Preoperative evaluation and inventory management system

Abstract

A system for generating a preoperative assessment of a patient comprises an assessment system configured to communicate with a client electronic device.Described system receives patient information about the patient, uses described patient information to access one or more medical images associated with described patient, described one or more medical images are performed one or more image processing techniques to identify the measured value about described patient's internal body structure, identifies one or more implant assemblies for described patient, determines whether described one or more implant assemblies are currently in stock, in response to determining that described one or more implant assemblies are currently not in stock, automatically generates an order and places an order for described one or more implant assemblies that are not in stock, generates a preoperative assessment for described patient, and makes described assessment be presented on the display device of client electronic device.

Description

Technical Field

[0001] The present disclosure generally relates to a system that utilizes information from various sources to provide a pre-operative assessment of a patient. Background Art

[0002] Currently, the process of evaluating a patient preoperatively can be tedious and time-consuming because the resources needed for such an evaluation are often disjointed, incomplete, or difficult to access or interpret. Summary of the invention

[0003] In an embodiment, a system for generating a preoperative assessment of a patient comprises an assessment system configured to communicate with one or more client electronic devices via a communication network. The assessment system comprises one or more computing devices and a computer-readable storage medium. The computer-readable storage medium comprises one or more programming instructions, the programming instructions make the one or more computing devices receive patient information about the patient when being executed, use at least a portion of the patient information to access one or more medical images associated with the patient, one or more image processing techniques are performed to the one or more medical images to identify one or more measured values ​​of the internal body structure of the patient from the one or more medical images, one or more implant assemblies for the patient are identified based on the one or more measured values, determine whether the one or more implant assemblies are currently in stock, in response to determining that the one or more implant assemblies are currently not in stock, automatically generate an order and place an order for the one or more implant assemblies that are not in stock, and generate a preoperative assessment for the patient, wherein the preoperative assessment comprises: the indication of the one or more implant assemblies, and for each of the one or more implant assemblies, whether the implant assembly currently has an indication in stock. The system makes the assessment be displayed on the display device of the client electronic device.

[0004] In an embodiment, the system may receive information about the patient's movement obtained from one or more sensors located on a portion of the patient's body. The system may receive patient information from a client electronic device. Optionally, the system may receive one or more medical images from one or more of: electronic medical record data; or electronic health record data.

[0005] The system can perform one or more image processing techniques on the one or more medical images to identify a condition associated with the patient by causing the one or more computing devices to apply a machine learning model that has been trained on a set of body structures.

[0006] Described system can identify one or more implant assemblies for described patient by accessing component data storage area, wherein said component data storage area comprises the size information about one or more implant assemblies, in described component data storage area, search for one or more implant assemblies with the size information corresponding to described one or more measured values, and identify the unique identifier that is associated with each implant assembly with the size information corresponding to described one or more measured values.

[0007] In an embodiment, described system can make described one or more computing devices determine whether described one or more implant assemblies are currently in stock by searching one or more in described unique identifier in an inventory data storage and determining the inventory status that is associated with described one or more unique identifiers. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] Figure 1 An example evaluation system according to an embodiment is depicted.

[0009] Figure 2 Depicted is an example method of generating a surgical assessment in accordance with an embodiment.

[0010] Figure 3 and Figure 4 Depicted is an example graphical user interface that a healthcare provider may use to provide patient information in accordance with various embodiments.

[0011] Figure 5 Depicted is a block diagram of an example of internal hardware that may be used to contain or implement program instructions according to an embodiment. DETAILED DESCRIPTION

[0012] In some embodiments, as used in the specification and including the appended claims, the singular forms "a, an" and "the" include plural, and references to specific numerical values ​​include at least the specific value, unless the context clearly indicates otherwise. The range may be expressed herein as from "about" or "approximately" a specific value and / or to "about" or "approximately" another specific value. When such a range is expressed, another embodiment includes from the specific value and / or to the specific value. Similarly, when the antecedent "about" is used to express a value as an approximation, it should be understood that the specific value forms another embodiment. It should also be understood that all spatial references, such as horizontal, vertical, top, upper, lower, bottom, left and right, are only for illustrative purposes and can be varied within the scope of the present disclosure. For example, references to "upper" and "lower" are relative and are used only in other contexts, and are not necessarily "superior" and "inferior". Typically, similar spatial references of different aspects or components indicate similar spatial orientation and / or positioning, that is, each "first end" is located at or points to the same end of the device. Furthermore, the various spatial terms used herein should not be construed as limiting the various insertion techniques or orientation of the implant relative to the location in the spine.

