numbers in different embodiments indicate the same or similar features.

[0216] The following detailed description is illustrative of the invention and should not be limited in scope by the depicted embodiments, nor should it be understood in any way as a limitation on the broad description of the invention as set forth hereinabove. These embodiments are described in sufficient detail to enable those skilled in the art to practice or implement the invention. The precise shape, size, and appearance of the components described or shown are not expected or required by the invention, unless otherwise stated. It is understood that any use, combination, or structural, logical, electrical, and mechanical change, modification, extension, or modification of any of the embodiments described or otherwise related may not be made without departing from the scope of the invention. Similarly, any functionally equivalent products, compositions, and methods, along with all single, combined, and sequences of steps, features, structures, sequences, processes, combinations, and compounds, singly or collectively, referenced or shown in this description, will remain within this scope.

[0217] The entire disclosure of all documents, including patents, patent applications, journal articles, laboratory manuals, books, charts, repositories, and any other type of document or resource referenced herein, unless otherwise stated, is not in any way an admission of prior art, prior or common knowledge claimed by one of ordinary skill in the art, or any other connection or assumption to the present invention.

[0218] Features presented throughout the figures, except for the first figure which serves as an initial overview, are referenced using the numerical order of the step of the invention to which they belong, as well as their logical order within the figures themselves.

[0219] The present invention will be described in terms of embodiments relating to analyzing a particular portion of orthopedic hard tissue and a corresponding prosthetic implant to determine, based on previous surgery, the potential quality of their resulting connection interface, the effects resulting from the connection procedure, any modifications that may be required once connected, and the performance and longevity of this connection. However, the present invention has applicability more generally in the field of analyzing a particular portion of tissue for an entity designed to fit or be installed in relation to the particular portion of tissue.

[0220] With advances in sensor technology and modern processing techniques, large amounts of data are readily available and can be processed to allow meaningful information to be extracted. Sensors, including optical, acoustic, 3D, 2D, environmental, and situational sensors, can be combined and configured to provide data regarding their respective subjects.

[0221] This data can then be processed to derive insights and conclusions that would not otherwise be known. Numerous machine learning, data science, and mathematical algorithms and techniques exist to accomplish this processing, each dependent on the characteristics of the data, including its volume, dimensionality, precision, and redundancy.

[0222] Statistical analysis is one such category of these data processing techniques, which typically aims to summarize entire sets or pools of data as a whole and produce measurements from them. These measurements generally provide insight into different characteristics of the data, such as the mean, standard deviation, variance, median, and range.

[0223] Supervised machine learning is another category of data processing technique that typically aims to find patterns or trends present within a particular set of data to use as indicators for mapping the data to associated values. This means that once provided, the algorithm can search new data, find the same or similar indicators, and predict the associated values. This makes it possible to derive meaning from data, including optical and acoustic signals, where statistical measures such as the mean or standard deviation may have little significance. It generally works by first training a machine learning algorithm or technique and then running it on new data.

[0224] Training consists of processing a set of data and then using them, along with their associated ground truth, to build an internal model. The training process is generally divided into two distinct stages: a preprocessing stage and a manipulation stage. Preprocessing consists of cleaning, reorganizing, formatting, and deconvolving the data to achieve a more usable form. Manipulation consists of scaling or aligning the preprocessed data, decomposing it into its components or representative elements, and then aggregating the results, if necessary. The resulting data can then be used to generate a model by finding any patterns or trends therein and creating a mapping between them and the data's associated ground truth.

[0225] Execution consists of providing the trained algorithm with new data whose associated values are unknown. The algorithm will then process this data in the same way as in the training phase, finding any patterns or trends present therein that are similar to those already known. The algorithm will then match the new data against the associated values based on these similar indicators.

[0226] The embodiments disclosed herein aim to improve systems and methods for use by surgeons during orthopedic prosthesis implantation procedures to maximize prosthesis integration and survivability by providing alternative procedures that significantly reduce reliance on inaccurate measuring equipment; providing assistance to involved personnel during installation; and making informed predictions about potential problems and service life of the resulting connection interfaces.

[0227] This is accomplished by utilizing an approach centered around the use of various sensors, e.g., physiological and / or optical sensors, in conjunction with data read and stored across multiple surgical procedures. Different types of sensors provide single or combined subject-based data that can be processed and interpreted to extract information that cannot be obtained manually. This is aided by algorithms and methods trained from historically generated data and associated information that can predict the end result of a subject of interest given the same inputs related to that subject of interest.

[0228] It should be understood that the present invention is not limited to orthopedic surgery, nor to any particular type or form of tissue or implant, but rather the systems and methods disclosed herein may be utilized in procedures such as, for example, implantation or internal fixation of medical devices.

[0229] 1, a schematic flow diagram is depicted depicting an implant fit analysis process 10 divided into the individual steps comprising the process. The flow of information between these steps and the individual operations they may include is explained in the overview. Data collection 100 utilizes a series of different sensors, possibly in an interleaved arrangement, resulting in variable amounts and types of data 100a based on the subject, which may be the patient's tissue undergoing the procedure or the implant or prosthesis planned for implantation within the patient's body. Data processing 200 preprocesses and manipulates data 100a to generate processed data 201 with increased usability and evaluability. Data interpretation 300 analyzes the processed data 200a and extracts useful information and structures based on the tissue and implant. Fitness information 400 outlines the types of single or combined conclusions that may result from these interpretations 300a. This is complemented by passing the generated fitness information through various models and algorithms adapted to predict 500 the service life and performance of the prosthetic implant. The models and algorithms utilized in the prediction step 500 are initially generated through a machine learning or training process based on a mapping between historical fitness information and the post-operative status of those patients. Once captured, the new compatibility information 300a can be passed through and its identified indicators will be mapped to corresponding values related to implant durability and predict the potential state of the connection interface 561. The resulting predictions 500a of implant performance and durability are used to inform the calculation of possible corrective actions 600 that can be taken by the surgeon to improve the performance and durability predictions 500 in real time while the procedure is in progress.

[0230] 2 depicts a detailed schematic diagram depicting an exemplary implementation of the data collection step 100 of the implant fit analysis process 10 as depicted in FIG. 1. Data is collected through a series of sensors that can vary across numerous possible embodiments in terms of their type, quantity, and placement. Sensors within these embodiments can operate independently or as part of a system or collection of sensors, each cooperating in some manner to increase the quality or quantity of sensed data. Each sensor can be completely self-contained or may require additional devices or systems to handle all or part of the required processing.

[0231] The physical placement of sensors is advantageously done to surround the subject to maximize sensing potential while causing the least amount of disturbance to the surrounding surgical environment. If sensors are present as part of a system in a collaborative setting, their placement should reflect this, such as sensing the subject from different angles and later combining the different perspectives together.

[0232] The sensors may be automated, manually triggered, or controlled through some combination of the two depending on the particular system embodiment. In situations where an appropriate sensing environment must be created, it may be more convenient to manually control the sensors when this environment is presented. Manual control can be achieved through approaches that may include voice control, gesture control, and different forms of physical actuation, the latter of which is present in certain embodiments described herein due to the precise control given to the surgeon or surgical assistant. Of course, in alternative embodiments, it may be more advantageous to have the sensors function autonomously, in conjunction with computational procedures to provide information without physical involvement from the surgeon or their assistants. Variations on these approaches may also exist, such as sensors that are automatically triggered when they perceive a required condition; for example, sensors may advantageously operate continuously in real time, and when a particular condition is perceived or observed, the sensor may, for example, generate an alarm, notify the surgeon that the condition has been achieved, or alternatively, identify an undesirable parameter and trigger the calculation of a corrective action to overcome or correct the undesirable condition, and trigger further action within the system.

[0233] In further embodiments, the sensor may be configured to sense in a periodic manner, since a perceived change may be unlikely to occur at any one time and its rate of sensing may be limited. In some embodiments, sensing need only occur once or may be continuous to provide a feed of information as close to real-time as possible. In situations where a snapshot or specific condition is sensed, providing sensed data in the form of a time delay may be implemented, since some condition may be required to yield useful data.

[0234] The choice of sensors and their configuration will depend on the object being sensed. Implant and tissue sensing will typically include at least one two-dimensional scanner (e.g., a 2D optical sensor array), a three-dimensional scanner (e.g., an OCT, structured light sensor, or laser line sensor), and a hyperspectral or spectral sensor. These should be positioned to encompass the implant or tissue at or near the implant site with a special focus, which is located over the area where osteotomy will or is occurring (as these are the primary areas that will be involved in the implant). Some of these sensors may operate in real time and sense periodically, provided they have access to a clear line of sight. Other sensors may be removed from direct operation until personnel have prepared the operating room environment for ideal sensing conditions before returning the environment after sensing has occurred (e.g., removing UV light sources from the environment so as not to interfere with autofluorescence measurement sensors). In both cases, it would be advantageous for trusted personnel to be provided with the ability to manually trigger the sensors in addition to their autonomous operation. The manual trigger will typically be equipped with a physical button or a touch screen control interface to allow for efficient interaction.

