21 results about "Clinical evaluation" patented technology

The application discloses an ophthalmic disease image classification system based on a big data model, which comprises a data set construction module, an image segmentation module and a model training module.The data set construction module is used for collecting eye images of patients with different ophthalmic diseases in an ophthalmic clinic and performing image marking processing to construct training and test data sets.The image segmentation module adopts YOLOv7 to perform image segmentation on the images taken by the smart phone to obtain eye images containing only the part below the eyebrows and above the zygomatic arch.The model training module adopts a five-fold cross-validation method to train the model on the images, and applies data enhancement technology, white balance adjustment and a transfer learning algorithm.The application relates to the technical field of ophthalmic disease image classification.The ophthalmic disease image classification system based on the big data model has high classification accuracy for diseases such as cataracts, keratitis and pterygium in the development stage test set and different clinical evaluation stages.

This invention relates to the field of image processing technology and discloses a posture deviation recognition method based on image recognition. The method involves acquiring a posture image at the moment a specified rehabilitation movement is completed, preprocessing and segmenting it to obtain a binary contour of the target posture, representing the contour points as a complex sequence, and performing closure processing and arc length resampling to standardize the number of contour points. Based on this, a DC-free and normalized Fourier descriptor is constructed to obtain the frequency domain features of the posture and the standard posture. The global shape deviation intensity and adaptive judgment threshold are calculated through frequency domain comparison, and the residual signal is reconstructed at the resampling point location to obtain the local deviation distribution. Finally, the posture deviation level and structured results are output according to a grading rule, thereby reducing the dependence on sensors and manual thresholding, realizing the quantitative assessment and presentation of posture deviation, and providing a basis for clinical assessment and training adjustment.

The application discloses a kind of digital lower jaw accurate positioning and customized removable denture preparation integrated method, it is related to stomatology technical field. Including: initial diagnosis data collection;Digital modeling and jaw position adjustment;Design treatment jaw position guide plate;Try on jaw position guide plate, check lower jaw movement, confirm facial form beautiful, prepare silicon rubber final impression, record the actual occlusion relationship of patient;Based on occlusion relationship preparation-final denture, the obtained-final denture is carried out clinical evaluation.The application is for complex oral conditions, such as occlusion disorder, temporomandibular joint disorder patient, by obtaining personalized fitting removable denture, adjusting the relative position relationship of upper and lower jaw bones, relieving abnormal stress of joint, assisting the stability and recovery of temporomandibular joint, realizing the unity of stability and aesthetic effect of stomatognathic system after repair.

The application discloses a lung inflammation lesion quantitative evaluation artificial intelligence model based on CT images and relates to the field of artificial intelligence models.The model comprises CT image preprocessing, self-supervised lesion segmentation and quantification, multi-modal joint pre-training coding and multi-task diagnosis and prognosis prediction module composition which are sequentially and communicatively connected, and realizes feature interaction through a unified feature embedding space.The model realizes self-supervised lesion segmentation and multi-dimensional quantification by adopting a three-dimensional generative reconstruction network, completes image-text feature fine-grained alignment through cross-modal contrast learning of an adversarial enhancement, and fuses multi-dimensional features.The model can complete multiple tasks such as ARDS diagnosis, non-invasive estimation of P / F ratio, severity grading and prognosis prediction in parallel, reduces dependence on artificial labeling, improves cross-center generalization capability and diagnosis precision, and provides a standardized intelligent tool for clinical evaluation of severe lung inflammation.

The application discloses a diabetes patient biological age calculation method based on glycosylation markers and a multi-task deep learning framework, and belongs to the technical field of bioinformatics and medical data processing technology, and the method comprises the following steps: obtaining glycomics feature data and clinical examination data of diabetes patients; preprocessing the data; obtaining a feature set by adopting a multi-method screening and fusion strategy; constructing a deep learning model comprising a multi-modal feature fusion module and a multi-task learning module for training, wherein the multi-task learning module comprises a main task of aging clock regression and at least one auxiliary task; and outputting biological age prediction values and age acceleration evaluation results of individuals by using the trained model. The method realizes deep fusion of multi-modal biological data and exclusive modeling for the diabetes population, can accurately quantify the biological aging process and the deviation degree relative to the calendar age, and provides an objective quantitative tool for clinical evaluation.