[0013] For the purposes of this application, the following terms shall have their respective meanings:

[0014] A "computing device," "electronic device," or "computer" refers to a device or system that includes a processor and memory. Each device may have its own processor and / or memory, or the processor and / or memory may be shared with other devices, such as in a virtual machine or container arrangement. The memory will contain or receive programming instructions that, when executed by the processor, cause the electronic device to perform one or more operations in accordance with the programming instructions. Examples of electronic devices include personal computers, servers, mainframes, virtual machines, containers, mobile electronic devices such as smart phones, wearables connected to the Internet, tablet computers, laptops, and appliances and other devices capable of communicating in an Internet of Things arrangement. In a client-server configuration, the client device and the server are electronic devices, where the server contains instructions and / or data that the client device accesses via one or more communication links in one or more communication networks. In a virtual machine arrangement, the server may be an electronic device, and each virtual machine or container may also be considered an electronic device. In the following discussion, for simplicity, a client device, a server device, a virtual machine, or a container may be referred to simply as a "device." This will be discussed below. Figure 5 Additional elements that may be included in an electronic device are discussed in the context of.

[0015] The terms "memory," "computer-readable medium," and "data storage" each refer to a non-transitory device on which computer-readable data, programming instructions, or both are stored. Unless the context clearly indicates that a single device or multiple devices are required, the terms "memory," "computer-readable medium," and "data storage" include single and multiple embodiments, as well as portions of such devices such as memory sectors.

[0016] Figure 1 An example evaluation system according to an embodiment is shown. Figure 1 As shown, the assessment system 100 may include a client system 102, a surgeon system 104, an analysis system 106, a hospital record system 108, and / or an operating room system 110. The client system 102 may include one or more client electronic devices 114a-N. In an embodiment, the client electronic device 114a-N may be an electronic device for receiving input associated with a patient. The client electronic device 114a-N may belong to the patient. For example, the client electronic device 114a-N may be a smart phone, a tablet, a mobile device, or a computer belonging to the patient. Alternatively, the client electronic device 114a-N may be an electronic device used by the patient or other person to provide input related to the patient and the patient's condition. For example, a patient in a clinical environment may be allowed to use a tablet to provide answers or feedback. As another example, a clinician may use the client electronic device 114a.-N to provide such input.

[0017] In an embodiment, the client system 102 may include one or more sensors 116a.-N. Sensors 116a.-N may be used to collect information about a patient. Example sensors include, but are not limited to, inertial measurement units (IMUs), electrocardiogram (ECG) sensors, electromyogram (EMG) sensors, accelerometers, altimeters, thermometers, microphones, cameras, barometers, and / or the like. In an embodiment, the sensor may be at least a portion of an implant, such as a spinal implant. For example, a load sensing assembly for a spinal implant may communicate with an electronic device (such as a reader device) using near field communication or via other communication means. The reader device may obtain information from the implant through this communication channel. Such devices may include, for example, a system disclosed in U.S. Patent Application No. 161039,592, entitled "Load Sensing Assembly for a Spinal Implant", which is incorporated herein by reference.

[0018] The surgeon system 104 may include one or more provider electronic devices 118a.-N. The provider electronic devices 118a.-N may include, for example, a smartphone, a tablet, a mobile device, a laptop computer, a desktop computer, and / or the like. In an embodiment, a surgeon or other healthcare provider may use the provider electronic devices 118a-N to access and view patient information.

[0019] In an embodiment, the analysis system 106 may include one or more electronic devices 120a.-N and / or data storage 122a-N. One or more electronic devices may be servers. The analysis system 106 may store patient information and sensor information received from the client system 102. As explained in more detail below, the analysis system 106 may analyze or process such information to develop, update, or revise a pre-operation plan or an operation recommendation.

[0020] One or more data stores 122a-N can store diagnostic information, as discussed in more detail below. One or more data stores 122a.-N can store inventory information. Inventory information refers to information about the current status of the inventory of medical supplies required to perform one or more medical procedures. For example, a spinal implant surgery may require a certain number of spinal implants, and each spinal implant may have one or more components, such as intervertebral implants, plates, pedicle screws and / or anchoring components, rods, etc. The inventory data stores 122a-N can store information about the current inventory of these components, such as part numbers, model numbers, the number of available components, setting numbers or configuration numbers, indications of alternative components, reordering status, and / or similar information.

