10 results about "Clinical value" patented technology

This invention discloses an automated multi-parameter assessment method, device, and medium for aortic imaging based on three-dimensional images. The method includes acquiring three-dimensional chest CT images and preprocessing them; extracting and optimizing an initial centerline based on the obtained three-dimensional vascular mask; dividing the initial centerline into ascending aorta, aortic arch, and descending aorta segments using the optimized initial centerline and the opening positions of branches on the aortic arch; extracting the average CT value of the adipose tissue surrounding the descending aorta based on the divided descending aorta segments; constructing orthogonal cross sections, performing dimensionality reduction and projection processing on each cross-sectional mask, and calculating the major and minor axes based on the farthest point pairs, outputting the major axis diameter sequence of each cross section; calculating the aortic calcification load, assessing aortic dilation and morphological variations, and evaluating the inflammatory status of the perivascular adipose tissue; and generating a comprehensive report of indicators. This invention fully utilizes existing imaging resources, improving the utilization efficiency and clinical value of medical imaging data.

The present application relates to the technical field of rectal cancer CTV segmentation, in particular to a rectal cancer CTV segmentation method based on CViL-UNet, comprising the following steps: preprocessing rectal cancer CT image data, converting a 3D CT sequence into a multi-channel 2D input format, removing image noise and intensity non-uniformity, and standardizing the pixel value range to adapt to the CViL-UNet model input. The present application realizes accurate identification of the key areas of CTV primary tumor, subclinical spread area and micro-infiltration focus through local-global feature cooperation and high-frequency detail enhancement, solves the pain points of missing micro-infiltration and misjudging healthy tissue in traditional methods, can process conventional CT images without intestinal preparation, breaks the dependence of traditional methods on ideal image conditions, and the automatic and real-time segmentation process in clinical value eliminates the subjectivity and inefficiency caused by manual intervention, while meeting the dual requirements of millimeter-level precision and rapid deployment for radiotherapy planning.

The application discloses a kind of based on dual-energy X-ray spinal osteoporosis grading diagnostic device and diagnostic method, dual-energy X-ray emission module: contain vertical and horizontal two groups of X-ray source (model: M200 microfocus X-ray tube, focal size ≤0.1mm), vertical group is set in the top central of scanning bed (120cm from bed surface), horizontal group is symmetrically set in the scanning bed both sides rack (80cm from bed surface, interval 60cm);The application not only solves the core defects of prior art in diagnostic accuracy, safety, practicality, but also expands the application scenario through portability, intelligent design, provides a new technical scheme for the precise grading diagnosis and standardized treatment of spinal osteoporosis, with significant clinical value and market prospect.

This invention discloses a multifunctional non-contact three-dimensional cardiac electrophysiological mapping system based on intracardiac ultrasound (ICE), comprising an ICE system, an ultrasound host, an ECG signal acquisition system, and a three-dimensional spatial positioning system. By adjusting the position and imaging angle of the ICE probe, it continuously acquires cardiac ultrasound signals, as well as heartbeat and respiratory signals, to synchronously acquire ultrasound signals at different cardiac cycles. Cross-correlation calculations are performed on the continuous ultrasound signals acquired at various spatial locations to obtain the three-dimensional cardiac excitation conduction sequence and maximum amplitude distribution within a cardiac cycle. This system can perform both intracardiac ultrasound imaging and non-contact three-dimensional electrophysiological mapping, reducing the use of catheters and medical costs in traditional contact electrophysiological interventional procedures, simplifying surgical operations, and improving surgical safety. Furthermore, it can simultaneously map the endocardium, intramyocardium, and epicardium, enabling more precise surgical planning and possessing significant clinical value.

Systems and methods for establishing a patient's current or future clinical or lab values are provided. A neural network is trained on a dataset of medical images, such as ultrasound images, that are tagged with information concerning the lab values of people who were imaged to produce the medical images. The trained neural network can then be provided with medical images of a patient, and the neural network can then make a determination as to the patient's current or future clinical or lab values.

The application discloses a method, system, device, medium and product for evaluating right ventricular function, and relates to the technical field of medical image processing and ultrasonic diagnosis. The method comprises the following steps: acquiring a sequence of multiple two-dimensional ultrasonic images of a right ventricle of a heart and spatial pose information of the sequence, and generating point cloud data; identifying an end-diastolic frame, an end-systolic frame and an anatomical structure point, and acquiring sparse point cloud data of the anatomical structure point; constructing a three-dimensional grid model of the end-diastolic frame and the end-systolic frame; determining multiple tracking points of a tricuspid annulus and acquiring a three-dimensional coordinate trajectory; calculating longitudinal displacement, area change rate and peak time dispersion based on the trajectory, and completing function evaluation. The application overcomes the limitations of angle dependence and point substitution of traditional TAPSE technology, realizes comprehensive evaluation of right ventricular function from three dimensions of longitudinal displacement, area change rate and systolic synchrony, and improves the accuracy and clinical value of evaluation.

The application discloses a heterogeneous data format automatic conversion and standardization processing method for monitors / anaesthetic machines, and relates to the technical field of medical data processing.The method comprises the following steps: S1, constructing a hierarchical surgery whole-process scene label system based on multi-center time sequence data and clinical value dynamic calibration correlation strength, combing the core clinical stages of surgery and setting exclusive scene labels, associating corresponding clinical operations, core monitoring parameters and data precision requirements for each label, and establishing a stage correlation mapping relationship between labels to support cross-stage dynamic flow; the core technology combination of the scene label system construction-real-time scene recognition-dynamic priority adjustment-unified standardization conversion solves the pain points of the insufficient adaptation of existing general conversion technology, and the hierarchical scene label system constructed by the application covers the induction period, maintenance period and recovery period of anesthesia, and can be associated with clinical operations, core parameters and precision requirements.

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.

This application discloses a home care system and procedure for postoperative care of gynecological malignant tumors. The method includes: determining patient information for the patient requiring care, including physiological data and postoperative data related to the malignant tumor; determining the real-time health status of the patient based on the physiological data and postoperative data; inputting the real-time health status into a preset nursing method atlas library to determine the corresponding home care method for the patient; acquiring personalized data for the patient and revising the home care method based on the personalized data to obtain the target home care method for the patient. This application can significantly improve the scientific rigor, safety, and patient compliance of home care, providing more clinically valuable and individualized rehabilitation support for postoperative patients with malignant tumors.

The application relates to the field of intelligent medical treatment, in particular to a children propofol anesthesia state monitoring method, device and program product based on a sensitive awakening index. The method comprises the following steps: acquiring electroencephalogram data and heart rate data of children injected with propofol; performing spectrum calculation on the electroencephalogram data to obtain a spectrum beta ratio, and calculating a heart rate variation degree through the heart rate data; performing anesthesia awakening index prediction through the spectrum beta ratio and the heart rate variation degree; and evaluating the anesthesia state of children through the predicted anesthesia awakening index. The application can sensitively detect the change of the anesthesia state of children and has good clinical value.