This invention relates to the field of biomedical technology, specifically to the use of the N-terminal fragment of insulin-like growth factor binding protein-4 (IGF-4) in the clinical assessment of heart failure and coronary artery disease. The N-terminal fragment of IGF-4 is obtained by catalytic hydrolysis of IGF-4 by pregnancy-associated plasma protein A. Clinical assessment of heart failure includes diagnosing or assisting in the diagnosis of heart failure, or assessing or assisting in the prognostic risk of heart failure. Clinical assessment of coronary artery disease includes assessing or assisting in the prognostic risk of coronary artery disease. By detecting the content of the N-terminal fragment of IGF-4 in a sample, heart failure can be diagnosed or assisted in the diagnosis, and the prognosis of heart failure and coronary artery disease can be assessed or assisted in, demonstrating good clinical assessment efficacy.

This invention relates to an interactive display method for fetal visualization content, characterized by: acquiring user interaction signals or commands, and parsing the signals to determine the corresponding interaction type; adjusting the state parameters of the fetal visualization model, including posture parameters, action parameters, and performance parameters, according to the interaction type and a preset mapping relationship; and driving the fetal visualization model to generate continuous dynamic response content by combining structural constraints and cross-frame temporal consistency processing, and outputting it through a display terminal. This invention achieves dynamic response and continuous presentation of fetal visualization content by establishing a correlation mechanism between interaction signals and visualization model state parameters. It is suitable for non-medical visualization display scenarios based on image data, and the output content is not used for medical diagnosis or clinical evaluation.

Systems and methods for generating a patient-specific clinical assessment are disclosed. A processor receives patient health data corresponding to a patient. The patient health data is analyzed using a trained large language model (LLM) configured to process the patient health data. Clinical insights are determined based on the analysis of the patient health data using the trained LLM. A set of interactive prompts is generated for a patient interface based on the identified clinical insights. The set of interactive prompts may obtain additional information associated with the patient. A set of patient responses responsive to the generated set of interactive prompts are received. The clinical insights and the set of patient responses are displayed on a clinician interface.

The present invention relates to a mechatronic device for measuring hand strength and wrist joint movement, designed to record biomechanical variables generated during the interaction between a user's hand and a manipulable object. The device comprises a structural body with instrumented contact surfaces, at least one force sensor to detect the pressure exerted by the fingers, a multiaxial inertial sensor to record the dynamic movement of the device, an electronic processing unit for signal acquisition and processing, and a communication module to transmit the data to an external analysis system.The integration of force sensors and inertial sensors allows simultaneous measurement of grip strength and kinematic behavior associated with wrist movement during functional tasks, providing a technological tool for clinical evaluation, rehabilitation, and biomechanical research of the upper limb.

The application discloses an ophthalmic disease image classification system based on a big data model, which comprises a data set construction module, an image segmentation module and a model training module.The data set construction module is used for collecting eye images of patients with different ophthalmic diseases in an ophthalmic clinic and performing image marking processing to construct training and test data sets.The image segmentation module adopts YOLOv7 to perform image segmentation on the images taken by the smart phone to obtain eye images containing only the part below the eyebrows and above the zygomatic arch.The model training module adopts a five-fold cross-validation method to train the model on the images, and applies data enhancement technology, white balance adjustment and a transfer learning algorithm.The application relates to the technical field of ophthalmic disease image classification.The ophthalmic disease image classification system based on the big data model has high classification accuracy for diseases such as cataracts, keratitis and pterygium in the development stage test set and different clinical evaluation stages.

A clinical evaluation system and method are disclosed that facilitate the use of one or more morphologic atrial depolarization waveform-based features or parameters determined from biophysical signals such as cardiac or biopotential signals that are acquired, in preferred embodiments, non-invasively from surface sensors placed on a patient while the patient is at rest. Morphologic atrial depolarization waveform-based features or parameters can be used in a model or classifier to estimate metrics associated with the physiological state of a patient, including the presence or non-presence of a disease, medical condition, or an indication of either. The estimated metric may be used to assist a physician or other healthcare provider in diagnosing the presence or non-presence and / or severity and / or localization of diseases or conditions or in the treatment of said diseases or conditions.

The application relates to the field of intelligent medical treatment, in particular to a visual function recession quantitative evaluation method, system and device. Early detection and accurate evaluation of visual function recession are key links for delaying disease progression and reducing the risk of irreversible visual function damage, however, existing clinical evaluation faces many limitations. In model training, the application corrects dimension sensitivity shrinkage by a psychological cognitive state, explicitly injects disc anatomical partition into the fundus feature extraction process, and introduces a physiological consistency ordering constraint between the visual function recession grade and the retinal age, so that the model accurately evaluates early visual function recession. The application provides a more accurate quantitative evaluation method, which has important clinical value and industrial value.