[0021] like Figure 1 As shown, the hospital record system 108 may include one or more hospital electronic devices 124a-N and one or more data stores 126a-N. The data stores 126a-N may be used to store medical record information of one or more patients. The medical record information may include, but is not limited to, electronic medical records (EMRs) and / or electronic health records (EHRs). The information may include images associated with the patient, such as X-ray images, CAT scans or CT images, radiographs, MRIs, and / or similar images.

[0022] In embodiments, the operating room system 110 may include one or more operating room electronic devices 128a-N. The operating room electronic devices 128a-N may be electronic devices present in the operating room, such as tablet computers or laptop computers. The operating room system 110 may also include one or more equipment items 130. The equipment items 130 may be configured to communicate with one or more operating room electronic devices 128a-N and / or other electronic devices, for example, via a wireless communication channel. Example devices 30 may include, but are not limited to, monitoring devices, surgical navigation systems (e.g., Medtronic StealthStation), robotic systems (e.g., Mazor X robotic guidance systems), surgical equipment, and / or the like. For example, embodiments of the system may integrate data obtained in the client system 102 and / or surgeon system 104 with preoperative planning software in the Mazor X system software to provide the Mazor system with information obtained by embodiments of the system to plan, revise, adjust, or otherwise change and then perform a surgical procedure.

[0023] like Figure 1 As shown, client system 102, surgeon system 104, analysis system 106, hospital record system 108, and / or operating room system 110 may communicate with one or more other systems via communication networks 112a-N. Communication networks 112a-N may be local area networks (LANs), wide area networks (WANs), mobile or cellular communication networks, extranets, intranets, the Internet, short-range communication networks, and / or the like. Although Figure 1 Separate communication networks 112a-N are shown, but it should be understood that these networks or some combination of these networks may be implemented as a single communication network.

[0024] Figure 2 An example method of generating a surgical assessment according to an embodiment is depicted. Figure 2 As illustrated, patient information may be obtained by the analysis system 200. The analysis system may obtain 200 patient information from one or more client electronic devices.

[0025] Patient information may include information provided directly by the patient. Patient information may include personal information about the patient, such as name, address, gender, contact information, and / or the like. Patient information may also include information about the patient's condition. For example, a patient may be evaluated for spinal surgery so that the patient can provide information about the condition of the patient's back, pain levels, treatments, and / or the like. Patient information may be provided by the patient or with the patient's consent.

[0026] In an embodiment, the patient may provide the patient information via a client electronic device. The patient may use his or her own client electronic device to provide the patient information. For example, the patient may provide the patient information via an application resident on or accessible via the patient's client electronic device (e.g., a mobile phone or tablet). Alternatively, the patient may provide the patient information via a website accessible via a laptop, desktop computer, or other electronic device.

[0027] In an embodiment, a patient may provide patient information using a client electronic device that is not associated with or owned by the patient. For example, an electronic device for inputting patient information may be provided to a patient being evaluated in a clinical setting. In yet another example, a third party, such as a healthcare provider, may input patient information on behalf of the patient via a client electronic device. For example, a surgeon or other healthcare provider may provide information about a patient's condition, such as the results of a physical examination. Figure 3 and Figure 4 Depicted is an example graphical user interface that a healthcare provider may use to provide patient information in accordance with various embodiments.

[0028] Patient information can include information about the patient collected by one or more sensors. The sensor can be located on or near the patient's area of ​​interest. For example, the patient can wear a patch or other device on a part of the patient's lower back with one or more sensors. These sensors can measure certain values ​​associated with the patient's movement, for example, translation (for example, pitch, roll and / or sideways) around the x-axis, y-axis or z-axis, angular velocity, gravity, pressure, acceleration, swivel and / or rotational orientation, direction of motion, ups and downs, surge, swing, position and / or the like. In this regard, the sensor system disclosed in U.S. Patent Application No. 16 / 039,592, entitled "Load Sensing Assembly for a Spinal Implant", is hereby incorporated by reference in its entirety.

