27 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.

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 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.

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.

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.

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.

The invention discloses a surgical navigation method based on a mixed knowledge model and a related device. The method comprises the steps of obtaining a mixed knowledge model fusing formalized operation specifications and probabilistic anatomical mapping; constructing a patient specific knowledge representation based on the model and patient preoperative data; multi-modal perception data of an operation scene is obtained in real time in the operation, semantic understanding is conducted through the mixed knowledge model, and semantic representation of the scene in the operation is generated; performing spatial association on the preoperative knowledge representation and the intra-operative semantic representation; and finally, generating and outputting augmented reality navigation information based on the association result and the knowledge model. Structured medical knowledge, patient individual anatomical features and real-time surgical scenes can be deeply combined, and intelligent surgical navigation which is more accurate and rich in semantics and has clinical context sensing capacity is achieved.

A system and method for selecting and / or designing a tool for a medical procedure by converting static / still medical images of a particular patient into dynamic and interactive images interacting with selected medical tools including medical devices by coupling a model of tissue dynamics and tool characteristics to the patient specific imagery for simulating a medical procedure in an accurate and dynamic manner by coupling a model of tissue dynamics to patient specific imagery for simulating the medical procedure.

A respiratory assistance system can provide high flow therapy to patients. The respiratory assistance system can include a patient interface that can deliver a gas flow to a patient and a gas source that can drive the gas flow towards the patient interface at an operating flow rate. The system can include a controller for controlling the operating flow rate of the gas. The controller can apply multiple test flow rate values in a range as the operating flow rate. For each of the test flow rate values, the controller can measure a patient parameter. The controller can determine a new flow rate value based on the measured patient parameters. Patient parameters can include respiration rate, work of breathing, or any other parameters related to the respiratory circuit.

The present application relates to the technical field of data feature mining, and particularly relates to a remote nursing management method based on data feature mining.The method comprises the following steps: obtaining patient specific conditions and demand data; selecting and configuring data sources according to the patient specific conditions and demand data to obtain a data source configuration table; collecting multi-dimensional data according to the data source configuration table and pre-processing the multi-dimensional data to obtain comprehensive patient data; modeling individual baselines for the comprehensive patient data to obtain an individual baseline model; standardizing real-time data of the comprehensive patient data to obtain standardized real-time data; calculating deviations according to the standardized real-time data and the individual baseline model to obtain a deviation value sequence; and analyzing the correlation of indexes for the deviation value sequence to obtain an index correlation matrix.The present application improves the utilization of data feature mining, makes remote nursing management more accurate and personalized, and ultimately improves nursing quality and efficiency.

Medical interventions utilizing catheters require precise and carefully controlled movement of a catheter and deployment of a medical device to limit damage (e.g., to the heart, veins, etc.). Pre-procedure simulation of catheter control can improve patient safety and outcomes. Information about a patient's anatomy, the catheter, and a medical device (e.g., an implant) can be used to create a patient specific anatomical model and a device model. The device model can be simulated moving through at least a portion of the anatomical model towards a deployment location to determine a desired implant deployment pose and develop at least one path for the device model to take towards the implant deployment location to reach the implant deployment pose minimizing contact-based damage to the anatomical model. The implant deployment pose and / or the path to reach the implant deployment location can be output to at least a display for viewing.

A method for diagnosing a joint condition includes in one embodiment: creating a 3d model of the patient specific bone; registering the patient's bone with the bone model; tracking the motion of the patient specific bone through a range of motion; selecting a database including empirical mathematical descriptions of the motion of a plurality actual bones through ranges of motion; and comparing the motion of the patient specific bone to the database.

Abstract A surgical system comprising: a surgical tool; and one or more controllers coupled to the surgical tool and being configured to: obtain a virtual bone model specific to a patient; identify an outer edge contour of the virtual bone model; generate an offset contour being spaced apart from the outer edge contour by an offset distance that is based on a geometric feature of the surgical tool; generate a face extending perpendicularly from, and along, the offset contour; produce a patient- specific virtual boundary by merger of the virtual bone model, the offset contour and the face; and configure the patient-specific virtual boundary to provide a constraint on movement and / or operation of the surgical tool. Abstract 20 26 20 52 95 06 J ul 2 02 6 A b s t r a c t 2 0 2 6 2 0 5 2 9 5 0 6 J u l 2 0 2 6

Method and / or apparatus embodiments for 3-D cephalometric analysis of a patient according to the application can display reconstructed volume image data from a computed tomographic scan of a patient's head including segmented dentition elements having an initial arrangement from one or more 2D / 3D views; can compute and display a plurality of cephalometric parameters for the patient according to the reconstructed volume image data; then use the patient specific cephalometric parameters and population biometry data, to identify one or more maxillofacial / dental abnormalities; and compose patient specific treatment plans to correct selected dentition abnormalities using maxillofacial / dental structure, which can be composed in a final tooth arrangement in a final virtual CT volume. One or more aligners can be generated to incrementally move dentition from the initial arrangement to the final tooth arrangement.

A synthetic patient specific A benchtop beating heart simulator that is custom-fabricated using 3D printing technology based on patient-specific imaging data has been developed. The simulator employs soft robotics to accurately mimic the motion and biomechanics of the left atrium, with a particular focus on the left atrial appendage and conditions associated with Heart failure with preserved ejection fraction (HFpEF). This simulator is designed to recreate hemodynamics in a controlled benchtop setting, allowing for precise testing and evaluation of medical device designs and interventions.

Surgical planning based on three-dimensional (3D) models includes obtaining two-dimensional (2D) imaging data of an anatomical region, of a patient, having patient anatomical features, generating a three-dimensional (3D) model of the anatomical region, the 3D model being specific to the patient and providing a 3D representation of the patient anatomical features, identifying at least one deformity exhibited in the 3D model and identified based on the 3D model and relative to target anatomical values for the patient anatomical features, where the identifying includes obtaining anatomical measurements based on anatomical landmarks of the patient as exhibited in the 3D model, comparing the anatomical measurements to the target anatomical values, and determining the at least one deformity based on the comparing, and determining, based on a relationship between the anatomical measurements and the target anatomical values, at least one correction to make to at least one anatomical structure of the patient.

Disclosed herein is a disposable patella clamp. In some examples, the patella clamp is configured as a patient specific patella clamp that is custom designed to fit the anatomy of a particular patient. A patient undergoes diagnostic imaging which provides patient specific data including patella diameter and thickness, patella shape, and limb alignment. The imaging can be used to generate a 3D digital model of the patient's anatomy associated with the patellar region. The patella clamp can be designed and manufactured based on the model as an integrated clamp using an additive manufacturing process with biocompatible materials. The patella clamp may include one or more tracking elements operative to enable the patella clamp to be used with a computer-assisted and / or robotic surgical system.