The application provides a treatment scheme generation system and method based on iterative generation and feedback, and relates to the technical field of medical decision support, which comprises the following steps: in each iteration process, a planner generates a treatment scheme according to a given patient case and historical information retrieved from a memory, and a evaluator performs multidimensional clinical evaluation on the obtained treatment scheme, and outputs corresponding evaluation scores and evaluation reasons; the treatment scheme generated in each iteration, the corresponding evaluation scores and evaluation reasons are stored in the memory; when a preset stop condition is met, the treatment scheme with the highest evaluation score is selected from the treatment schemes generated in multiple iterations as the final treatment scheme output. The treatment scheme generation system and method based on iterative generation and feedback provided by the application realize accurate, comprehensive and safe generation of treatment schemes by introducing a multidimensional clinical evaluation system, a score-aware memory retrieval mechanism and a dynamic early stopping strategy.

Biomedicine, in particular an N-terminal fragment of insulin-like growth factor binding protein-4, can be used in the clinical assessment of heart failure and coronary heart disease.

The present application relates to the technical field of medical equipment and artificial intelligence, and particularly relates to a brain disease data intelligent evaluation method and system for carbon monoxide poisoning. The brain disease data intelligent evaluation method for carbon monoxide poisoning comprises the following steps: S1: a dynamic data acquisition framework covering the whole cycle of the disease is designed, and core data at key time points after carbon monoxide poisoning is collected, wherein the core data comprises multi-modal image data, clinical evaluation data and laboratory data; S2: a time series data analysis engine is constructed, multi-modal image data collected is subjected to space-time registration through the time series data analysis engine, batch extraction of key brain region microstructure parameters is performed, and dynamic trajectory data of each parameter is calculated, wherein the dynamic trajectory data comprises a current value, a change rate and an evolution mode of the parameter. Through construction of a dynamic data acquisition and intelligent analysis system covering the whole cycle of the disease, the present application realizes evaluation of the evolution process of brain injury after poisoning.

The application discloses a kind of continuous pelvic tilt posture under acetabular form evaluation method and system, belong to clinical evaluation technical field.Method includes: constructing pelvic three-dimensional model and femur three-dimensional model;Establish pelvic front plane anatomical reference coordinate system;Obtain the double hip axis of refined estimation;Get left and right two optimal acetabular opening planes, take the normal vector of left and right two optimal acetabular opening planes as the initial axis of corresponding side pelvic acetabulum;Construct the rigid body motion model of pelvic acetabular initial axis around the continuous rotation of double hip axis, calculate the change of acetabular axis direction under different pelvic tilt postures and corresponding acetabular anteversion angle, abduction angle;Establish acetabular abduction angle / anteversion angle curve under pelvic tilt, complete acetabular form evaluation.The application realizes the automatic calculation of acetabular space direction and accurate acetabular form evaluation, and lays a foundation for finding the most suitable patient acetabular prosthesis installation angle.

This invention provides a cardiac surgery nursing method and system based on multimodal data fusion. The method includes: acquiring multimodal monitoring data of a target patient within a preset time period, the multimodal monitoring data including physiological time-series data, clinical assessment text data, and medical imaging data; constructing a multidimensional fusion feature vector corresponding to the multimodal monitoring data based on the physiological time-series data, clinical assessment text data, and medical imaging data; acquiring historical multimodal monitoring data of the target patient within multiple historical time periods after surgery, calculating a deviation feature vector based on the historical multimodal monitoring data and the multidimensional fusion feature vector; calculating a disease deterioration trend index based on the deviation feature vector, and generating different nursing plans according to different disease deterioration trend indices. This invention identifies disease deterioration trends by constructing an individualized dynamic assessment system, transforming it into a graded nursing response, and realizing intelligent nursing from passive monitoring to proactive early warning, and from a unified plan to individualized decision-making.

This application relates to the field of radiopharmaceutical clinical evaluation technology, specifically providing a method, electronic device, and storage medium for evaluating the clinical efficacy of radiopharmaceuticals. The method includes the following steps: determining an activity function modeling method and contribution factors based on the composition and half-life of the drug to be evaluated; using the activity function modeling method to obtain the activity function of the tumor lesion region and the activity function of the risk organ based on spatiotemporal distribution data; calculating a first absorbed radiation dose based on the activity function of the tumor lesion region, the total drug activity, and the contribution factors corresponding to the tumor lesion region; calculating a second absorbed radiation dose based on the activity function of the risk organ, the total drug activity, and the contribution factors corresponding to the risk organ; and generating an efficacy evaluation index based on the ratio of the first absorbed radiation dose to the second absorbed radiation dose. This method can effectively reduce resource waste, shorten the new drug launch cycle, and prevent promising drugs from being shelved due to failure to pass Phase III clinical trials.

The present disclosure relates to the technical field of biomedicine, in particular the use of an N-terminal fragment of insulin-like growth factor binding protein-4 in the clinical assessment of heart failure and coronary heart disease.