[0029] As another example, one or more sensors can be located inside. For example, one or more sensors can be incorporated into an implant, such as a spinal implant. The sensor can obtain one or more measurements, and one or more measurements are sent to an electronic device located outside the patient via a wireless communication network. For example, the load sensing assembly of a spinal implant can obtain one or more measurements of the force between the fixing screw and the longitudinal component of the implant when the fixing screw engages with the anchoring component of the implant. Alternative sensors can be used within the scope of the present disclosure. In this regard, the sensor system disclosed in U.S. Patent Application 16 / 039,592, entitled "Load Sensing Assembly for a Spinal Implant", is hereby incorporated by reference in its entirety.

[0030] In an embodiment, one or more client electronic devices may send at least a portion of the patient information to the analysis system.

[0031] Return to reference Figure 2 , the analysis system may receive 202 patient medical information. The patient medical information may include information from a patient medical record or health record, such as electronic medical record (EMR) data or electronic health record (EHR) data. In some embodiments, the patient medical information may include one or more medical images of at least a portion of the patient's body. The medical image may be a CT scan image, an X-ray image, an MRI image, and / or the like.

[0032] In various embodiments, the analysis system may receive 202 patient medical information from a hospital record system. Alternatively, the client system may receive the patient medical information from the hospital record system and may provide at least a portion of the patient medical information to the analysis system.

[0033] The analysis system can perform one or more image processing techniques on the medical image to calculate one or more related measurements from the image, such as the size constraints (e.g., length, width, diameter, height, volume, angle) of a screw, rod, plate, or intervertebral implant and / or the potential positioning or position constraints or optimization of a single implant or multiple implants that operate in coordination and / or are used together to correct one or more conditions. Sagittal mobility can be further evaluated using images and related data obtained by various embodiments of the system. In addition, lordosis angle and disc height can be obtained through various images and data that can be obtained in certain embodiments.

[0034] The analysis system may store the diagnostic information in one or more data stores of the analysis system, or in one or more data stores accessible to the analysis system. Diagnostic information refers to information about one or more medical conditions and / or treatments for one or more medical conditions. In embodiments, the diagnostic information may include medical journals, articles, research reports, and / or the like.

[0035] In various embodiments, diagnostic information may be classified, labeled, or marked according to the medical condition related to such diagnostic information. For example, a research study on spinal implant efficacy may be classified, labeled, or marked as being related to a "spinal condition." In some embodiments, diagnostic information may relate to more than one medical condition.

[0036] Return to reference Figure 2 , the analysis system may identify 204 at least a portion of the relevant diagnostic information. The analysis system may determine a type of medical condition associated with the patient from at least a portion of the received patient information and / or patient medical information. For example, the analysis system may parse the received patient information and / or at least a portion of the received patient medical information for one or more keywords corresponding to one or more of the categories, labels, or tags associated with the diagnostic information. The analysis system may use the analyzed information to identify 204 at least a portion of the relevant diagnostic information.

[0037] In an embodiment, the analysis system may generate 206 a patient assessment based on at least a portion of the patient information, patient medical information, and / or related diagnostic information. For example, the analysis system may analyze one or more medical images or portions thereof to determine information related to the patient's medical condition.

[0038] In various embodiments, one or more machine learning techniques or models (e.g., support vector machine learning models, logistic regression, or neural networks) may be used to generate the assessment. The machine learning model may be trained on a data set for a desired body structure, pathology, condition, classification, and / or the like.

[0039] For example, a machine learning model may be trained on a dataset for a particular pathology. The dataset may include images indicative of the pathology and / or images not indicative of the pathology. Similarly, a dataset may be trained on a dataset for a particular condition (e.g., a fracture, the presence of a tumor, the presence of a deformity, the presence of trauma, and / or the like). The dataset may include one or more images indicative of the condition and / or one or more images not indicative of the condition.

[0040] For example, a machine learning model can be used to provide a probability that a patient has stenosis. The model can be trained on annotated images depicting stenosis in other individuals. The model can also be trained on annotated images of other conditions that are not stenosis, such as spondylolisthesis and herniated discs. The annotations can correspond to dimensions, characteristics, or features of various components, such as foramen height, medullary deformation, lateral concavity volume, and / or the like. Through such training, the model can learn the features in the annotated images.

[0041] When the model is applied to an image, it can generate a probability that the patient exhibits a pathology or condition defined by the model. The model can be updated based on feedback received from users of the system. For example, if the model predicts that a patient is unlikely to have a particular condition, but the patient is diagnosed with the condition by a doctor, the model can be updated with that information to further improve the model.