[0235] Referring to FIG. 2 , the surrounding environment and relevant personnel should be prepared for any sensing procedures that may occur, depending on the sensors in use (101). This may, in certain embodiments, involve implicit preparation of the environment to ensure or increase the probability that optimal conditions will occur, as well as temporary explicit modification of the environment if the sensors involved are unable to sense effectively during typical conditions. Such modifications may include having personnel remove any interfering equipment and adjust any environmental conditions, such as lighting. Certain embodiments will typically require some elements of implicit and explicit preparation, as will be understood by those skilled in the art. Because orthopedic surgery is generally time-constrained, both from a financial and medical perspective, reliance on periodic sensors operating around the typical operating environment of the surgical procedure is advantageous over those that require constant changes of settings and interruptions to the typical surgical procedure (which may occur less frequently during the procedure, which can be advantageous for significantly improving surgical outcomes, with minimal cost to the interruption of the surgical procedure itself).

[0236] The sensor configuration is prepared (102) in consideration of any sensing procedures that may occur during the surgical procedure, assuming the environment is in a state that allows this to be possible or at least efficient. This may involve changing the position, alignment, and orientation of the sensors, both independently and in relation to one another. Additional equipment, such as a stand or platform, may be required for these changes. In certain embodiments, the sensors will already be located in an optimized configuration as part of a pre-built system or platform. When the opportunity arises, the system as a whole can be moved into position within a relatively short time frame, reducing environmental impact and disruption to the surgical procedure. After preparations 101, 102 have occurred, the sensing procedure may begin (103).

[0237] Sensing 103 is undertaken based on a set duration, which determines the number of possible iterations based on the particular sensor utilized in a particular embodiment. In embodiments requiring the environment and sensor configuration to be adjusted for optimal sensing conditions, these parameters are likely constrained by those settings. During orthopedic surgery, this duration is likely to be only a few minutes, as time is critical to its success, meaning that only a few hundred sensing iterations are likely possible. In embodiments allowing for passive sensors, the duration may depend on the total lifespan of the object being sensed or the operations performed in connection therewith, with the iterations similarly determined. After sensing is complete, the previously implemented preparation means 101, 102 may be reverted, if necessary.

[0238] Data may also be collected directly through contributions from verified personnel or from the system 104, and may include documents, records, and databases that may either be derived from the surgical procedure or sourced from external storage repositories such as current or historical patient records. In certain embodiments, such external sources may comprise any resource that may provide additional information about the patient or the procedure they are undergoing, such as, for example, patient records, medical records, and historical surgical or procedure data.

[0239] All sensed and provided data will be collected and presented in an easily accessible format 105 as required by the necessary processing in step 200. Data collection may preferably involve extraction of data in whatever format is deemed most usable, generally determined by the sensor from which it originated. Sensed data obtained from multiple sensors may initially appear in raw format and must be converted into data in a form easily accessible to data processing step 200 so that meaningful calculations can be performed on the collected data and meaningful analyses and predictions can be derived therefrom. Such formatting of raw sensor data may advantageously be performed by an external control unit. Similarly, provided data may appear in a format that is not easily accessible, such as paper, requiring manual entry into a digital system to make it accessible to the data processing system. In certain embodiments, all data will advantageously be stored in the same way so that they can be accessed in the same way. This method of storage would ideally be random access memory (RAM) of the central system, but depending on the raw amount of data and the processing speed required for data processing, solid state drives or hard disks could alternatively be used. In alternative embodiments, a database could be used to store and access this data. Such a database could use strict storage and access guidelines such as those imposed by SQL, or could be more flexible and scalable using technologies such as NoSQL.

[0240] Figure 3 shows a detailed schematic diagram depicting an exemplary data processing step 200 of the implant fit analysis process 10 as depicted in Figure 1. Data processing involves preparing and manipulating data, generally converting it into a more useful form, both in terms of its usability and evaluability.

[0241] The collected data (201) is typically in a somewhat raw format, which may contain noise, errors, or redundancies. If data containing such imperfections is used raw, typically during processing, redundant calculations, inconsistencies, or incorrect results may result. These must therefore be corrected or removed (202), depending on their type and severity.

[0242] Noisy data may be defined as data that is partially correct but includes other portions that are corrupted or erroneous. The ratio between correct data and erroneous data is an indicator of the type of action that can be taken in response. If only a small amount is erroneous, it may be possible to correct this amount based on the correct data, or it may be removed, provided that the remaining data provides sufficient benefit in its reduced form. However, if the amount of incorrect data is large, removing the data in its entirety is likely the only option.

[0243] Erroneous data may be defined as data that is incorrect and potentially contains a value that cannot exist either through the medium that created it or in relation to the surrounding data. Erroneous data cannot be corrected in most scenarios because it typically bears no relationship to the value it should have had, and therefore is usually removed.

[0244] Redundant data may be defined as data that is free of error but that does not add any value or benefit to the dataset as a whole, but only serves to increase its volume and introduce inconsistencies. Redundant data cannot be corrected because it is technically correct, and therefore is usually instead removed or ignored.

[0245] Removal or modification of data depends largely on the origin and format of the data and the severity of the error. Depending on the format, removal may be relatively straightforward, leaving the remaining data in a reduced state. In some cases, the data will remain valid, while others may require additional modification to achieve this. This may involve combining the remaining data with another set of reduced data to create a complete set, or replacing the data with dummy data that would not affect the final result. In comparison, modification of data is more difficult and requires knowledge of the expected structure to determine what is missing or erroneous so that it can be corrected. Techniques for achieving this depend heavily on the data itself and may not even be possible. In certain embodiments, all redundancies and errors will be directly removed, while any noise will be corrected if additional benefit can be discerned.

[0246] Collected data (201) will typically need to be rearranged and formatted (203) to increase access efficiency and make its storage more logical from a processing perspective, with its initial form likely based on the order and format of its origin (such as a particular sensor system or collection of personnel) being suboptimal for operation.

[0247] In certain embodiments, rearrangement consists of collecting data from multiple sources and sorting it so that data with similarities or usable in similar ways will be grouped together, despite being from different sources. This will allow data to be searched based on characteristics they may have, and related data to be found within the same vicinity. Formatting will consist of various structures that increase the accessibility of different groupings in terms of the types of data that can be manipulated simultaneously and sequentially. Other embodiments may have different approaches to formatting and arrangement depending on their application.

[0248] The collected data (201) may be sampled (204) to create different segments, which provide additional utility compared to operating on the data as a whole. Sampling (204) may consist of reducing the data pool to a more representative one, whereby it may contain a smaller amount of data, but the value or benefit the data provides overall will be either the same as or relatively comparable to the full data set. It may consist of dividing or partitioning the data pool into individual segments, each segment usually having a different purpose defined by how it may be used. This may include separate segments for averaging, testing, training, and / or validation, according to requirements.

[0249] In certain embodiments, the entire data pool may first be reduced to a more representative sample, whereby the computational burden may be reduced and the remaining data may be more easily interpretable. This reduced sample would then be divided into multiple different segments.

[0250] In certain embodiments, this reduced sample may be divided into four different segments, and that division of the data pool will yield the best final results over any other division.

[0251] The first two larger segments are used as the primary data source, and all related processing is performed with the intent of deriving usable information. The results of each individual segment can then be compared or averaged to ensure that the results seen from processing one of the segments are a result of the processing and not due to inherent features within the data itself or any other inconsistencies. This comparison can also be performed between individual segments and several resulting combinations of them to monitor the effect of additional or different data on accuracy or derivable information.

[0252] The remaining smaller segments can be used to test the performance and effectiveness of the larger segments, which, as will be understood by those skilled in the art, will primarily be performed with machine learning data processing techniques, data science, and mathematical algorithms or methods to determine the accuracy of the calculations actually performed and whether they can be performed based on data that is not yet known.

[0253] Other embodiments may be implemented that determine the division of collected data based on the intended use and processing that will be performed on the data. It may be advantageous to use the data as a whole, or to use multiple sets of data and average the solution. Similar combinations or approaches may also exist for data segments that may not be directly involved in data generation, such as testing and validation segments (which may not necessarily exist).

[0254] The collected data (201) may need to be scaled or aligned (205) so that it is easily comparable. This is because values provided by different sources (or even values provided by the same source) may vary significantly in range, even if they may represent (or describe) the same case. By shifting the range to a common point, comparisons may become easier, and processing algorithms or methods with such requirements may become feasible.

[0255] In certain embodiments, this is done for all values originating from the same source during a single sensing step, and it may also be done for all sensing steps, depending on the situational and environmental differences associated with each. Data from different sources will likely not be scaled together, as their representations may be so different that the processing required to result in a comparable form would reduce its overall usefulness. Other embodiments will likely be scaled depending on the sensors used, the intended use of the sensed data, and the processing of that data.

[0256] The collected data (201) can be reduced, separated, or disassembled (206) into its component elements or individual elements that provide data for identifying and using only the main useful elements, as opposed to all of them.

[0257] This decomposition will reduce the amount of redundancy present, which in turn will reduce the computational load since the remaining elements will no longer be processed. However, this is based on the assumption that the constituent elements retain most of the utility, or at least enough that any small amount of utility retained by the remaining data will result in no or less benefit than reduced computation.

[0258] Selected components may hold relevance for a particular application or type of processing, while the remaining data cannot be used or would not yield meaningful information by doing so. This is particularly evident in machine learning, data science, and mathematical algorithms or methods, as components are typically good indicators when used in various complex mapping procedures.

[0259] In certain embodiments, data will be decomposed into its component parts when a particular element or set of elements will better represent the data as a whole compared to the entire dataset. They will also be used in conjunction with machine learning, data science, and machine learning algorithms or methods to increase their predictive accuracy, particularly in scenarios where the component parts are relatively more precise. Other embodiments will likely decompose data to some degree into its component parts, typically for the same reasons as the embodiments discussed above, but possibly in different quantities and scenarios.

