72 results about "Patient specific" patented technology

Systems and methods for designing and implementing patient-specific surgical procedures and / or medical devices are disclosed. In some embodiments, a method includes receiving a patient data set of a patient. The patient data set is compared to a plurality of reference patient data sets, wherein each of the plurality of reference patient data sets is associated with a corresponding reference patient. A subset of the plurality of reference patient data sets is selected based, at least partly, on similarity to the patient data set and treatment outcome of the corresponding reference patient. Based on the selected subset, at least one surgical procedure or medical device design for treating the patient is generated.

A computer implemented method for training a whole medical image foundation model, including: receiving a plurality of medical image datasets; extracting local sections of image data from the plurality of medical image datasets; obtaining one or more causal variables associated with the local sections and / or patient; training one or more self-supervised learning models based on the local sections of image data and the causal variables; combining the one or more trained self-supervised learning models with a deep learning network configured to combine a latent representation of the local sections of image data from the one or more trained self-supervised learning models into a patient-level representation; and combining, with the one or more trained self-supervised learning models and the deep learning network, at least one further network or function configured to accept the patient-level representation as input, the at least one further network or function operable to perform one or more patient-specific prediction tasks.

The invention provides a non-equidistant lattice distribution optimization method based on local dose constraint. The method comprises the following steps: generating an initial lattice based on a patient image and a delineated target region; calculating and storing a dose kernel in advance based on a Monte Carlo algorithm; calling a dose core to carry out rapid dose optimization and calculation; establishing an independent peripheral subspace dose evaluation region and a local dose constraint condition for each lattice target region; establishing a multi-objective function for lattice target region position optimization; dynamically adjusting lattice positions and intervals by using a rapid simulated annealing method or a gradient descent method until target region position distribution and dose distribution meeting a dose multi-objective function and constraint conditions are generated; according to the invention, through dynamic adjustment of the lattice spacing and position and combination of the patient specific anatomical structure, accurate control of the valley dose around each lattice is realized, so that the problems of non-uniform target dose distribution and insufficient valley dose control in traditional equal spacing lattice radiotherapy are solved.

A system and method for planning and simulating a surgical operation to create a patient-specific spinal implant are disclosed. The system comprises a remote server configured to receive patient-specific medical image data and generate a 3D mesh model of the patient's spine using algorithms that separate vertebral bodies, remove artifacts, and smooth surfaces. A doctor's computer receives the 3D mesh model and allows real-time manipulation of intervertebral spaces to achieve a desired spinal curvature. The server generates a spinal implant design with surface-mapped endplates matching the patient's vertebral anatomy, which is transmitted to a 3D printer for manufacturing. The method includes steps of receiving image data, generating and updating the 3D mesh model based on doctor input, generating the final implant design, and transmitting it for production. The invention enables the creation of patient-specific spinal implants with improved conformity and surgical outcomes.

Device for personalized radiotherapy using AI-controlled dosimetry, comprising: - a patient data acquisition unit configured to receive real-time data on patient-specific health information, including medical imaging data from the patient's CT, MRI and PET scans, biometric data from wearable or hospital-grade monitoring devices, electronic health records (EHRs) and previous treatment history; - The medical image processing unit, which analyzes and processes the captured image data, performs various functions, from tumor segmentation and identification of organs at risk to the spatial alignment of multimodal images for radiotherapy planning; - a CPU is configured to preprocess other collected data by (a) normalizing imaging and physiological data, (b) aggregating imaging and physiological data, (c) encoding imaging and physiological data for subsequent analysis, and (d) ensuring the compatibility and consistency of data from different sources; - an artificial intelligence (AI) controlled dosimetry machine that is communicatively connected to the central processing unit, wherein the AI-controlled dosimetry machine is configured to use machine learning algorithms to evaluate patient-specific parameters, optimize the distribution of a radiation dose and predict responses to treatment; - a visualization and treatment planning interface coupled with the AI-driven dosimetry machine, the visualization interface being configured to provide radiation dose distributions, patient-specific treatment recommendations, and comparative dose plans that enable data-driven adjustments by clinicians; and - a visualization interface with a feedback mechanism that allows real-time input from practicing clinicians and continuous automatic recording of patient response, which is used for iterative improvement of both the dosimetry model and the treatment plan.