[0042] In some embodiments, the analysis system may use pattern matching to determine whether similarity exists between at least a portion of a medical image of the patient and at least a portion of one or more images in a data set. For example, the analysis system may compare an image of a femur of the patient to an image of a broken femur to determine whether similarity exists. If pixel-level analysis of the images results in a match within a certain degree of certainty or within a certain standard deviation, it may be determined that similarity exists. If similarity exists, the analysis system may generate an assessment that includes an indication that the medical image of the patient exhibits features consistent with features associated with a broken femur.

[0043] Similarly, the machine learning technique can compare the image of the patient's femur to the image of the unfractured femur in the data set to determine the presence of similarity. If similarity exists, the analysis system can generate an assessment including an indication that the medical image of the patient exhibits features consistent with features associated with the unfractured femur.

[0044] In various embodiments, the analysis system may retrieve medical images of a particular condition from a data store that is part of the analysis system or other data store that is accessible to the analysis system (eg, via a communication channel).

[0045] The acquired medical images can be converted into a format compatible with machine learning techniques, and the techniques can be applied to the image representation.

[0046] In other embodiments, the analysis system may analyze one or more medical images to determine a condition classification. A classification may refer to a specific type of medical condition. For example, a spinal condition may be associated with a variety of classifications, such as a lumbar degeneration classification, an anterior cervical classification, a TL complex degeneration classification, a cervical complex degeneration classification, an adult deformity classification, a pediatric deformity classification, a tumor classification, and / or a trauma classification. Additional and / or alternative classifications may be used within the scope of the present disclosure.

[0047] An analysis system may determine a condition classification by applying a machine learning model that compares at least a portion of one or more of the received medical images of a patient to at least a portion of one or more images associated with a known classification or a known lack of classification.

[0048] For example, the analysis system may want to determine whether a patient has experienced spinal trauma. The analysis system may apply a machine learning model that compares at least a portion of a medical image of the patient to at least a portion of a retrieved image to determine whether similarities exist. In some embodiments, the machine learning model may use pattern matching to determine whether similarities exist between at least a portion of a medical image of the patient and at least a portion of a classification-specific medical image.

[0049] In various embodiments, the analysis system can analyze one or more medical images to determine one or more dimensions associated with a specific body structure. The dimensions can be related to one or more implants needed or recommended for treating a patient's condition. For example, a spinal implant such as a screw assembly and a spinal rod (or other similar type of implant) can be recommended to treat a patient's spinal condition. A spinal rod can be attached to the outside of two or more spinal components to help treat spinal abnormalities. The screw assembly can be used to connect a longitudinal component to a spinal component. The analysis system can analyze one or more medical images of at least a portion of the patient's spine to determine one or more dimensions of one or more spinal components of the patient, such as the distance between the spinal components. The analysis system can use one or more of the determined dimensions to recommend one or more implant options, as described in more detail below.

[0050] In an embodiment, the type of analysis that the analysis system can perform can be based on input received from a user of the system (e.g., a healthcare provider).For example, a surgeon can provide input indicating that the analysis system should analyze one or more medical images to determine one or more specific dimensions of a body structure.

[0051] The assessment may include at least a portion of the patient information, the patient medical information, and / or any relevant medical images. The assessment may also include information about the analysis performed by the analysis system. For example, if the analysis system analyzed one or more medical images to determine whether a particular condition exists, the assessment may include an indication of whether the system determined the condition exists. As another example, if the analysis system analyzed one or more medical images to determine a classification associated with the condition, the assessment may include an indication of the classification determined by the system. As yet another example, if the analysis system analyzed one or more medical images to determine one or more dimensions of a portion of interest of the patient, the assessment may include the one or more dimensions.

[0052] In various embodiments, the assessment may include a diagnosis of the patient's medical condition, potential complications, and / or an indication of whether surgery is recommended for the patient. However, in such cases, the recommendation may preferably be confirmed by a licensed medical professional before execution.

[0053] In various embodiments, if analytical system has obtained one or more sizes in the zone about patient's health, then analytical system can use one or more in the described size to determine one or more medical supplies for program.For example, analytical system can determine one or more implants or the implant assembly that can be selected for the patient based on the size of described acquisition.