[0260] Collected data (201) and possibly constituent elements can be aggregated together (207) to create individual entities that have greater utility compared to the individual data or elements of which they are made. By collapsing available data into a single representation, this also reduces the amount of redundant computation involved.

[0261] The aggregation approach (207) depends largely on the use, type, and representation of the data and the form of processing in which the results will be used. A simple approach may involve, for example, averaging the data involved together, while a more complex one may involve, for example, providing weights to each individual element and implementing procedures that process and combine them based on these weights. As the amount of information associated with the data and the context of the use increases, the complexity and usefulness afforded by these aggregation approaches may likewise increase.

[0262] In certain embodiments, data or component elements may be aggregated together (207), provided that this aggregation provides advantages over what would otherwise be possible individually. While this may be implemented for all data sources, it will likely be limited to data of similar origin, as different aggregation algorithms may require a certain amount of similarity to be productive. Other embodiments will likely aggregate data as well, with their dependencies determining how and to what extent this will occur.

[0263] Other processing methods 208 may optionally be utilized 208 in addition to those described above, as will be understood by those skilled in the art, and according to requirements. The order and presence of data processing steps employed in a particular embodiment does not necessarily reflect the order and presence of the approach 209 described herein. For example, according to the requirements of the type of data collected, the particular data processing steps utilized for a particular application may include any useful selection of available data processing steps 209, and such selected steps may be applied in any suitable order.

[0264] Turning now to Figure 4, there is shown a detailed schematic diagram depicting an exemplary data interpretation step 300 of the implant fit analysis process 10 as depicted in Figure 1. Data interpretation involves analyzing the processed data in an evaluable form (301) and generating information and statistics based on that data that can describe various characteristics.

[0265] Measurements defined by mathematical or statistical formulas, theories, or concepts may be calculated (302) based on the assessable processed data (301) obtained from the data processing process 200 to generate summarized information. These calculations will typically result in a single value that can describe a particular characteristic or set of characteristics associated with the data used. This may include measurements such as the mean, standard deviation, and variance of the data. These calculations should be performed based on sets or samples of processed data that contain some degree of similarity, as if the data were completely independent, the results would reflect this independence and may have little practical utility.

[0266] While measurements themselves may not allow conclusions to be drawn based solely on them, they have alternative utility in providing reinforcement for conclusions developed through other data interpretation approaches, which may likely be their primary purpose, particularly if the desired conclusion is fairly complex.

[0267] In certain embodiments, these calculations are performed based on all data samples, assuming they are similar enough to yield useful results, and this similarity may be based on their origin, their processing method, or their subject. Other embodiments will likely perform these calculations similarly, but the data sets they use as input may differ based on their application.

[0268] Custom, specialized, or standardized measures may be calculated based on the assessable processed data (301) to generate information (303). These calculations are typically based on the data itself and its representation, which in turn are closely related to its origin, or more specifically, the particular sensor that generated the data (assuming the data was actually generated by a sensor). This means they are highly application-dependent and may not necessarily be included in all embodiments (although this is likely to be the case if an embodiment has the required conditions and capabilities to utilize them). Such calculations may include those based on image coloration, acoustic signal wavelength, or position readings.

[0269] Custom or specialized calculations are those applicable only to specific situations and may have been created or modified specifically for this purpose. Standardized measures for comparison are those created and maintained by a standards organization that have the same meaning and formula, regardless of their subject or the data from which they are provided.

[0270] In certain embodiments, both custom and standardized measures are used where benefits can be derived from each. Custom measurements will consist primarily of those directly related to medical or surgical procedures, such as determining the mechanical axis for a particular knee joint. Standardized measures will be derived primarily from the International Organization for Standardization (ISO) and may include those based on geometric, structural, and morphological measurements. This will allow attributes including surface flatness and roughness to be calculated in a comparable manner. Other embodiments will likely utilize both, provided they exist in situations that would allow this.

[0271] Machine learning, data science, and the implementation of mathematical algorithms or methods 304 can be used to generate predictions based on the assessable processed data 301. These predictions will typically detail certain characteristics of the data that cannot be definitively determined to varying degrees of accuracy.

[0272] Predictive algorithms and methods come in many different forms, separated by their usage requirements. The quantity and quality of data provided to them determines their level of accuracy and therefore their usability. Ideally, each set of data provided should be reasonably independent and large enough so that the predictive algorithm or method can learn why data is present in the set it is in and any edge cases that may exist.

[0273] The data run through these predictive algorithms or methods typically cannot be used raw and must be processed in a specific way based on the form of predictive analysis, which may involve converting the data into a more accessible form before converting it back into a more assessable form (increasingly single and easier to collaborate on, often consisting of specific component elements).

[0274] In certain embodiments, supervised algorithms or methods will be the primary form of predictive analytics. These work by mapping input data to a value or set of values using indicators determined through prior historical data. This process involves two main steps: training and execution.

[0275] Training consists of providing an algorithm or method with a large amount of data along with a value or set of values that each should correspond to. The algorithm or method will look at the data and the corresponding values and determine which indicators in the data result in which values. A computational structure is created from this mapping that takes the data as input and, based on the indicator, returns its corresponding value, which it includes as output.

[0276] Execution consists of passing new data through this structure / model, which extracts relevant indicators therefrom and then returns a corresponding value or set of values, which could include, for example, calculated values representing a numerical prediction of the implant performance and life expectancy of an orthopedic implant.

[0277] Evaluation of the evaluable processed data (301) may be performed manually (305), either by verified personnel or through prior documentation. In certain embodiments, it consists of a surgeon or other medical practitioner examining the data as it is generated and providing conclusions and insights based on their experience, which may be used to train computational models used to analyze the data. Similar conclusions may also be provided preoperatively based on medical records, which may inform the various processes and approaches described herein.

[0278] Data analysis may be performed to varying degrees by the sensor itself or through an attached control unit 306. This will likely be highly data dependent, whereby the analysis provided may be based on attributes or characteristics for which that particular sensor was specifically engineered.

[0279] The results of this internal analysis may be of independent benefit, or it may be used as additional data that can be processed by and aid in subsequent interpretation approaches by being included as assessable processed data 301. In certain embodiments, both of these approaches may be used, as it will be appreciated that internal sensor processing may yield information that is independently useful and information that is useful as part of a larger data pool.

[0280] Other interpretive approaches may exist in addition to those mentioned above. (307) The order and presence of these approaches do not necessarily reflect the order and presence of the approaches herein. (308)

[0281] 5 illustrates an exemplary implant and tissue interface, each state outlined as part of a depiction of an exemplary fitness information generation step 400 of the implant fitness analysis process 10 as depicted in FIG. 1. The state refers to the condition of the implant 401 or tissue 402 at a particular given moment, which is typically determined intraoperatively. It can be interpreted as a set of characteristics that can be used to describe a particular part or area of itself.

[0282] Implant condition 404 comprises a set of descriptors providing information related to the construction and integrity of the physical implant. Composition 405 describes the type of material the implant may be made of. Certain materials may degrade faster, be more vulnerable to impact, or cause a reaction when used against some types of tissue. Degradation 406 may describe both the current state of the implant and the rate at which it will naturally degrade once inserted. If the implant has already begun to degrade or has an accelerated rate of degradation, inserting it will likely result in reduced service life and performance for the patient. Density 407 may describe the degree of particle occlusion present within the implant, providing an indication of its hardness and the degree to which it may react to external trauma. Particle dissolution 408 is the rate at which material particles may be expelled from the implant and how this rate changes over time. These particles are generally considered foreign bodies within the patient and may prompt internal responses that may damage the connection interface between the implant and tissue.

[0283] Tissue condition 410 comprises a set of descriptors that provide information related to the patient's tissue health at the implant site. Composition 411 describes the types of minerals that may comprise the tissue. Different minerals and their abundance generally provide reliable indicators of the health and age of a particular tissue, particularly when variations in these properties exist. This is further reinforced by tissue density 412, which defines the tightness of these mineral infills or at least how specific minerals relate to one another. Hydration 413 may describe the water content present in the tissue, which may be useful in measuring the effects of any previous osteotomy and timing implant insertion. Necrosis 414 is the death of tissue cells, which may be caused by the osteotomy method or internal issues within the body. Discoloration 415 is the specific color the tissue exhibits; any variations typically cannot be discerned without advanced visual sensors. Reflectance 416 is the amount and color that the tissue can actively reflect. Thermal consistency 417 is the temperature of the tissue and the degree to which temperature is dispersed throughout the tissue. Measuring thermal consistency is often a good way to monitor the extent to which tissue is being affected when performing osteotomies and other surgeries.

[0284] These condition descriptors are generated based on the interpretation procedure 308 detailed in FIG. 4 and may include or be influenced by any patient-specific conditions or structures. Generation of all descriptors may not be possible depending on the available data sources and the types of descriptions that may be useful for a particular application. The implant condition descriptors 404 and tissue condition descriptors 410 explored herein are those that may be useful in determining compatibility between implants and tissues as part of certain embodiments, although other implant 409 and other tissue 418 condition descriptors may exist, as will be understood by those skilled in the art.

[0285] Figure 6 illustrates an exemplary implant-tissue interface, with the morphology of each (i.e., implant prosthesis 401 and patient tissue 402) outlined as part of the depiction of an exemplary fitness information generation step 400 of the implant fitness analysis process 10 as depicted in Figure 1. Morphology refers to the shape, form, or structure of the implant 401 and tissue 402, which is typically determined intraoperatively. It can be interpreted as a set of characteristics that can be used to describe a particular part or area of itself.

