28 results about "Patient model" patented technology

The present disclosure provides systems and methods for providing neurostimulation therapy according to gait features. The gait features may be analyzed to develop a patient model between gait features and optimal settings for a neurostimulation therapy using machine learning operations. The model is used to control ongoing neurostimulation therapy for the patient.

The invention provides an illness state tracking and early warning system based on a knowledge database, and the system comprises a data module which is responsible for collecting and integrating clinical knowledge and patient data information in the cardiovascular field; the survival rate prediction analysis module is used for extracting pathological features from the patient model, performing survival rate prediction analysis by using a deep learning algorithm and a neural network model, and displaying a prediction result to a physician in a visual report form; and the illness state auxiliary tracking, studying and judging module monitors the illness state change of the patient in real time, analyzes the illness state development trend and risk factors in combination with a knowledge graph and a patient model, and provides treatment scheme recommendation and early warning notification functions. Through the beneficial effects of integrating medical resources, improving data security, accurately predicting the survival rate of a patient, monitoring and early warning in real time, widely applying a knowledge graph, being friendly to user interaction, feedback circulation and continuous optimization and the like, powerful support is provided for prevention, diagnosis and treatment of cardiovascular diseases.

A three-dimensional (3D) digital image of an individual patient marked with target therapeutic locations on a skin surface of the patient is used for locating those treatment locations on a patient's skin for fascial manipulation and other treatments. The 3D image may be obtained by merging an unmarked 3D digital patient model image converted from a 2D optical image with a “generic” musculoskeletal digital model having optimized target therapeutic locations distributed over a skin surface thereof. The generic musculoskeletal digital model may be obtained from a library of such images.

A patient's health is modeled through multiple scenarios. A base model incorporates rules that govern a response by a human being to one or more diseases, as well as a relation between health metrics and the diseases. Health attributes of the patient are obtained. From the base model and the health attributes, a patient model is generated. The patient model is modeled under different parameters to generate health metrics. From an analysis of the parameters and the resulting health metrics, occurrence probabilities for each of the sets of parameters are determined. Risks are identified, which indicate a likelihood of the patient transitioning from the initial state to an adverse outcome such as a diseased state. A report provides a diagnosis of the patient and one or more remedies / interventions that are predicted, based on the plural sets of parameters and resulting health metrics, to avoid or prevent an adverse outcome.

The application discloses a kind of real-time non-rigid registration method and system of surgical navigation image based on deep learning, comprising: calibration the space transformation matrix between RGB-D camera and medical imaging equipment;RGB-D camera real-time capture patient whole body optical image and depth information;Medical imaging is carried out to patient, while reconstructing the initial model of patient based on SMPL based on human posture estimation neural network of deep learning;Medical image is point cloud, and the point-to-plane spatial position matching relationship between medical image and the initial model of patient is established;Neural network reconstructs the dynamic change of patient model in operation in real time, and the non-rigid deformation field of medical image is solved by matching relationship;Registration medical image, update display.The application can realize the real-time non-rigid registration of surgical navigation medical image to different body posture patient, solve the movement, deformation of organ, soft tissue caused by patient posture change in surgical navigation, thereby cause the problem of medical image failure, help to improve the accuracy of surgical navigation.

A computer-implemented method for generating, by one or more computer processors, a three dimensional model for use in assisted navigation of a patient's oral cavity is provided.SOLUTION: Receiving 302 a first dataset comprising surface boundary information, generating 304 a digital patient model from at least a portion of the first dataset, performing 306 a 3D surface scan of a portion of an intraoral cavity using a 3D scanner, the 3D surface scan comprising a region of interest, aligning 308 the acquired 3D surface scan to at least a portion of the digital patient model, determining 310 a spatial transformation configured to align or register the acquired 3D surface scan and the 3D digital patient model, and using at least the transformation: Determining 312 position and orientation information of the 3D scanner relative to the digital patient model and generating 314 a second dataset comprising the position and orientation information of the 3D scanner relative to the 3D patient model.SELECTED DRAWING: Figure 2A

Methods, systems, and apparatuses are described for associating dielectric properties with a patient model.