The invention relates to the technical field of medical image processing, in particular to an enterostomy mucous membrane state evaluation system based on color comparison, which comprises a multi-modal data acquisition module used for acquiring and analyzing a mucous membrane image sequence, ambient light characteristics and basic physiological data and generating acquisition data records with aligned timestamps; and the individualized benchmark modeling module is used for establishing a patient specific color reference benchmark through HSL color space analysis on the basis of the collected data records and the preoperative physiological indexes, and generating an initial adaptive color gamut threshold model. According to the method, the specific initial color reference is established for each patient, and the sliding window algorithm is combined to continuously monitor the color gamut drift so as to dynamically update the reference, so that accurate self-adaption to individual physiological differences of the patients and postoperative slow recovery changes is realized, and therefore, the objectivity and accuracy of evaluation are greatly improved; the problem of misjudgment caused by individual differences or normal evolution of physiological states in a traditional fixed threshold value method is effectively avoided.

A medical wearable matching system is provided that pre-assesses a fit of a wearable article of medical device for a patient. The matching system may facilitate fitting and selection of a class of wearable articles for a patient. The matching system may also evaluate potential effects of the fit on operations of the medical device. Patient specific data, such as measurements, are fed into a fit prediction artificial intelligence (“AI”) model. The fitting AI model is trained with fitting data of previous patients to predict a class of wearable medical article that is likely, above a threshold amount, to provide a target fit for the patient. The patient specific information may also be fed into an adverse potential AI model. The adverse potential AI model is trained with patient experience data describing adverse operations of a wearable article worn by prior patients.

Systems and methods are provided for multi-schema analysis of patient specific anatomical features from medical images. The system may receive medical images of a patient and metadata associated with the medical images indicative of a selected pathology, and automatically classify the medical images using a segmentation algorithm. The system may use an anatomical landmark detection algorithm leveraging Deep Reinforcement Learning (DRL) techniques to automatically locate one or more anatomical landmarks associated with the patient specific anatomical feature within the medical images. A 3D surface mesh model may be generated representing the patient specific anatomical features including the located one or more anatomical landmarks. The located one or more anatomical landmarks may be used to guide placement of a 3D model of a medical device that may be fused with the 3D surface mesh model to generate a patient specific 3D model of the medical device.

The invention relates to the technical field of medical evaluation, in particular to a basic drug use tendency evaluation method and device, a medium and a product. The method comprises the following steps: acquiring a disease type, a patient specific attribute and medication information of a target patient; according to a standard evidence-based intensity database, determining intermediate evidence-based intensity of each drug type in the medication information in disease types, patient specific attributes and medication schemes; obtaining attribute information of a target hospital seeing a doctor by the target patient, and adjusting the intermediate evidence-based intensity based on the attribute information to obtain the actual evidence-based intensity of the target patient; determining the average standard evidence-based intensity of each base drug type corresponding to the disease type; and calculating a difference value between the actual evidence-based intensity and the average standard evidence-based intensity as a base drug use tendency degree, and generating a drug use rationality evaluation result for the target patient according to the base drug use tendency degree. According to the invention, multi-dimensional evaluation can be carried out on the drug use rationality of the patient, and the accuracy of the evaluation result is improved.

A real-time automated method to diagnose and / or detect stroke and engage the patient, care-takers, emergency medical system and stroke neurologists in the management of this condition includes the steps of continuously measuring natural limb activity, conveying the measurements to a cloud based real-time data processing system, identifying patient specific alert conditions, and determining solutions for acting upon needs of the patient. The system by which the method is implemented includes at least one body worn sensor continuously measuring natural limb activity and a patient worn data transmission device conveying the measurements to a cloud based real-time data processing system that identifies patient specific alert conditions and determines solutions for acting upon needs of the patient. In an example solution, motion data that reflects upper limb movements of a user is received from one or more sensors, specific changes in user movement are determined by estimating several quantitative signal features, and the results are input into a machine learning model to detect if the user's movements reflect a change due to the occurrence of a stroke. The quantitative features and the machine learning model determine the degree of motor deficit induced by a stroke as reflected by changes in time-series measures of signal magnitude, variability, complexity, and interrelation. The solution operates in two distinct modes, one by continuously monitoring subject activity and the second by evaluating short duration data segments when the subject is performing prescribed movement tasks. In both modes the solution detects if the user has suffered a stroke and estimates a motor deficit score to determine the severity of the stroke.