[0286] Implant and tissue morphology 421 are equipped with a set of descriptors that provide information related to their shape, form, and structure. Shape 422 typically describes the geometry in terms of its contours and mass. This descriptor is the basic starting point in determining morphological compatibility between implant and tissue, as it will define whether the two can actually fit together. If either shape causes a collision when joined, insertion is not necessarily possible. In certain embodiments, the distance between the contours of each shape during insertion should be as small as possible. Porosity 423 describes the number of small physical pores an entity may contain and the size and distribution of these pores. Stiffness 424 describes the degree to which a particular entity is fixed in terms of its inability to be moved or bent into a different shape. While not necessarily critical when used with respect to a single entity, when used with respect to two or more, it can provide a measure of connectability and fault tolerance.

[0287] Flatness 425 describes the deviation between the height of peaks present on a particular surface and its average height. If this deviation is relatively large, the surface may be considered to have a low degree of flatness, and the opposite is true if it is relatively small. However, this definition often depends on the situation and application, as a surface that has an uneven distribution but allows an object to be placed flush on it may still be considered flat. In certain embodiments, it may be defined according to the ISO standard, which states that a surface may be considered flat if the peaks and troughs present within it do not exceed a certain limit. This limit is likely to be set at 0.3 mm, which is the maximum clearance required to reduce postoperative issues such as aseptic loosening. All surfaces of the tissue 402 may need to be flat to accommodate the surface of the implant 401.

[0288] Parallelism 426 describes the deviation and distribution of peak heights between one surface and another. If their peak heights and distributions are similar, both can be assumed to be parallel. This definition also often depends on the context and application in which it is used. In certain embodiments, it will be defined according to ISO standards. This states that if the peaks and troughs of a surface, according to its current angle, do not exceed a predetermined limit, it can be considered parallel to a particular reference plane or other surface. All corresponding surfaces between the implant 401 and the tissue 402 may need to be parallel to ensure maximum contact. This likely means that the predetermined limit should be a minimum.

[0289] Roughness 427 describes normal irregularities affecting the surface peaks and troughs, typically resulting from a particular machining process or natural biological growth. Waviness 428, by comparison, instead refers to abnormal irregularities and tends to be more spaced apart or of longer wavelengths. This is generally considered a broader form of roughness. It typically results from tool distortion, vibration, or heat treatment. In certain embodiments, the tissue roughness and waviness can be advantageously engineered to match that of the implant and promote osseointegration.

[0290] These morphological descriptors are typically generated based on the interpretation procedure 308 detailed in Figure 4 and may include or be influenced by any patient-specific conditions or structures. Generation of all descriptors may not be possible depending on the available data sources and the types of descriptions that may be useful for a particular application. While the descriptors 421 explored herein are those that may be useful in determining compatibility between implants and tissues as part of certain embodiments, other implant and tissue morphological descriptors may exist 429.

[0291] 7 illustrates an exemplary implant-tissue connection interface and its associated compatibility information outlined as part of a depiction of an exemplary compatibility information generation step 400 of the implant compatibility analysis process 10 as depicted in FIG. 1. The compatibility information 400 refers to characteristics, traits, and attributes related to the quality of the connection interface 403 that exists between a particular implant 401 and tissue 402.

[0292] Tissue health 442 comprises the tissue condition 410 of the patient's tissue 402 and the condition the patient's tissue will be affected by both the implantation procedure and the implant itself. This is divided into two distinct considerations. The first involves whether tissue will be able to reside within the connection interface 403. If tissue health is too compromised (as may be the case in some patients), a replacement procedure may not be beneficial or recommended. This may also be the case if tissue health would not allow it to properly participate in the interface, such as if its potential for osseointegration is relatively low and it may react negatively with various types of fixatives. The second consideration is how it will reside alongside the implant and, further, how suitable the implant material is.

[0293] The suitability 443 of the implant material with the condition 404 of the implant 401 involves two main areas that are closely linked: The possible effects of implants: for example, if the implant is made of a material known to be relatively brittle and would therefore have a large amount of particle dissolution, there is a high probability of an internal reaction, which may result in damage to the connection interface. The same can occur if the material, through its contact with the tissue, induces a natural reaction, such as an allergic response. The influences that the implant itself may be subjected to, for example, everyday stresses based on patient activity, are expected depending on the replaced joint. However, if this stress is too great or too frequent, trauma can occur. This may make the implant increasingly susceptible to further stresses, which may create or exacerbate issues related to its connection interface. This information, along with tissue health, will provide insight into how the tissue and implant will interact as part of the connection interface 403.

[0294] Implant insertability and associated difficulties 444 involves comparing the implant and tissue morphology 421 after proper preparation to determine the feasibility of connecting them. This will typically involve one of two different scenarios. The first scenario is that the required osteotomy was not performed or is not to the required depth, and the tissue would be too large, making insertion unlikely. The second scenario is that the required osteotomy was performed and exceeded the required depth. This means that the distance between the tissue contour and the implant contour is too large, and therefore insertion would be relatively easy, but the resulting fit would be poor. In certain embodiments, a result between these two scenarios can be achieved where the distance between the implant contour and the tissue contour is minimized.

[0295] The degree of contact during insertion 445 details the quality of the fit or connection interface that exists between the implant and the tissue. If there is a small degree of contact, or if the contact is distributed in an uneven or irregular manner, the resulting connection interface may be considered poor. This is because the less contact there is across the fit, the more difficult it is for the tissue to successfully integrate into the implant. Instead, only certain sections may be properly attached, which means that when the interface is under stress, these sections will be unevenly affected and wear more rapidly. This effect is less pronounced when a fixative is used at the connection interface, but it is still important because the same problem would occur if not all areas of the implant were in contact with the fixative. Relatively speaking, if there is a large degree of contact and this contact has an even distribution, the resulting connection interface may be considered high quality. This is the desired result for the specific embodiment discussed above.

[0296] Implant kerf coverage and distribution detail the extent to which the tissue surface is shaped 446 to receive the implant kerfs in an advantageous manner. The implant kerfs are specific coatings over the surface of the implant intended to promote osseointegration of the tissue. This will likely consist of a proportion and distribution of matching peaks and troughs, each inserted into the corresponding troughs and peaks of the implant kerfs. This may be based on the morphology of the kerfs themselves, as opposed to any particular implant, since the kerf pattern will likely be independent of the implant. In certain embodiments, the tissue surface will preferably match the implant kerfs so that a higher level of osseointegration can be achieved.

[0297] This compatibility information is generated based on the tissue and implant characteristics 404, 410, 421 detailed in Figures 5 and 6 and the interpretation procedure 308 detailed in Figure 4, and may include or be influenced by any patient-specific conditions or physical structures, not all of which may be possible depending on the available data sources and types that may be useful for a particular application. While the compatibility information 400 explored herein may be useful in defining compatibility between implants and tissues as part of certain embodiments, other compatibility information may exist 447.

[0298] FIG. 8 illustrates an exemplary implant-tissue connection interface, and the effects of the insertion process are outlined as part of a depiction of an exemplary fitness information generation step 400 of the implant fitness analysis process 10 as depicted in FIG.

[0299] Inserting an implant into tissue during surgery is not an easy task. It typically requires a large amount of physical force from the surgeon or other involved personnel. This is especially evident when the tissue has undergone multiple osteotomy procedures to ensure that its post-osteotomy morphology (i.e., the connection interface 403) is as suitable as possible for the implant, leaving only a minute gap for insertion. According to some literature, this should be interpreted as each point in the tissue 402 being a maximum of 0.3 mm away from the implant 401.

[0300] Thus, while the likelihood of damaging the implant is fairly low, issues or damage (to either the tissue or the implant) may occur to the tissue 402 and implant 401 during the implant insertion process. This typically involves damaging or destroying various post-osteotomy details along the tissue surface (e.g., at the connection interface 403). For osseointegration-based procedures, this would consist of destroying peaks and disrupting their distribution 461, resulting in an imperfect interface surface, as shown in FIG. 8. For fixative-based procedures, it would consist of spreading the fixative irregularly, such that some areas 462 may have more than others.

[0301] While this may not be as detrimental for fixative-based procedures, for procedures that rely on osseointegration, this process essentially changes the tissue morphology. Based on these changes, it is possible that the new morphology results in a lower quality connection interface. This may create or increase the probability of some postoperative problems occurring.

[0302] The extent of peak fracture or fixative displacement may be analyzed and predicted prior to insertion based on previously compiled compatibility information 400 as detailed in FIG. 7. This evaluation may be used to inform the generation of other compatibility information and may prompt the regeneration or recalculation of that which may already exist. This should be performed as many times as deemed necessary according to requirements, for example, to maximize implant performance and life expectancy predictions in step 500 of implant compatibility analysis process 10. In certain embodiments, this will be interpreted each time the generated compatibility information for the implant, tissue, or their resulting connection interface is changed.

[0303] FIG. 9 illustrates a partially simulated implant 401, tissue 402, and resulting connection interface 403 for assessing ideal placement of the implant as part of a depiction of an exemplary compatibility information generation step 400 of the implant compatibility analysis process 10 as depicted in FIG. 1 .

[0304] The placement may comprise many different measures and characteristics, including, for example, the degree of contact between the implant 401 and the tissue 402, the angle of the implant relative to the tissue, and the stress distribution of the implant.