The present disclosure shows a generated, large-scale and realistic patient dataset that mimics real patient data distributions, supports multiple treatments, and provides ground truth for the effects of these treatments. The datasets generated are of synthetic patients modeled on a large nationwide cohort of real patient data including their history of diagnoses, medications, and laboratory values. A data generation process is designed by adapting an Anonymization Through Data Synthesis Using Generative Adversarial Networks (ADS-GAN) model for fictitious patient information generation and using a neural network for treatment outcome generation. The synthetic dataset demonstrates strong similarity to the original dataset as measured by the Wasserstein distance. In addition, privacy of the original patients is preserved so that the dataset can be made available to the research community to evaluate causal inference models.

The application discloses a doctor-patient joint decision-making method based on LLM-Agent multidisciplinary dynamic cooperation and relates to the technical field of doctor-patient joint decision-making. The method comprises the following steps: acquiring a disease description, extracting symptoms and signs by an LLM, assigning a department according to the symptoms and signs by the LLM, acquiring a multidisciplinary team composed of doctor agents of the corresponding department, independently analyzing the disease description by each doctor agent, generating a scheme, then integrating the schemes, and determining whether to adopt the scheme as a treatment scheme by a voting review mechanism. Patient preferences are extracted from the disease description based on a predefined preference format and a pre-constructed lightweight LLM. Then, a patient model is constructed based on the patient preferences and the disease description. Potential conflicts between the treatment scheme and the patient preferences are identified by an LLM. The patient agent quantitatively evaluates whether the treatment scheme is satisfactory according to the potential conflicts. If not, the next iteration is entered, otherwise the negotiation is stopped. The treatment scheme when the negotiation is stopped is output.

The invention belongs to the technical field of biological diagnosis, and provides an application of histone H3K27 acetylation in early diagnosis of non-infectious uveitis, which is characterized in that the histone H3K27 acetylation level is used as a judgment marker of early non-infectious uveitis. According to the invention, the acetylation expression level of histone H3K27 in cross-model and cross-species non-infectious uveitis is evaluated by establishing an EAU mouse model, an IL-17A induced human RPE cell model and an NIU patient model; the result shows that the acetylation level of the histone H3K27 is obviously reduced in three non-infectious uveitis models, and the histone H3K27 can be used as a diagnostic marker of the non-infectious uveitis.

The invention discloses a dynamic optimization method and device for medical treatment resource reservation, equipment and a medium. The method comprises the following steps: acquiring and preprocessing multi-dimensional panoramic data; constructing a bimodal patient model of static clinical portrait and dynamic state evolution, and combining a dynamic weight clinical index evaluation and clinical priority dynamic quantification method to obtain clinical features of the patient; obtaining an inspection time length prediction result, and constructing an inspection operation constraint matrix to obtain an inspection operation constraint rule; an MADDPG multi-agent resource scheduling model under the CTDE framework is constructed, and a resource scheduling joint optimization strategy is obtained through multi-agent joint training; and generating a reservation scheme based on the resource scheduling joint optimization strategy, and performing rolling updating on the reservation scheme based on a look-ahead rolling rescheduling mechanism. The invention belongs to the field of diagnosis strategy optimization. According to the invention, a more reasonable doctor-seeing appointment strategy can be provided for the doctor-seeing personnel.

The application discloses a somatosensory interaction tactile feedback fusion training system for nursing teaching and belongs to the technical field of nursing teaching, and specifically comprises the following steps: establishing a student operation behavior vector library; analyzing the operation behavior vector library and constructing a parameterized virtual patient model; extracting palpation depth deviation values and pressure distribution deviation amounts, integrating the same into an inference deviation parameter set; deconstructing a physiological parameter space to identify inference deviation, establishing an adversarial variation instance generation rule; inferring the deviation parameter set and corresponding physiological parameter adjustment amount based on a historical training cycle of an application rule, training an output parameter change strategy; obtaining the inference deviation parameter set based on real-time operation behavior vectors and inputting the parameter change strategy, and generating an adversarial variation patient instance as a distribution instance for a subsequent training cycle. Through directional generation of the adversarial variation instance, the application realizes accurate matching of student operation weaknesses and training content, and improves the migration efficiency of skills to a real clinical environment.