A hip joint navigation system is provided that includes a base having at least one channel disposed therethrough for receiving a pin for mounting the base to the pelvis. A mount feature is disposed on a top surface. A registration jig is configured to couple with the base and to engage anatomical landmarks. In some aspects, a patient specific jig system for hip replacement is provided including an engagement surface formed to closely mate to acetabular bone contours of a specific patient and a registration feature configured to be in a pre-determined orientation relative to an acetabulum the patient when the jig is coupled with acetabular bone contours of the specific patient. In other aspects, methods of using the systems are provided.

The invention relates to the field of orthopedic surgery risk assessment, in particular to an orthopedic surgery risk assessment method and system. The method comprises the following steps: acquiring preoperative data and intra-operative data of an orthopedic surgery of a patient, performing data preprocessing on the preoperative data and the intra-operative data, and extracting bone microstructure spatial features in initial orthopedic surgery data by using a cascade feature extraction network; calculating an association weight of the image data and the biological signal through a cross-modal attention mechanism, and constructing a patient specific skeleton model based on finite element analysis to perform real-time scene simulation on the characteristic orthopedic surgery data; the influence of joint contact stress distribution under different osteotomy angles, a bone implant interface micro-motion threshold after implantation of an internal fixation instrument and hemodynamic changes on a bone healing microenvironment is simulated in real time, complications caused by misoperation are remarkably reduced, and the success rate of an operation is increased.

A hip joint navigation system is provided that includes a base having at least one channel disposed therethrough for receiving a pin for mounting the base to the pelvis. A mount feature is disposed on a top surface. A registration jig is configured to couple with the base and to engage anatomical landmarks. In some aspects, a patient specific jig system for hip replacement is provided including an engagement surface formed to closely mate to acetabular bone contours of a specific patient and a registration feature configured to be in a pre-determined orientation relative to an acetabulum the patient when the jig is coupled with acetabular bone contours of the specific patient. In other aspects, methods of using the systems are provided.

A system and method for generation and use of radiation outcome prediction (response & side effects) score for patients undergoing radiotherapy for various medical conditions wherein the score is a personalized score, provided by analyzing multiple parameters including the tumor specific, patient specific, gene specific and treatment planning specific parameter(s), during and post therapy.

A computer implemented method for generating a 3D printable model of a patient specific anatomic feature from 2D medical images is provided. A 3D image is automatically generated from a set of 2D medical images. A machine learning based image segmentation technique is used to segment the generated 3D image. A 3D printable model of the patient specific anatomic feature is created from the segmented 3D image.

Systems and methods for determining IRA candidacy and optimizing autologous corneal graft location and orientation to standardize and improve IRA procedures. A novel digital virtual model (i.e., a digital twin) of the eye is provided that integrates corneal imaging, pachymetry, topography and limbus / white-to-white measurements, and automates key surgical variables such as trephine size, autologous corneal graft rotations, and curvature and thickness matching, reducing cognitive load on the surgeon and improving accuracy. The embodiments herein provide precise graft placement parameters to maximize the amount of clear cornea in the visual axis. By providing real-time, patient specific guidance, the present embodiments enhance surgical precision and visual outcomes.

A computer implemented method for generating a 3D printable model of a patient specific anatomic feature from 2D medical images is provided. A 3D image is automatically generated from a set of 2D medical images. A machine learning based image segmentation technique is used to segment the generated 3D image. A 3D printable model of the patient specific anatomic feature is created from the segmented 3D image.