[0305] A visualization based on the morphology of the implant 472 and tissue 473 can be generated. An ideal placement 471 can then be derived from these individual visualizations, with the previously generated compatibility information 400 to guide this process. In certain embodiments, generating the compatibility information can involve determining the maximum amount of contact possible between the implant and tissue, the most favorable angle for the implant to be inserted, the amount and likely distribution of any fracture, and / or the diffusion or displacement of any added fixative.

[0306] The results of physically inserting the implant onto the tissue can be compared to this ideal placement to determine the degree to which they approximate together and what may need to be modified to minimize this difference. This may involve generating information based on the physical fit using various sensors or other measurement devices. This equipment may be general-purpose, special-purpose, or previously used such as those detailed with reference to data collection process 100 of FIG. 2, and may require processing and interpretation similar to that detailed with reference to processes 200 and / or 300 of FIGS. 3 and 4, respectively. The information generated based on the physical fit and the simulated fit may require a certain degree of similarity to be comparable.

[0307] The comparison-based feedback can be quantitative or qualitative. Quantitative feedback can consist of indicators that provide information about the amount an existing implant or tissue should be adjusted to achieve a more favorable comparison. Figure 10 demonstrates the types of indicators that can be used, including implant or tissue translation in all spatial directions 491, 492, and 493, and implant or tissue rotation across all rotational axes 494, 495, and 496.

[0308] Qualitative feedback may consist of recommendations or additional considerations based on the insertion process, including an analysis of the amount of force used and whether the amount of force should be increased or decreased, historical patterns or trends toward specific insertion issues, and whether the insertion angle was suboptimal.

[0309] An insertion may be returned when it is deemed relatively unfavorable beyond a certain limit. In this scenario, some additional calculations, such as simulated fit, physical fit, and surface failure or fixative displacement prediction as discussed above, would need to be repeated.

[0310] Figure 11 is a detailed schematic diagram depicting the data processing portion of an exemplary implant performance and life expectancy prediction step 500 of the implant fitness analysis process 10 as depicted in Figure 1. The process and steps involved are very similar to those detailed in the procedure 200 of Figure 3, except for the data sources and the intent behind the processing of the data.

[0311] The data sources involved are raw compatibility information, medical records, and other patient data 501, either generated (e.g., from suitable sensors during the procedure) or obtained from external sources. This data should include sufficient information to determine the compatibility of the tissue with its associated implant, along with the health and lifestyle details of the affected patient.

[0312] The idea behind processing the data is to best prepare the data 501 for training and execution within predictive algorithms and methods, which may involve different types of pre-processing and manipulation to transform the data into a form that provides the most advantage for this use.

[0313] Data preprocessing 502 involves converting the data into a usable format in preparation for subsequent data operations 508 to yield the most useful results. This data may initially be in an improper format and likely be used for the purpose of describing a particular connection interface 403. Because this purpose differs from the intended predictive analysis, it is possible that at least some of the data provided may be considered noisy, erroneous, or redundant and may be processed as discussed above. This could potentially introduce inconsistencies into subsequent processing. To minimize the risk that such inconsistencies will affect the results, any defects or other issues present in the data should be cleaned 503 through removal or by being corrected, as discussed above, provided that the amount of benefit provided by the corrected portions outweighs the effort required to achieve them. In certain embodiments, defective data may be immediately removed unless a viable path to correcting them exists.

[0314] As part of the pre-processing step 502, the patient data 501 may need to be rearranged and formatted to increase its efficiency and make its storage more logical in relation to various predictive approaches (504). Its current format likely reflects its use in describing the interface 403 between the implant 401 and the associated tissue 402, and may be presented in a manner that increases its efficiency in doing so, which is likely suboptimal for predictive analysis.

[0315] In certain embodiments, specifically for the intended prediction approach, the rearrangement step 504 will consist of grouping together data that may have established similarities or other relationships. This will make accessing or retrieving related data or data that accurately represent a particular aspect or set of aspects easier and more efficient. Formatting will consist of structuring these different groupings to allow different sets of data to be simultaneously and subsequently manipulated and analyzed. This will make traversing from one set of data to another related set of data relatively simple and computationally inexpensive. Other embodiments will have different approaches to formatting and arrangement, depending on the type of manipulation and subsequent prediction approach intended for the data.

[0316] The patient data (501) may be sampled as part of the pre-processing step 502 to create different portions (505) that may provide additional utility as opposed to operating on the data as a whole. Sampling (505) consists of reducing the data pool to one that is more favorable for a particular type of use, such as reducing the data as a whole to only a portion that can be considered representative.

[0317] In certain embodiments, for a particular intended predictive approach, data 501 is first sampled to create a single data pool that is more representative of the data as a whole (505). This means that the utility provided by this representative data pool should be greater than or equal to that of the original. This representative pool will then be divided into three distinct segments. The first, largest segment, known as the training set, will be used to train predictive algorithms and methods. The second, smaller segment, known as the test set, will be used to test the trained predictive approach. The third, even smaller segment, known as the validation set, will be used to verify the results of the trained predictive approach that yielded a favorable accuracy rate on the test set.

[0318] Other embodiments will likely use a similarity sampling approach that is consistent with the prediction approach, although additional customization may be made depending on the specifics.

[0319] Manipulation (508) of patient data (501) involves converting it into a highly evaluable format in preparation for and to obtain the most usefulness from a subsequent prediction algorithm or method 513. This data may initially be in a format in which values exist based on how they were originally represented. Because representations will likely vary across the data, achieving an adequate level of comparability between different sets may not be feasible, or it may be done to a suboptimal degree. By scaling or aligning these values to a common point (509), comparability between different sets is increased.

[0320] In certain preferred embodiments, all values present in the dataset that can be considered comparable and have directly or similarly equivalent initial expressions, particularly for the intended predictive approach, should be scaled (509). This is because some types of predictive analytics generally work better when all data is within some known range. It also makes it easier to handle and distinguish the data, particularly when presenting it, if the need arises. Other embodiments will likely use similar scaling techniques, which will again be based on their intended predictive algorithm or method.

[0321] Patient data 501 can be reduced, split, or decomposed into its component elements 510 as part of data manipulation according to requirements. These resulting individual elements can be used to identify existing features that make up the data and may be more informative or representative compared to others. This is important for predictive analytics, as these types of features generally make good indicators and can greatly increase its usefulness.

[0322] In certain embodiments, particularly for the intended predictive approach, the data are decomposed into constituent elements (510) where individual elements or other features are found to make a significant contribution in determining the overall description of the data as a whole.

[0323] The provided data 501 and the constituent elements derived therefrom may be aggregated together into a single entity 511 as part of a data operation. The aggregated entity should provide more utility compared to the individual elements or data used to create it, although this may not be the case if the decision is made from a storage or computational perspective.

[0324] The aggregation approach typically depends on the type and representation of the data or constituent elements involved. Elements may need to share a degree of similarity or equivalence to be considered for aggregation.

[0325] In certain embodiments, elements should be aggregated together if they would provide additional utility, particularly for the intended predictive approach (511). This means that if the aggregated entity indicates better characteristics of the data set compared to the individual elements, the aggregation should be retained.

[0326] The final processed data 514 results after the provided data 501 has been pre-processed (502) and manipulated (508) according to requirements. Other pre-processing approaches (506) and manipulation approaches (512) may exist outside of those explicitly outlined herein and need not necessarily be performed in the order presented, or at all (511). The determination and order of approaches depends entirely on the available data and the intended use.

[0327] Other pre-processing approaches 506 and manipulation approaches 512 may be utilized, if desired, in addition to those described above, as would be understood by one skilled in the art. The order and presence of these pre-processing approaches 507 and manipulation approaches 513 do not necessarily reflect the order and presence of the approaches as depicted in FIG.

[0328] Figure 12 shows a detailed schematic diagram depicting the information prediction portion of the exemplary implant performance and life expectancy prediction step 500 of the implant fitness analysis process 10 as depicted in Figure 1. This involves using three different data sources within a series of different prediction approaches to generate information and values that may provide insight into how long the implant will last and the causes of its degradation, if applicable.

[0329] The first data source is suitability information, medical records, and other patient data 514, which has recently been processed to provide additional utility during predictive analysis as detailed in Figure 11. The second data source is the same except that it includes additional historical processed data 551 that has been previously generated. These sources will be used as derivable data from which indicators and other mapping mechanisms can be found.

[0330] A third data source, containing a specific set of values corresponding to each value in the second data source 551, is historical data 552 of actual implant performance and life measurements provided by previous patients, which can be used as ground truth and what can be predicted.

[0331] Predictions may be generated based on the first data source (514) by training and running (553) different forms of machine learning, data science, and mathematical algorithms or methods. In certain embodiments, this will consist primarily of different supervised approaches. These types of approaches generally operate in two distinct phases, including a training phase and an execution phase.

[0332] The training phase involves second and third data sources 551 and 552, where each set of data in the second data source 551 maps to a particular set of values in the third data source 552. It consists of identifying indicators in each set of data that are partially or substantially involved in this mapping, so that if another set of data contains these same indicators, it is likely that it will also have the same or similar corresponding values. This will continue until a mapping structure is developed that will map the analyzed indicators to their most commonly referenced values.

[0333] The execution phase involves only the first data source 514, which has no known corresponding values. It consists of first identifying the same indicators found during the training phase within each set of data in this source. These indicators are then fed into the previously created mapping structure to identify the values to which they correspond. These values are then defined as the values to which the initial set of data may correspond.

