43 results about "Patient model" patented technology

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

The invention relates to the technical field of data analysis, in particular to a VR-based CT room operation virtual training method and system. The method comprises the following steps: acquiring an equipment model and a patient model in current CT training operation; analyzing a relative position relationship between the equipment model and the patient model at the initial position to obtain an initial abnormal value; analyzing posture changes of a plurality of limb parts of the patient model before and after the limb adjustment operation to obtain a plurality of posture anomaly indexes; according to the initial abnormal value, analyzing the change of an overlapping region between a plurality of limb parts of the patient model and the equipment model in the examination process of the current CT training operation, and obtaining a plurality of position abnormal indexes; and obtaining an operation evaluation result of the current CT training operation according to the plurality of attitude anomaly indexes and the plurality of position anomaly indexes. According to the invention, the evaluation accuracy of the abnormal operation condition of the CT training operation can be improved.

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.

For patient weight estimation in a medical imaging system, a patient model, such as a grid, is fitted to a depth image. One or more feature values are extracted from the fitted patient model, thereby reducing noise and clutter in the values. The weight estimation is regressed based on the extracted features.

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.

System (SYS) and delated methods for supporting an imaging operation of an imaging apparatus (IA) capable of assuming different imaging geometries. The system comprises an input interface (IN) for receiving a current image acquired by the imaging apparatus (IA) of a current region of interest (ROI_1) at a current imaging geometry (p). A pre-trained machine learning component (MLC) computes output data that represents an imaging geometry change (Δp) for a next imaging geometry (p′) in relation to a next region of interest (ROI_2). An output interface (OUT) outputs a specification of the imaging geometry change.

The invention relates to the technical field of virtual simulation, in particular to a child electronic bronchoscope operation emergency rescue simulation training system which comprises a VR device used for presenting a virtual scene of one or more emergency conditions of a virtual patient in a child electronic bronchoscope operation; the patient model is used for being operated and used by an operator; the camera is used for collecting emergency rescue operation executed by an operator aiming at the virtual scene of one or more emergency conditions of the virtual patient; the processing module is connected with the VR equipment and the camera, and is used for judging whether the emergency rescue operation is correct or not, obtaining a judgment result, prompting an operator based on the judgment result, providing a changeable emergency rescue training environment through a virtual scene presented by the VR equipment, providing a more real simulation training feeling for the training of the operator, and improving the training experience of the operator. Therefore, the effectiveness of sudden emergency rescue training in the child electronic bronchoscope operation is improved.

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.

An automatic checkpoint identification system receives a user input including patient health information or pre-operative images. The system generates, as an output of a patient model estimator, a 3D patient model with one or more anatomical checkpoints from the user input. The system receives, from an external imaging system, one or more imaging frames tracking a medical procedure. The system detects, by a controller, progress of the medical procedure based on a proximity of an intravascular device to one of the one or more anatomical checkpoints. The system generates, by a controller, an action recommendation based on the proximity.

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.

The application discloses a kind of automatic positioning methods of pelvic implant passage, comprising the following steps: step 1, establish pelvic general anatomical three-dimensional template;Step 2, define implant passage template based on the three-dimensional template of step 1;Step 3, registration is implemented to non-homologous pelvic CT image based on internal information;Step 4, obtain the coordinate conversion between CT coordinate system and anatomical coordinate system;Step 5, the alignment between target pelvic in non-homologous pelvic and pelvic general anatomical three-dimensional model is implemented;Step 6, based on step 2 and step 5, establish passage automatic search.The application adopts the method of three-dimensional model statistical analysis to obtain accurate pelvic general anatomical three-dimensional template, realizes the alignment between different patient models using image registration, based on the space transformation between image coordinate system and anatomical coordinate system, the pelvic implant passage of each patient can be obtained by the method of three-dimensional model registration, so as to shorten the operation time, guide the operation of doctor, and reduce the purpose of doctor's manual operation.

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 the related field of medical education, in particular to a virtual standardized patient construction method based on a multi-modal large model. Comprising a multi-modal data preprocessing module, an interdisciplinary adaptation framework, a hybrid expert model architecture and a dynamic interaction response system. The hybrid expert model architecture performs fusion reasoning on a multi-modal case library of the multi-modal data preprocessing module and scene parameters of the interdisciplinary adaptation framework to generate a virtual standardized patient model with emotions; the dynamic interactive response system asks questions for students according to the virtual standardized patient model of the hybrid expert model, and identifies answers and interactions of the students; the answers and interaction data of the students are transmitted to the virtual standardized patient model of the mixed expert model architecture for reasoning, and the students are further questioned according to the reasoning result of the mixed expert model architecture until the examination is finished; the virtual standardized patient generated by the method has a real emotional state of the patient, interaction is more real, and interaction which better conforms to character features is carried out according to reasoning of student questions.

The invention provides a patient screening method and device based on a mild cognitive impairment patient model, a storage medium and electronic equipment, and relates to the technical field of mild cognitive impairment screening, and the method comprises the steps: collecting research sample data based on community natural population health data; extracting training data based on the research sample data; training an extreme gradient lifting algorithm model based on the training data to obtain a plurality of preliminary models; performing model performance evaluation on the plurality of preliminary models, and selecting the preliminary model meeting evaluation requirements as a rapid screening model for mild cognitive impairment; screening patients with mild cognitive impairment based on the rapid screening model for mild cognitive impairment. Through the method, patients with mild cognitive impairment can be accurately screened, and the screening efficiency is high.