A surgical system includes a first guide component that is couplable with a first metatarsal, and a second guide component configured to releasably couple with a medial cuneiform. The surgical system also includes a cut guide and a correction guide. A surgical method is disclosed that includes the steps of coupling a first guide with a first metatarsal of a patient using at least one first stabilization wire; removing the first guide from the first metatarsal; coupling a second guide with the medial cuneiform using at least one second stabilization wire; removing the second guide from the medial cuneiform; engaging a cut guide with the first metatarsal via the at least one first stabilization wire; disengaging the cut guide from the first metatarsal; engaging the cut guide with the medial cuneiform via the at least one second stabilization wire; and disengaging the cut guide from the medial cuneiform.

A radiology report automatic generation method, system, device and medium based on patient specific priori knowledge uses a special token to represent missing patient clinical context information, so that a text encoder can process complete and incomplete clinical context input in a unified manner, and then robust clinical context features are extracted; the method comprises the following steps: constructing a space-time fusion network STF, integrating previous medical images of a patient, establishing a difference mapping relation between a current image and a historical image for modeling an evolutionary process of a disease, and extracting space-time visual features with time dependence; establishing an attention-enhanced hierarchical fusion network for fusing multiple layers of hidden states in a visual encoder so as to extract multiple layers of hierarchical visual features with rich semantics; a prior perception progressive fusion network is introduced, and patient specific prior knowledge and hierarchical visual features are gradually fused in a coarse-to-fine mode to generate multi-modal features facing radiology report generation; the method comprises the following steps: designing a two-stage training strategy: in the first stage, aiming at image-text alignment, improving the accuracy of medical image-text retrieval; and in the second stage, the generation of the radiology report is taken as a target, a text decoder is optimized, and the performance of the generated radiology report in the aspects of clinical semantic accuracy and language expression quality is improved.

Disclosed are systems and methods for rapid generation of simulations of a patient's spinal morphology that enable pre-operative viewing of a patient's condition and to assist surgeons in determining the best corrective procedure and with any of the selection, augmentation or manufacture of spinal devices based on the patient specific simulated condition. The simulation is generated by morphing a generic spine model with a three-dimensional curve representation of the patient's particular spinal morphology derived from existing images of the patient's condition. Other anatomical structures in the patient's skeletal system are likewise simulated by morphing a generic normal skeletal model, as applicable, particularly those skeletal entities that are connected directly or indirectly to the spinal column.

A system for making a resection of a bone having localized anatomical surface features prior to resection, comprising: a resection guide locator including a body, the body including opposing medial and lateral wings extending outwardly from the body, the body defining a recess being a socket defined by an annular wall, the annular wall including an upper wall, a lower wall, a resection guide having a body defining a resection slot, at least a portion of the resection guide body sized and configured to be received within the recess defined by the resection guide locator.

A surgical planning and assessment system is disclosed. The system may include a computing system having a processor, a data store, and a patient specific planning and analysis module. The system may be configured to store a patient specific target plan including a digital representation of a spinal rod having a target geometry and transform the digital representation into a curved line. The system may generate a spline data set comprising a plurality of spline segments that are sequentially linked to one another. The system may adjust, iteratively, each spline segment until the plurality of spline segments approximate the curved line. The system may generate a digital file comprising drawings for manufacturing a spinal rod based on the plurality of spline segments. In various embodiments, each spline segment may be defined by a corresponding arc or a corresponding line extending between a corresponding starting point and a corresponding ending point.

Systems and methods for designing and implementing patient-specific surgical procedures and / or medical devices are disclosed. In some embodiments, a method includes receiving intra-operative data during a surgical procedure to install a patient-specific implant in a patient. The system can compare the intra-operative data to a pre-operative plan to determine whether the implant is positioned and located according to the pre-operative plan.

Systems and methods described herein relate to developing a virtual anatomical model of a coronary artery with specific conditions and related fluid properties, such as the velocity and viscosity of selected fluid, to provide accurate parameters for an intravascular imaging procedure. The resulting parameters may relate to the contrast agent volume, time for pullback of intravascular tool, and contrast agent injection force. The models may be patient specific, based on characteristics of a patient's artery. The systems and methods may be utilized for PCI planning, vascular device design and process optimization.