A patient's health is modeled through multiple scenarios. A base model incorporates rules that govern a response by a human being to one or more diseases, as well as a relation between health metrics and the diseases. Health attributes of the patient are obtained. From the base model and the health attributes, a patient model is generated. The patient model is modeled under different parameters to generate health metrics. From an analysis of the parameters and the resulting health metrics, occurrence probabilities for each of the sets of parameters are determined. Risks are identified, which indicate a likelihood of the patient transitioning from the initial state to an adverse outcome such as a diseased state. A report provides a diagnosis of the patient and one or more remedies / interventions that are predicted, based on the plural sets of parameters and resulting health metrics, to avoid or prevent an adverse outcome.

A method for operating an X-ray facility for recording a three-dimensional (3D) image data set of a target area of a patient is provided. A recording arrangement including an X-ray detector and an X-ray source may be rotated about an axis of rotation for recording two-dimensional projection images based on the image data set. A model instance of a parameterizable patient model that is patient-specific and 3D is determined. Target area information describing the target area is determined in the model instance from default information. At least two at least partially different partial recording areas of the target area are determined from the target area information. The partial recording areas cover the target area along the axis of rotation. One projection image set is recorded for each of the partial recording areas, and the image data set is reconstructed from the projection image sets.

The invention relates to the technical field of medical simulation training, in particular to a diagnosis and treatment thinking dynamic simulation training method and system based on a computable physiological model, and the method comprises the steps: instantiating a virtual patient model defined by a physiological state vector, a clinical expression vector and a static parameter; receiving a user diagnosis and treatment operation instruction; according to the medical knowledge rule base, the influence of the coupling differential equation set dynamic calculation operation on the physiological state vector is solved; according to the updated physiological state, generating a corresponding clinical manifestation vector through a mapping function; the clinical manifestation is converted into a multi-mode feedback signal to be output; and forming an interactive training cycle, and performing multi-dimensional quantitative evaluation based on the complete operation sequence and the final state of the model. The system takes a computable model as a core, realizes continuous dynamic evolution and response of cases, and can effectively train and evaluate the high-order diagnosis and treatment thinking ability of clinicians.

A computer-implemented method (100) for generating, by one or more computer processors, a 3-dimensional model (10D) for assisted navigation of a patient's oral cavity, the method based at least on a first scan of an anatomical region of the patient's oral cavity, the method comprising: receiving a first data set comprising surface boundary information from the first scan, and generate a 3-dimensional digital patient model from at least a portion of the first data set; performing a 3-dimensional surface scan on a portion of the oral lumen using a 3-dimensional surface scanner, and thereby obtaining a 3-dimensional surface scan, the 3-dimensional surface scan comprising the region of interest; matching the acquired 3-dimensional surface scan with at least a portion of the 3-dimensional digital patient model; determining a spatial transformation using the acquired 3-dimensional surface scan and the digital patient model, the spatial transformation configured to align the acquired 3-dimensional surface scan with the 3-dimensional digital patient model or to reach a predetermined degree of registration; determining position and orientation information of the 3-dimensional surface scanner relative to the 3-dimensional digital patient model using at least the transformation; and generating a second data set including the position and orientation information of the 3-dimensional surface scanner relative to the 3-dimensional digital patient model.

The invention discloses a doctor-patient joint decision-making method based on LLM-Agent multidisciplinary dynamic cooperation, and relates to the technical field of doctor-patient joint decision-making. The method comprises the following steps: acquiring disease description, and extracting symptoms and signs from LLM; and according to the symptoms and the signs, performing department distribution by the LLM, and obtaining a multidisciplinary team composed of the doctor agents of the corresponding departments. Each doctor agent independently analyzes the disease description and generates a scheme, then integrates the schemes, and determines whether to adopt the scheme as a treatment scheme or not by a voting review mechanism. Patient preferences are extracted from the condition description based on a predefined preference format and a pre-constructed lightweight LLM. And then constructing a patient model by the patient preference and the condition description. Potential conflicts between treatment regimens and patient preferences are identified by LLM. And the patient intelligent agent quantitatively evaluates whether the treatment scheme is satisfied according to the potential conflict. And if not, entering the next iteration, otherwise, stopping negotiation. And outputting the treatment scheme when the negotiation is stopped.

