48 results about "Patient specific" patented technology

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 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.

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 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 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 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.

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.

Methods of treatment and patient specific clinical trials are disclosed. According to an aspect, a method includes generating a patient specific tumor model. The method also includes testing one or more drugs on the patient specific tumor model. Further, the method includes treating a patient based on the results of the patient specific tumor model tests.

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.

A system for a neurological assessment is provided. The system includes a memory storing instructions for the neurological assessment. The system includes a display, an audio output, a camera, a microphone, and a processor operably coupled thereto. The processor executes the instructions to cause the system to prompt a patient to perform actions, record video and audio of the actions as non-standardized patient specific information via the camera and the microphone, and generate relevant clipped portions from the video and the audio. The relevant clipped portions convert the non-standardized patient specific information into standardized patient specific information. The processor executes the instructions to cause the system to cause a determination of a neurologic status for the patient based on the standardized patient specific information by transmitting the relevant clipped portions to a second system for analysis and receiving an analysis result including the neurologic status from the second system.

A patient specific epileptic seizure(ES) prediction model using only electroencephalography (EEG) data with residual neural networks (ResNets) and transfer learning (TL) techniques (i.e., SPERTL) is provided. One exemplary provided model was trained on EEG data from 23 patients with a seizure prediction horizon (SPH) of 5 minutes and used the validation data to plot precision-recall curves to aid in selecting preferred thresholds. Testing on unseen data shows the provided model outperforms related art methods by achieving the highest average sensitivity of 88.1%, specificity of 92.3%, and accuracy of 92.3%. Results also demonstrate the proposed model is less susceptible to false positives while maintaining a high positive prediction rate.

SYSTEM AND METHOD FOR RECONSTRUCTION OF 3-DIMENSIONAL ANATOMY Present disclosure relates to a system and method for reconstructing 3D anatomy. The method comprises receiving at least one of an axial X-ray image and a sagittal X-ray image of a spine of a patient from one or more sources. Thereafter, the method comprises determining at least one measurement angle using at least one of the axial X-ray image and the sagittal X-ray image and at least one historical 3D spine anatomical model in a database. Subsequently, the method comprises determining at least one of anatomical features and skeletal features of the spine using at least one of the axial X-ray image and the sagittal X-ray image of the spine. Lastly, the method comprises reconstructing a patient specific 3D spine anatomical model based on the at least one measurement angle and at least one of the anatomical features and the skeletal features of the spine using a trained CNN model.

A system for a neurological assessment is provided. The system includes a memory storing instructions for the neurological assessment. The system includes a display, an audio output, a camera, a microphone, and a processor operably coupled thereto. The processor executes the instructions to cause the system to prompt a patient to perform actions, record video and audio of the actions as non-standardized patient specific information via the camera and the microphone, and generate relevant clipped portions from the video and the audio. The relevant clipped portions convert the non-standardized patient specific information into standardized patient specific information. The processor executes the instructions to cause the system to cause a determination of a neurologic status for the patient based on the standardized patient specific information by transmitting the relevant clipped portions to a second system for analysis and receiving an analysis result including the neurologic status from the second system.

The invention discloses an ultrasonic spectrum Doppler signal reconstruction method and device and imaging equipment. The method aims to solve the problem of intermittent lack of pulse Doppler signals caused by B-mode scanning insertion in an ultrasonic duplex mode. The method comprises the following core steps: firstly, continuously collecting a section of pulse Doppler signal, and learning a patient specific dynamic coefficient by using a sparse autoregression model; then during duplex operation, high-precision interpolation compensation is carried out on a signal containing a missing point by using the coefficient; constructing a pseudo-random observation matrix based on a Poisson disk sampling criterion, and sampling the interpolated signal; and finally, combining the observation data and introducing an interpolation signal as a priori, and reconstructing a complete time domain signal through a compressed sensing algorithm. The method can adapt to physiological features of different patients, while the real-time performance is guaranteed, the accuracy and continuity of spectrum reconstruction are remarkably improved, artifacts are effectively eliminated, and more reliable hemodynamic information is provided for clinical diagnosis.

A surgical computing device may include a processor configured to implement two neural networks, a primary neural network trained with a procedure focus and support neural network trained with a patient focus. Data indicative of a surgical patient, a target procedure, and a proposed procedure plan may be input to the support neural network. The support neural network may generate a patient specific mapping from this data. The patient specific mapping and the data indicative of a surgical patient, a target procedure, and a proposed procedure plan may be input to the primary neural network. The primary neural network may output a modified procedure plan that is different from the proposed procedure plan. In an example, the support neural network is trained to isolate anatomical elements. And the primary neural network is trained to identify procedure plans associated with improved patient outcomes. Such a two-network approach may facilitate the use of diverse training data sets to better refine procedure plans for a variety of uses.

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.