Methods and systems are disclosed for assisting with the management of anemia in a patient. In some examples, the method includes accessing patient parameters associated with the patient, wherein the patient parameters include an aligned hemoglobin time series for the patient generated from multiple measurement sources. The method then includes accessing a physiology-based model and adapting the physiology-based model into a patient specific model that predicts future hemoglobin levels for the patient based on one or more erythropoiesis-stimulating agent (ESA) dosing regimens, wherein adapting the physiology-based model to the patient specific model utilizes the patient parameters, and generates estimates of patient-specific physiological characteristics. The method then includes running simulations with the patient specific model and determining a recommended ESA dose. The recommended ESA dose that the model predicts will cause either or both of the patient's hematocrit or hemoglobin concentration to reach a desired range within a specified time frame.

In providing radiation therapy (RT) support, a patient specific toxicity risk is estimated for a RT side effect using a Bayesian network that receives as inputs values of biomarkers of the patient. A patient-specific RT plan is optimized with respect to parameters including the patient-specific toxicity risk. During delivery of RT according to the plan, at least one updated value is received for the biomarkers of the patient, and an updated patient-specific toxicity risk is estimated using the Bayesian network with the updated value(s). The Bayesian network biomarker nodes and a toxicity risk node representing the patient specific toxicity risk, and directed arcs with arc weights representing strengths of interdependencies between the nodes connected by the directed arcs. A graphical user interface (GUI) is provided via which a clinician may interact with the Bayesian network. A test recommendation may be initiated or updated for scheduling of a patient test based on the updated patient-specific toxicity risk.

A method of titrating an antidiabetic for a patient is disclosed, along with a system for performing the method. The method includes providing a database having anonymized personal parameters for a plurality of previously managed subjects and generating cohorts from the plurality of previously managed subjects based on similarities in the anonymized personal parameters. The method further includes receiving patient specific personal parameters and identifying from the generated cohorts a patient similar cohort corresponding to the patient specific personal parameters. A titration output parameter to optimize may either be entered or computed. Based on the titration output parameter to optimize and the patient similar cohort, a customized titration protocol for the patient can then be derived.

Transfusion independence in, and overall survival of, myelofibrosis patients can be improved by selecting a patient specific treatment method. A patient's baseline ferritin level can be used as a biomarker to select a first line treatment. The patient's ferritin levels during treatment can inform decisions about terminating treatment or changing treatments to maintain transfusion dependence and / or convert a patient to transfusion independent from transfusion requiring or transfusion dependent.

This invention relates to a method of radiography of at least a portion of a height of a patient body in standing position, comprising: one or more first vertical scanning of said portion of patient body height by a first radiation source (21) and a first radiation detector (23), one or more second vertical scanning of said portion of patient body height by a second radiation source (22) and a second radiation detector (24), wherein further comprising: making a patient specific 3D reconstruction on at least a second part (H2) of said portion of patient body height, at least combining therefore together both said first and second 2D images with complementary data, making a computed tomography of said second part (H2) of said portion of patient body height, said second part (H2) of said portion of patient body height being shorter, or at least twice shorter, than said first part (H1) of said portion of patient body height, said second part (H2) of said portion of patient body height being determined by at least one of said one or more first vertical scanning and at least one of said one or more second vertical scanning, said complementary data, used to make said patient specific 3D reconstruction on at least said second part of said portion of patient body height, comprising said computed tomography of said second part (H2) of said portion of patient body height.

A surgical planning and assessment system is disclosed. The system may include a computing system having a processor, a data store, a patient specific planning and analysis module, and a display. The system may be configured to access a database storing a plurality of possible surgical plans. The computing system may store a target surgical plan including a plurality of patient specific inputs including at least one preoperative medical image of a spine of a target patient and analyze the target surgical plan to determine a predicted alignment of the spine of the target patient. The computing system may develop a plurality of predictive models including a predicted alignment of the spine of the target patient based on the target surgical plan and suggest at least one alternative surgical plan with respect to the target surgical plan.