The application provides a small needle knife control method and system based on motion sensing data, and relates to the technical field of small needle knife control. The method comprises the following steps: acquiring patient data, and constructing a digital patient model according to the patient data; determining an operation forbidden area according to the digital patient model; acquiring operator historical data; acquiring a three-dimensional space coordinate of a knife tip through an optical positioning camera arranged above a surgical area; acquiring handle motion sensing data; determining a first unit displacement tissue impedance change rate and a second unit displacement tissue impedance change rate; determining a safety boundary warning distance according to the operator historical data, the operation forbidden area and the three-dimensional space coordinate of the knife tip; determining an operation stability coefficient; and performing small needle knife control according to the first unit displacement tissue impedance change rate, the second unit displacement tissue impedance change rate, the safety boundary warning distance and the operation stability coefficient. According to the application, the accuracy of small needle knife control can be improved.

An artificial intelligence cerebral aneurysm detection system using 3D angiographic CT according to one embodiment of the present invention comprises: an input unit for acquiring a CT image of a patient; and an information processing unit including a patch model operating processor for operating a patch model that predicts the location of a cerebral infarction on a patch basis, and a patient model operating processor for operating a patient model that predicts the presence or absence of a cerebral aneurysm on a patient basis based on the results of the patch model. By doing so, the presence or absence of a cerebral aneurysm in a patient can be determined, thereby contributing to the improvement of the accuracy of cerebral aneurysm diagnosis.

The invention relates to a computer-implemented method for operating an imaging X-ray device (15), wherein a virtual patient model (7) is provided for an examination procedure of a patient. - the virtual patient model (7) of the patient, which describes at least the surface (6) of the patient, is determined based on patient information describing the patient, - a model position for the patient model (7) is determined in a coordinate system (10) of the X-ray device (15), in which the radiation distribution during an X-ray image of the examination procedure is also known, and - the patient model (7) is positioned according to the model position, wherein, to determine the position of the patient model (7), at least one X-ray image (1) of the examination procedure, which is taken at a current patient position of the patient in the X-ray device (15) and is available in the coordinate system (10) of the X-ray device (15), is supplied as input data to a trained position determination function (3), the output data of which describe an assignment of at least one distinguished point of the patient model (7) to at least one distinguished point of the X-ray image (1), wherein the model position is determined from the output data.

The invention relates to radiographic imaging. In order to improve effective dose estimation, a computer-implemented method (400) for determining an effective dose of a subject from a radiographic examination is provided in which a radiographic imaging device having a radiation source and a radiation detector is used to acquire a two-dimensional (2D) radiographic image of the subject, the method comprising: a) providing (410) a three-dimensional (3D) patient model; b) determining (420) a set of non-rigid registration parameters for adapting the 3D patient model to the object to map points in a 2D radiographic image of the object to points in the 3D patient model; c) registering (430) the 3D patient model to the subject using the determined set of non-rigid registration parameters, and d) determining (440) the effective dose based on the registered 3D patient model of the body part, geometry data of the radiographic imaging device, and generator data of the radiation source.

The invention discloses a reverse optimization method for Flash radiotherapy and a radiotherapy planning system, and relates to the technical field of radiotherapy plans, and the method comprises the steps: receiving image data of a patient, delineating a target region and an organ at risk on the image data of the patient to obtain a patient model, setting a dose target and a dose rate target, and converting the dose target and the dose rate target into a mathematical model; generating initial equipment parameters, and adjusting the equipment parameters by performing iterative optimization so as to minimize the value of the mathematical model; and carrying out visual display on an optimization result and outputting a final treatment plan. According to the method, a dose rate distribution calculation method is innovatively provided, the dose rate is taken as one of core optimization objectives and is incorporated into a reverse optimization mathematical model, direct optimization of the dose rate is realized, and an optimal equipment parameter combination can be automatically searched according to set Flash effect related clinical objectives, so that a Flash treatment plan meeting requirements is generated.

A computer-implemented method (100) for generating, by one or more computer processors, a 3-dimensional model (100) for use in assisted navigation in the oral cavity of a patient, wherein the method is based on at least one initial scan of an anatomical region of the patient's oral cavity, comprising: receiving an initial data set comprising surface boundary information from the initial scan, and generating a 3-dimensional digital patient model from at least a portion of the initial data set, performing a 3-dimensional surface scan of a portion of the oral cavity using a 3-dimensional surface scanner and thereby acquiring a 3-dimensional surface scan, wherein the 3-dimensional surface scan comprises a region of interest, aligning the acquired 3-dimensional surface scan with at least a portion of the 3-dimensional digital patient model, and definingusing the acquired 3D surface scan and the digital patient model, a spatial transformation designed to align the acquired 3D surface scan and the 3D digital patient model or to bring them to a predetermined degree of overlap, determining position and orientation information of the 3D surface scanner relative to a 3D digital patient model using at least the transformation, and generating a second dataset comprising the position and orientation information of the 3D surface scanner relative to the 3D digital patient model.