[0334] This training phase is often performed using different segments of data, as opposed to the data as a whole, which may include training, test, and validation segments, where the data and corresponding values are known for each. This would begin by first generating a mapping structure corresponding to only the training segment. The data in the test segment would then be run through this structure, and the values it returns would be compared to the actual known values of the segment. This would provide a measure of accuracy depending on how close the returned values are to the actual ones. If this accuracy is satisfactory (somewhere between 95 and 100%, depending on the specific embodiment), it would be tested again using the validation segment. This is to simulate its performance on real-world data, since it has previously experienced the training and test segments, but the validation segment will remain unknown to it. This ensures that the mapping structure will perform well based on all the data, as opposed to just the test segment (a phenomenon known as overfitting).

[0335] Supervised algorithms or methods vary widely not only in their complexity but also in their predictive power, and using various types of them in parallel can provide useful results in addition to comparisons. These algorithms or methods can alternatively include linear and polynomial regression, logistic regression, naive Bayes networks, Bayes networks, support vector machines, decision trees, random forests, k-nearest neighbor classifiers, and neural networks, including other algorithms or methods, as would be understood by one of ordinary skill in the art.

[0336] Other embodiments may use different predictive approaches, including unsupervised, semi-supervised, and reinforcement approaches, as will be understood by those skilled in the art. Unsupervised and semi-supervised algorithms or methods are provided with a dataset and attempt to extract meaning from it with little or no instruction as to what they are looking for. This allows unknown information or connections present in the data to be discovered, which may provide additional utility depending on their content and their consistency in other datasets.

[0337] Reinforcement algorithms or methods may attempt to launch a series of calculations with the goal of resulting in a particular value. They are provided with a positive or negative stimulus depending on the accuracy of this value compared to what they should have had. When provided with a positive stimulus, they may continue performing the same calculations they were doing and perform additional calculations similar to these. When provided with a negative stimulus, they may stop performing their current calculations and try one that is different to a varying degree. A degree of randomness is typically added to these algorithms to give them a starting point, which means they may require more execution cycles to reach a satisfactory result compared to previous predictive analytics approaches.

[0338] Predictions can be generated based on the processed data 514 by launching simulations with different types of scenarios, events, and conditions that may affect the embeddings (553). These types of cases are likely to be mathematically simulated, and a probabilistic measure is added to account for situations that are currently inconclusive.

[0339] In a particular embodiment, a simulation will be designed for different types of embedded impairments and the scenarios in which these can take shape. It will be provided with two main sources of data.

[0340] The first source 514 would be processed data containing various information related to the quality of the implantation procedure, which would be used to determine the types of problems that may be most prevalent or to which the implant and associated tissues may be vulnerable.

[0341] The second source 552 is information related to lifestyle and other aspects of the patient, which may include their level of activity and the resulting average amount of trauma their implants can endure. This information will indicate the rate and degree of deterioration any issues may undergo and the probability of physical trauma causing them.

[0342] Although the simulation is currently referred to as unitary, this may not be the case, as additional benefits may be found by dividing it into individual simulations, each with its own unique purpose or predictive goal. Given the complexity typically involved, division may be advantageous, at least from a development and production perspective.

[0343] Other embodiments may utilize different simulations depending on the situation and application, which will likely depend on the type of implant, as procedures occurring within the human body will be affected differently depending on the tissue or body part that is replacing or augmenting.

[0344] The generated predictions will be used to provide insight into information related to the performance and longevity of the implant procedure (556). These types of information typically involve either the influence of certain variables on the implant (557-558) or the correlation between several variables and the condition of the implant (559-560). In certain embodiments, they will be based primarily on orthopedic indicators that define when problems may arise with a particular implant. This will allow appointments to be made in advance and certain preventative measures to be taken intraoperatively, resulting in more favorable outcomes.

[0345] A patient's lifestyle in terms of their activity level indicates the amount of trauma an implant will typically endure. The impact of this trauma and the degree to which it may worsen over a period of time can be predicted by comparing this activity level or any particularly high-impact events with a deterioration rate determined based on patient input (558).

[0346] Implant composition and tissue health will be known to some extent prior to the implantation procedure, and by comparing these two sets of information together, the implications for implant longevity and when a patient may require revision surgery can be predicted (559).

[0347] The physical condition, health, and age of the patient may be assumed to have a strong correlation 560 with the useful life of the implant. The types of circumstances and trauma in which the implant may be likely to be vulnerable may potentially be determined through this correlation. The point at which revision is deemed necessary may be predicted based on this information and historical data of similar patients.

[0348] The morphology of the tissue and implant (561) determines the quality of the associated fit or connection interface that may exist between them. If this connection interface begins to deteriorate, morphology, especially when used in conjunction with predictions made related to the health and composition of the implant and tissue (558), may be a likely insightful indicator of the possible reasons for deterioration over time. By comparing indicators related to morphology, and therefore the quality of the connection interface, to the point at which a revision is deemed necessary, it may be possible to predict when this point will occur.

[0349] Other predictive approaches and resulting information may exist outside of those explicitly outlined herein. (561) Predictive approaches are not necessarily performed only once; they may also be performed in parallel and sequentially if there is a reason to do so. (556)

[0350] FIG. 13 shows a detailed schematic depicting the generation of a set of histomorphology correction procedures for the surgeon to consider implementing.

[0351] The process begins (604) with an initial sampling (605) of a set of corrective actions that are ideal for shifting tissue morphology into the best mechanical alignment.

[0352] The resulting set of sampled corrective actions (606) for ideal mechanical alignment may not be possible to implement for various reasons, which will be detailed herein.

[0353] First, there may not be enough pre-existing tissue to form a tight fit that would result in the best implant performance and life expectancy (500).

[0354] Additionally, the accuracy of the surgical resection tools being used may be below a threshold that would allow for precise application of a set of corrective procedures. For example, if the ideal tissue morphology is a thin slice at some angle, this may be beyond the capabilities of the surgeon using the available tools.

[0355] As discussed above, the estimated resulting tissue morphology is fed into implant performance and life expectancy prediction (500) and simulated to provide resulting information (557), which can be used to compare against other simulated sets of treatments and existing conditions of the tissue.

[0356] The resulting information regarding the simulated resulting tissue morphology (557) is evaluated to determine whether the treatment set is desirable as detailed above and, if applicable, to calculate one or more numerical quantifications for use as comparator values (607).

[0357] The resulting information 557 is compared (608) against the best set of corrective actions simulated so far in the process (610), if applicable. If the resulting information 557 is the more optimal set of corrective actions as compared, the sampled set of corrective actions (606) is stored (609) and replaces the best set of corrective actions (610).

[0358] The process will then consider whether it has reached a current performance limit (611), which may be a limit on some type of scarce resource, such as computation time, real time, energy, storage space, or cooling capacity.

[0359] If there are resources available to continue searching for a better set of corrective actions, a relaxed set of corrective actions (605) will be sampled.

[0360] If resources are exhausted, the best set of corrective actions (610) is compared (612) against the current configuration's resulting information (557).

[0361] If the best set of corrective procedures (610) exceeds a predetermined threshold, it is displayed for their consideration for implementation to the surgeon 613. This can then result in a different implant performance and life expectancy prediction based on tissue condition and morphology after the surgeon performs the set of corrective procedures.

[0362] If the best set of corrective actions (610) does not exceed a predetermined threshold, the process alerts the operator that the action threshold has been reached (614), indicating that further substantial improvement is unlikely to be achieved.

[0363] The features presented herein may be implemented electronically through any possible system or machine capable of completing them within any limitations imposed by the particular application, which may be implemented online, offline, or in a capacity relying on some combination of the two.

[0364] Data extracted or generated as a result of the features presented herein can be stored electronically, which can be done offline, online, or through some combination of the two. It can be accessed immediately or within a delayed time frame for retrieval, processing, and any other form of use. All types of data can be stored, although some may only be maintained intermittently.

[0365] It should be understood that the features presented herein and the different processes they comprise do not necessarily have to be performed in the order described, nor do they require specific circumstances or situations. The order, nature, preparation, and execution may depend on numerous circumstances, such as those typically applicable to medically applicable inventions or methods. One such circumstance may be patient condition and morphology, which may require additional processes or customization to arise with any specific issues or limitations, such as those common to medical practice, such as orthopedics.

[0366] It will be understood by those skilled in the art that variations and modifications of the invention described herein will become apparent without departing from the spirit and scope thereof. Such variations and modifications that become apparent to those skilled in the art are deemed to fall within the broad scope and sphere of the invention as described herein.

[0367] Future patent applications may be filed in Australia or overseas based on or claiming priority from this application. It should be understood that the following provisional claims are provided by way of example only and are not intended to limit the scope of what may be claimed in any such future applications. Features may be later added to, or omitted from, the provisional claims so as to further define or redefine the invention or inventions.

[0368] Methods 10, 100, 200, 300, 400, 500, and 600 as depicted in Figures 1-4 and 11-13 (and associated submethods described herein) may be implemented using a computing device / computer system 1000 such as that shown in Figure 14, and the processes of Figures 1-13 may be implemented as software, such as one or more application programs executable within computing device 1000. In particular, the steps of methods 10, 100, 200, 300, 400, 500, and 600 are affected by instructions in the software executed within computer system 1000. The instructions may be formed as one or more code modules, each for performing one or more specific tasks. The software may also be divided into two separate parts, with a first part and corresponding code modules implementing the described methods and a second part and corresponding code modules managing the user interface between the first part and a user. The software may be stored in a computer-readable medium, including, for example, the storage devices described below. The software is loaded from the computer-readable medium into the computer system 1000 and then executed by the computer system 1000. A computer-readable medium having such software or a computer program recorded thereon is a computer program product. Use of the computer program product in the computer system 1000 preferably provides an advantageous apparatus for quality analysis of the implantation process and predicted service life of an orthopedic implant within an intraoperative environment.