[0054] In embodiments, the analysis system may cross-reference at least a portion of the obtained dimensions with information from a component data store. The component data store may store information about one or more medical supplies, such as length, width, diameter, trajectory, weight, volume, composition, and / or other dimensional information of one or more supplies or components of one or more supplies. In various embodiments, the component data store may also store a unique identifier associated with the one or more supplies. The component data store may be implemented as part of an inventory data store, as described below, or as a separate data store.

[0055] In various embodiments, the analysis system can compare at least a portion of the obtained dimensions with one or more dimensions included in the component data storage area to identify one or more medical supplies that can be used. For example, the analysis system can determine that the patient needs a spinal rod with a length of x cm based on the distance between the two spinal components of the patient. The analysis system can search the component data storage for a spinal implant with a spinal rod component with a length of x cm, and include an indication of such a spinal implant in the assessment of the patient. Additional and / or alternative components, supplies, measurements, and dimensions can be used within the scope of the present disclosure.

[0056] In an embodiment, the evaluation system can analyze the current inventory to determine whether any medical supplies need to be ordered to perform the procedure. For example, the analysis system may include or have access to one or more data stores that store information about what medical supplies are needed for one or more medical procedures. The information may include a description of the supplies, a unique identifier associated with the supplies, the number of supplies required for the procedure, and / or the like. The analysis system can use the diagnosis from the generated evaluation to identify the specific procedure in question. The analysis system can use the identified procedure to identify one or more supplies required to perform the procedure.

[0057] In an embodiment, the analysis system may cross-reference the identified supplies with an inventory data store. For example, the analysis system may cross-reference the unique identifier obtained from the component data store with the inventory data store to determine whether a particular medical supply or component is in stock. The inventory data store may store inventory information about medical supplies. The information may include, but is not limited to, the quantity of each supply available or in stock, the unique identifier associated with the supply, the location of the supply, and / or similar information.

[0058] If the analysis system determines that all identified supplies are in stock, the analysis system can include that information in the assessment it generates. The analysis system can also include information about the supplies, such as a unique identifier or location. Alternatively, if the analysis system determines that one or more identified supplies are not currently in stock, the analysis system can include in the assessment an indication of which supplies will need to be re-ordered. For example, the analysis system can automatically re-order any such supplies by sending a request to an inventory management system.

[0059] In various embodiments, the generated assessment can include an indication of one or more medical supplies or components that are recommended for the procedure. The assessment can also include an indication of whether a particular supply or component is currently in stock.

[0060] Return to reference Figure 2 , the analysis system can cause at least a portion of the generated assessment to be displayed at one or more client electronic devices and / or surgeon electronic devices 208. For example, a surgeon accessing the patient record can access and view the assessment from the surgeon's electronic device. As another example, the patient can access and view his or her own assessment using his or her own electronic device.

[0061] Figure 5 Example hardware that may be used to contain or implement program instructions is shown. Bus 500 serves as the main information highway that interconnects the other illustrated components of the hardware. CPU 505 is the central processing unit of the system and performs the calculations and logical operations required to execute the program. CPU 505 is either alone or in combination with Figure 5 One or more of the other elements disclosed in the are combined as an example of a processor, as such terms are used in this disclosure. Read-only memory (ROM) and random access memory (RAM) constitute examples of non-transitory computer-readable storage media 520, memory devices, or data storage, as such terms are used in this disclosure.

[0062] Program instructions, software, or interactive modules for providing an interface and performing any query or analysis associated with one or more data sets may be stored in the memory device 520. Optionally, the program instructions may be stored on a tangible, non-transitory computer-readable medium, such as a compact disc, digital disc, flash memory, memory card, USB drive, optical storage media, and / or other recording media.

[0063] Optional display interface 530 may allow information from bus 500 to be displayed in audio, video, graphical or alphanumeric format on display 535. Various communication ports 540 may be used to communicate with external devices. Communication port 540 may connect to a communication network such as the Internet or an intranet.

[0064] The hardware may also include an interface 545 that allows data to be received from an input device such as a keyboard 550 or other input device 555 such as a touch screen, remote control, pointing device, video input device, and / or audio input device.

[0065] It should be understood that the various and other features and functions disclosed above, or their replacements, may be combined into many other various systems or applications or combinations of systems and applications as desired. In addition, those skilled in the art may subsequently make various currently unforeseen or unanticipated substitutions, modifications, changes or improvements, which are also intended to be covered by the appended claims.