The invention relates to a computer-implemented method for operating an imaging X-ray apparatus for providing a virtual patient model for a patient examination procedure, determining a virtual patient model on the basis of patient information; determining a model position of the patient model in a coordinate system of the X-ray apparatus; and positioning the patient model in accordance with the model position, for determining the patient model position, at least one X-ray image recorded in a current patient position in the X-ray apparatus during the examination procedure and present in the coordinate system of the X-ray apparatus is input as input data into a trained position determination function, the output data of the position determination function specifying a correspondence between at least one salient point of the patient model and at least one salient point of the X-ray image, wherein the model position is determined from the output data. The invention also relates to an X-ray apparatus, a computer program product and an electronically readable data carrier.

A method for operating an X-ray device includes ascertaining a patient model of a patient that describes a surface of the patient, based on patient information. A model position is determined for the patient model in a coordinate system of the X-ray device, in which radiation distribution during an X-ray image acquisition of the examination procedure is also known. The patient model is positioned in accordance with the model position. An X-ray image of the examination procedure, which is acquired for a current patient position of the patient in the X-ray device and exists in the coordinate system of the X-ray device, is supplied as input data to a trained position determination function. Output data from the trained position determination function describes a mapping of a distinguished point of the patient model to a distinguished point of the X-ray image. The model position is determined from the output data.

The application discloses a medical treatment resource reservation dynamic optimization method and device, equipment and medium, including: acquiring and preprocessing multi-dimensional panoramic data; constructing a static clinical image and a dynamic state evolution bimodal patient model, and combining a dynamic weight clinical index evaluation and a clinical priority dynamic quantification method to obtain patient clinical characteristics; obtaining an examination duration prediction result, and constructing an examination operation constraint matrix to obtain an examination operation constraint rule; constructing a MADDPG multi-agent resource scheduling model under a CTDE framework, and obtaining a resource scheduling joint optimization strategy through multi-agent joint training; generating a reservation scheme based on the resource scheduling joint optimization strategy, and performing rolling update on the reservation scheme based on a forward-looking rolling rescheduling mechanism. The application belongs to the field of diagnosis resource strategy optimization. The application can provide more reasonable treatment reservation strategies for treatment personnel.

Method for determining at least one value of at least one imaging parameter for an X-ray image of a patient (5) lying on a patient bed (6), comprising - S1: contactless scanning of at least part of the patient's surface (5) using at least one electromagnetic sensor (31), - S2: Calculating the three-dimensional contour of the scanned surface, - S3: Identify at least one anatomical landmark of the patient (5) using the three-dimensional contour, - S4: Determining the position of the anatomical landmark in the coordinate system of the patient bed (6), as well as - S5: Determining the value of the recording parameter based on the position of the anatomical landmark, - S6: Calculating a patient model based on the three-dimensional contour for the pictorial representation of the patient (5), - S7: Highlighting the identified landmark in the patient's image (5), - S8: Providing a graphical user interface (23) with a button (1,2,7,10) wherein the button (1,2,7,10) represents the identified landmark, wherein the button (1,2,7,10) is designed to trigger the determination of the recording parameter value through user interaction.

A human body positioning method for X-ray transmission examination. The method comprises: acquiring a visible light image of a patient on a hospital bed, and a preoperative medical image of the patient obtained by using a fluoroscopic examination device (S101); on the basis of the visible light image, obtaining a corresponding parameterized patient model, and on the basis of the parameterized patient model, performing fitting to obtain a standard skeletal model (S102); performing segmentation and reconstruction on the preoperative medical image, so as to obtain a personalized skeletal model (S103); and on the basis of a target bony landmark, fusing the personalized skeletal model and the standard skeletal model to obtain a target fused image, so as to position a target organ of the patient (S104). By using the method in the present disclosure, a target organ can be quickly positioned preoperatively on the basis of a target bony landmark.