[0369] 14, an exemplary computing device 1000 is illustrated. The exemplary computing device 1000 can include, but is not limited to, one or more central processing units (CPUs) 1001 equipped with one or more processors 1002, a system memory 1003, and a system bus 1004 that couples various system components including the system memory 1003 to the processing unit 1001. The system bus 1004 can be any of several types of bus structures including a memory bus or memory controller, a peripheral bus, and a local bus using any of a variety of bus architectures.

[0370] Computing device 1000 also typically includes computer-readable media, which may include any available media that can be accessed by computing device 1000 and may include both volatile and nonvolatile media, and removable and non-removable media. By way of example, and not limitation, computer-readable media may comprise computer storage media and communication media. Computer storage media includes media implemented in any method or technology for storage of information such as computer-readable instructions, data structures, program modules, or other data. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVDs) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to store desired information and that can be accessed by computing device 1000. Communication media typically embodies computer-readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transport mechanism and includes any information delivery media. By way of example, and not limitation, communication media includes wired media such as a wired network or direct-wired connection, and wireless media such as acoustic, RF, infrared and other wireless media. Combinations of any of the above should also be included within the scope of computer-readable media.

[0371] The system memory 1003 includes computer storage media in the form of volatile and / or nonvolatile memory such as read-only memory (ROM) 1005 and random access memory (RAM) 1006. A basic input / output system 1007 (BIOS), containing the basic routines that help to transfer information between elements within the computing device 1000, such as during start-up, is typically stored in ROM 1005. RAM 1006 typically contains data and / or program modules that are immediately accessible to and / or presently being operated on by the processing unit 1001. By way of example, and not limitation, FIG. 14 illustrates an operating system 1008, other program modules 1009, and program data 1010.

[0372] Computing device 1000 may also include other removable / non-removable, volatile / non-volatile computer storage media. By way of example only, FIG. 14 illustrates a hard disk drive 1011 that reads from and writes to non-removable, non-volatile magnetic media. Other removable / non-removable, volatile / non-volatile computer storage media that may be used with the exemplary computing device include, but are not limited to, magnetic tape cassettes, flash memory cards, digital versatile disks, digital video tape, solid-state RAM, solid-state ROM, etc. Hard disk drive 1011 is typically connected to system bus 1004 through a non-removable memory interface, such as interface 1012.

[0373] The drives and their associated computer storage media, discussed above and illustrated in Figure 14, provide storage of computer-readable instructions, data structures, program modules, and other data for computing device 1000. In Figure 14, for example, hard disk drive 1011 is illustrated as storing operating system 1008, other program modules 1014, and program data 1015. Note that these components can be either the same as or different from operating system 1008, other program modules 1009, and program data 1010. Operating system 1013, other program modules 1014, and program data 1015, which are given different numbers thereto, illustrate that, at a minimum, they are different copies.

[0374] The computing device also includes one or more input / output (I / O) interfaces 1030 connected to the system bus 1004, including an audio-video interface that is coupled to output devices including one or more of a video display 1034 and a loudspeaker 1035. The input / output interface 1030 is also coupled to one or more input devices including, for example, a mouse 1031, a keyboard 1032, or a touch-sensitive device 1033, such as, for example, a smartphone or tablet device.

[0375] In connection with the description that follows, computing device 1000 may operate in a networked environment using logical connections to one or more remote computers. For convenience of illustration, computing device 1000 is shown in FIG. 14 as connected to network 1020, which is not limited to any particular network or networking protocol, but may include, for example, Ethernet, Bluetooth, or IEEE 802.X wireless protocol. The logical connection depicted in FIG. 14 is a general network connection 1021, which may be a local area network (LAN), a wide area network (WAN), or other network, such as the Internet. Computing device 1000 is connected to general network connection 1021 through a network interface or adapter 1022, which is in turn connected to system bus 1004. In a networked environment, program modules depicted relative to computing device 1000, or portions or peripherals thereof, may be stored in memory of one or more other computing devices communicatively coupled to computing device 1000 through general network connection 1021. It will be appreciated that the network connections shown are exemplary and other means of establishing a communications link between computing devices may be used.

[0376] (interpretation) (bus) In the context of this document, the term "bus" and its derivatives refer to parallel connectivity such as Industry Standard Architecture (ISA), conventional Peripheral Component Interconnect (PCI), or PCI. Although described in the preferred embodiment as a communications bus subsystem for interconnecting various devices, including those using serial connectivity such as PCI Express (PCIe), Serial Advanced Technology Attachment (Serial ATA), etc., it should be broadly construed herein as any system for communicating data.

[0377] (according to ~) As explained herein, "according to" can also mean "as a function of," and is not necessarily limited to an integer defined in relation thereto.

[0378] (composite item) As described herein, a "computer-implemented method" should not necessarily be inferred as being performed by a single computing device, as the steps of the method may be performed by two or more cooperating computing devices.

[0379] Similarly, objects such as "web server," "server," "client computing device," "computer-readable medium," etc., as used herein should not necessarily be construed as a single object, but may be implemented as two or more objects working together, e.g., a web server may be construed as two or more web servers in a server farm working together to achieve a desired goal, or the computer-readable medium may be distributed in a hybrid manner, e.g., program code is provided on a compact disc that can be activated by a license key downloadable from a computer network.

[0380] (Database) In the context of this document, the terms "data source" and "database" are interchangeable, and derivatives of these terms may be used to describe a single database, a set of databases, a system of databases, etc. A system of databases may comprise a set of databases, which may be stored on a single implementation or may span multiple implementations. The term "database" is not limited to referring to a database format, but rather may refer to any database format. For example, database formats may include MySQL, MySQLi, XML, etc.

[0381] (process) Unless specifically stated otherwise, and as will be apparent from the discussion that follows, throughout the specification, discussions utilizing terms such as "processing," "computing," "calculating," "determining," "analyzing," etc., will be understood to refer to the acts and / or processes of a computer or computing system or similar electronic computing device that manipulates and / or transforms data represented as physical quantities, such as electronic quantities, into other data similarly represented as physical quantities.

[0382] (Processor) Similarly, the term "processor" may refer to any device or portion of a device that processes electronic data from, for example, registers and / or memory, and transforms the electronic data into other electronic data that may be stored, for example, in registers and / or memory. A "computer" or "computing device" or "computing machine" or "computing platform" may include one or more processors.

[0383] The methodologies described herein, in one embodiment, can be implemented by one or more processors receiving computer-readable (also called machine-readable) code including a set of instructions, which, when executed by one or more of the processors, performs at least one of the methods described herein. Any processor capable of executing (sequentially or otherwise) a set of instructions that define operations to be performed is included. Thus, one example is a typical processing system including one or more processors. The processing system may further include a memory subsystem including main RAM and / or static RAM and / or ROM.

[0384] (Computer-readable medium) Furthermore, the computer-readable carrier medium may form or be included in a computer program product, which may be stored on a computer-usable carrier medium and which comprises computer-readable program means for causing a processor to perform the methods as described herein.

[0385] (Networked or multiple processors) In alternative embodiments, one or more processors may operate as stand-alone devices or may be connected, e.g., networked, to other processors in a networked deployment; one or more processors may operate in the capacity of a server or client machine in a server-client network environment, or as a peer machine in a peer-to-peer or distributed network environment. One or more processors may form a web appliance, a network router, switch or bridge, or any machine capable of executing (sequentially or otherwise) a set of instructions that specify actions to be performed by that machine.

[0386] Note that while some diagrams show only a single processor and a single memory carrying computer-readable code, those skilled in the art will understand that many of the components described above are included but not explicitly shown or described so as not to obscure aspects of the invention. For example, while only a single machine is illustrated, the term "machine" should be taken to include any collection of machines that, individually or collectively, execute a set (or sets) of instructions to implement any one or more of the methodologies discussed herein.

[0387] (implementation) It should be understood that the steps of the methods discussed are, in one embodiment, performed by a suitable processor (or processors) of a processing (i.e., computer) system executing instructions (computer-readable code) stored in a storage device. It should also be understood that the present invention is not limited to any particular implementation or programming technique, and that the present invention may be implemented using any suitable technique for implementing the functionality described herein. The present invention is not limited to any particular programming language or operating system.

[0388] (Method or means of performing a function) Furthermore, some of the embodiments are described herein as methods or combinations of elements of methods that may be implemented by a processor or processor device, computer system, or other means for performing a function. Thus, a processor with the necessary instructions for performing such a method or element of a method forms a means for performing the method or element of a method. Furthermore, elements described herein of apparatus embodiments are examples of means for performing the functions performed by the elements for purposes of performing the invention.

[0389] (Embodiment) Throughout this specification, references to "one embodiment" or "an embodiment" mean that a particular feature, structure, or characteristic described in connection with an embodiment is included in at least one embodiment of the invention. Thus, the appearances of the phrases "in one embodiment" or "in an embodiment" in various places throughout this specification do not necessarily refer to all the same embodiments, although they may. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments, as would be apparent to one of ordinary skill in the art from this disclosure.