Claims

1. A system for generating a preoperative assessment of a patient, the system comprising: An evaluation system configured to communicate with one or more client electronic devices via a communication network, wherein the evaluation system comprises one or more computing devices and a computer-readable storage medium, wherein the computer-readable storage medium comprises one or more programming instructions that, when executed, cause the one or more computing devices to: training a machine learning model on a data set for a spinal condition to produce a trained machine learning model, wherein the data set includes one or more images depicting the spinal condition and one or more images not depicting the spinal condition, receive patient information about the patient, accessing one or more medical images associated with the patient using at least a portion of the patient information, identifying diagnostic information for the patient based at least in part on the patient information, wherein the diagnostic information identifies the spinal condition, identifying a trained machine learning model associated with the spinal condition, applying the trained machine learning model to at least a portion of the one or more medical images associated with the patient to determine that the patient exhibits the spinal condition, identifying one or more medical supplies needed to perform a particular procedure for treating the spinal condition of the patient by accessing one or more data stores that store information regarding what medical supplies are needed for one or more medical procedures, performing one or more image processing techniques on the one or more medical images to identify one or more dimensional constraints about at least a portion of the patient's spine from the one or more medical images, By comparing the dimensional information stored in the identified dimensional constraint with the component data storage area, identifying one or more implant assemblies for treating the described spinal condition of the described patient, Determine whether described one or more medical supplies and described one or more implant assemblies are currently in stock, In response to determining that at least one of the one or more medical supplies and the one or more implant assemblies is not currently in stock, for not having medical supplies or implant assemblies in stock, automatically generate an order and place an order, Generating a preoperative assessment for the patient, wherein the preoperative assessment comprises: The indication of described one or more medical supplies and described one or more implant assemblies, for each in described one or more medical supplies and described one or more implant assemblies, the indication of whether described medical supplies or described implant assembly are currently in stock, and For each in the described one or more medical supplies and described one or more implant assemblies in stock, at least one item in the quantity of the described medical supplies of every type or described implant assembly, the unique identifier that is associated with described medical supplies or described implant assembly, the position of described medical supplies or described implant assembly, and The evaluation is caused to be displayed on a display device of the client electronic device.

2. A system according to claim 1, wherein the one or more programming instructions that, when executed, cause the one or more computing devices to receive patient information include one or more programming instructions that, when executed, cause the one or more computing devices to receive information about the patient's movement obtained from one or more sensors located on a portion of the patient's body.

3. A system according to claim 1, wherein the one or more programming instructions that, when executed, cause the one or more computing devices to receive patient information include one or more programming instructions that, when executed, cause the one or more computing devices to receive patient information from a client electronic device.

4. The system of claim 1 , wherein the one or more programming instructions that, when executed, cause the one or more computing devices to access one or more medical images associated with the patient comprise one or more programming instructions that, when executed, cause the one or more computing devices to receive the one or more medical images from one or more of: Electronic medical record data; or Electronic health record data.

5. The system of claim 1 , wherein the one or more programming instructions that when executed cause the one or more computing devices to perform one or more image processing techniques on the one or more medical images to identify one or more dimensional constraints about at least a portion of the patient's spine from the one or more medical images include one or more programming instructions that when executed cause the one or more computing devices to obtain one or more of: one or more lordotic angles; the height of one or more discs of the patient's spine; One or more dimensions of one or more spinal components of the patient's spine.

6. system according to claim 1, wherein when being executed, make described one or more computing devices identify described one or more programming instructions for described patient's one or more implant assemblies and comprise one or more programming instructions, described one or more programming instructions when being executed make described one or more computing devices: Accessing a component data storage area, wherein said component data storage area comprises size information about one or more implant components; Searching in the component data storage area for one or more implant components with the size information corresponding to the one or more measured values; and Identify the unique identifier that is associated with each implant component with the size information corresponding to the one or more measurements.

7. system according to claim 6, wherein when being executed, make described one or more computing devices determine whether described one or more implant assemblies currently have described one or more programming instructions in stock to comprise one or more programming instructions, described one or more programming instructions when being executed make described one or more computing devices: searching an inventory data store for one or more of the unique identifiers; and An inventory status associated with the one or more unique identifiers is determined.

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