[0390] Similarly, in the above description of exemplary embodiments of the invention, it should be understood that various features of the invention are sometimes grouped together in a single embodiment / arrangement, figure, or description thereof for the purpose of simplifying the disclosure and aiding in understanding one or more of the various aspects of the invention. This method of disclosure, however, should not be interpreted as reflecting an intention that the claimed invention requires more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive aspects lie in less than all features of a single foregoing disclosed embodiment. Accordingly, the claims following the Detailed Description are expressly incorporated into this Detailed Description, with each claim standing on its own as a separate embodiment / arrangement of the invention. Furthermore, while some embodiments described herein include some features (but not others) that are included in other embodiments, as would be understood by one of ordinary skill in the art, combinations of features from different embodiments are also intended to be within the scope of the invention and form different embodiments / arrangements. For example, in the following claims, any of the claimed embodiments can be used in any combination.

[0391] Additional Embodiments Accordingly, one embodiment of each of the methods described herein is in the form of a computer-readable carrier medium carrying a set of instructions, e.g., a computer program, for execution on one or more processors. Accordingly, as will be appreciated by those skilled in the art, embodiments of the present invention may be embodied as a method, an apparatus, such as a special purpose device, an apparatus, such as a data processing system, or a computer-readable carrier medium. The computer-readable carrier medium carries computer-readable code, which, when executed on one or more processors, causes the processor or processors to implement the method. Thus, aspects of the present invention may take the form of a method, an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a carrier medium (e.g., a computer program product on a computer-readable storage medium) carrying computer-readable program code embodied in the medium.

[0392] (specific details) In the description provided herein, numerous specific details are set forth. However, it should be understood that embodiments of the present invention may be practiced without these specific details. In other instances, well-known methods, structures, and techniques have not been shown in detail in order not to obscure an understanding of this description.

[0393] (technical term) In describing the detailed description of the invention illustrated in the drawings, specific terminology will be relied upon for purposes of clarity. However, it is understood that the present invention is not intended to be limited to the specific terminology so selected, and that each specific term includes all technical equivalents, which operate in a similar manner to accomplish a similar technical purpose. Terms such as "forward," "rearward," "radially," "circumferentially," "upward," "downward," etc., are used as terms of convenience to provide points of reference and should not be construed as limiting terms.

[0394] (Different instances of objects) As used herein, unless otherwise specified, the use of ordinal adjectives "first," "second," "third," etc. to describe a common object merely indicates that different instances of a similar object are being referred to and is not intended to imply that the objects so described must exist in a given sequence, either in time, space, ranking, or in any other manner.

[0395] (Scope of the invention) Thus, while what are considered to be preferred arrangements of the invention have been described, those skilled in the art will recognize that other and further modifications may be made therein without departing from the spirit of the invention, and it is intended to claim all such changes and modifications as fall within the scope of the invention. Functionality may be added or deleted from the block diagrams, operations may be interchanged between functional blocks, and steps may be added or deleted to methods described within the scope of the invention.

[0396] Although the present invention has been described with reference to specific examples, it will be appreciated by those skilled in the art that the present invention can be embodied in many other forms.

[0397] (Industrial Applicability) From the above, it is apparent that the arrangements described are applicable to the mobile device industry, particularly for methods and systems for distributing digital media via mobile devices.

[0398] It should be appreciated that the method / apparatus / device / system at least substantially as described / illustrated above provides a method and system for quality analysis of the implantation process and predicted useful life of an orthopedic implant within an intraoperative environment.

[0399] The systems and methods described herein and / or shown in the drawings are offered by way of example only and are not limitations on the scope of the invention. Unless specifically stated otherwise, individual aspects and components of the systems and methods may be modified or substituted therefor with known equivalents or as yet unknown substitutes that may be developed in the future or that may be found to be acceptable substitutes in the future. The systems and methods may also be modified for various applications while remaining within the scope and spirit of the claimed invention, because the range of potential applications is vast, and because the systems and methods are intended to be adaptable to many such variations.

Claims

1. 1. A method of operation of a system for intraoperative implant fit analysis and life expectancy prediction for a prosthetic implant to be integrated with a patient's physiological tissue, said method comprising: a processor of the system collecting data during a surgical procedure via a plurality of sensors and a plurality of data sources mounted in proximity to tissue and implants, the plurality of sensors being contained within a surgical environment in which the surgical procedure is performed; the processor correcting the collected data during the surgical procedure to remove data dimensions; the processor determining tissue condition, tissue morphology, implant condition, and implant morphology based on the collected and modified data during the surgical procedure; generating compatibility information indicating a simulated degree of compatibility between the tissue and the implant based on the tissue condition, the tissue morphology, the implant condition, and the implant morphology during the surgical procedure; processing, by the processor, the compatibility information during the surgical procedure into a format adapted for evaluation against a predetermined comparator; generating and providing, via a display device, a visualization depicting the simulated fit during the surgical procedure, the visualization being generated based on data collected via the plurality of sensors; generating and providing a set of corrective actions during the surgical procedure to modify the tissue condition and morphology for improved post-operative implant performance and lifespan; receiving, by the processor, additional data during the surgical procedure indicative of a physical fit between the tissue and the implant; the processor comparing the simulated fit depicted by the visualization to (i) the physical fit and (ii) the set of corrective actions during the surgical procedure; modifying the set of corrective actions during the surgical procedure in response to comparing the simulated goodness of fit; the processor configuring the display device to provide the modified set of corrective actions during the surgical procedure; A method of operation comprising:

2. The method of claim 1 , wherein the tissue comprises biological tissue.

3. The method of claim 1 , wherein the prosthetic implant comprises a knee prosthesis or a hip prosthesis.

4. 10. The method of claim 1, wherein the prosthetic implant comprises one or more features, the one or more features comprising kerfs or patterns on one or more surfaces to promote at least one of bone integration or to increase rigidity of fixation to the tissue.

5. The method of claim 1 , wherein the sensor comprises at least one sensor, the at least one sensor individually configured to monitor, sense, collect, and provide data of interest.

6. The method of claim 5 , wherein the object includes one or more of the tissue, the implant, a connection interface between the tissue and the implant, a surrounding environment, or a result of a treatment or interaction.

7. 10. The method of claim 1, wherein at least one of the sensors requires external intervention, the external intervention including at least one of a change in sensor position, angle, proximity, proximity, configuration, illumination, or timing.

8. The method of claim 1 , wherein the tissue condition includes at least one of composition, hydration, density, necrosis, discoloration, reflectivity, or temperature.

9. The method of claim 1 , wherein the implant condition includes at least one of composition, degradation, density, or particle dissolution.

10. The method of claim 1 , wherein the tissue morphology and the implant morphology each include one or more of shape, flatness, parallelism, roughness, waviness, peak distribution, porosity, or stiffness.

11. The method further includes, prior to determining the tissue condition, the implant condition, the tissue morphology, and the implant morphology, processing the collected data by the processor, the processing the collected data by the processor comprising: said processor removing any noisy, erroneous or redundant data from said collected data; the processor formats the collected data, flattens the collected data, or extracts the collected data from a storage device; the processor sampling the collected data; said processor scaling or aligning said collected data so that its values ​​are within comparable ranges; the processor decomposing or deconvolving the collected data so that representative or particular features or portions of the collected data can be divided into component elements; or said processor aggregating said collected data such that individual features, components, segments, or portions of said collected data may be combined into a single entity. The method of claim 1 , comprising at least one of:

12. The method of claim 11 , wherein the processor's determining the tissue condition, the tissue morphology, the implant condition, and the implant morphology comprises interpretation of the processed collected data.

13. The method of claim 12 , wherein the processor comprises executing a machine learning, data science, or mathematical algorithm or method based on the processed collected data.

14. The method of claim 12 , wherein the processor's interpretation of the processed collected data is performed by at least one of a sensor controller or a network bridge.

15. 13. The method of claim 12, wherein the processor's interpretation of the processed collected data includes any observational input provided by verified personnel.

16. generating the compatibility information based on data interpreted from the tissue having a receiving surface, an associated implant having an engagement surface, and an interface between the receiving surface and the engagement surface; The operating method includes: the processor generating a degree of compatibility of the interface with one or both of the receiving surface and the engaging surface; analyzing the effect of implant insertion or fixation; the processor assessing a fit between the implant and the tissue during the surgical procedure; the processor predicting the useful life and post-operative implant performance of the implant; The method of claim 1 , comprising:

17. 17. The method of claim 16, wherein the processor generating the degree of compatibility comprises comparing the tissue condition to the implant condition and comparing the tissue morphology to the implant morphology.

18. 18. The method of claim 17, wherein the processor comparing the tissue condition to the implant condition includes the processor measuring a degree of compatibility between the tissue condition and the implant condition.

19. 18. The method of claim 17, wherein the processor comparing the tissue morphology to the implant morphology includes the processor measuring a degree of compatibility between the tissue morphology and the implant morphology.

20. 20. The method of claim 19, wherein the processor measuring the degree of compatibility between the tissue morphology and the implant morphology includes the processor measuring whether the shape and form of the tissue allows the implant to be inserted.

21. 20. The method of claim 19, wherein the processor measuring the degree of compatibility between the tissue morphology and the implant morphology includes the processor measuring the degree of contact the implant makes with the tissue when inserted.

22. 2. The method of claim 1, wherein the processor generating a means for predicting post-operative implant performance comprises the processor training a machine learning, data science, or mathematical model to provide the performance prediction.

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