11results about How to "Strong clinical practicability" patented technology

The utility model relates to medical nursing field relates to a portable hip joint postoperative abduction lifting device, the utility model discloses: U type inflation slope pad and T type inflation body position pad, the U type inflation slope pad includes first air bag pad and second air bag pad, the first air bag pad top is equipped with the mouth that places, the second air bag pad sets up in the inside of first air bag pad high middle part gap, T type inflation body position pad sets up in U type inflation slope pad one side, T type inflation body position pad both sides are equipped with U -shaped groove, and U type inflation slope pad and T type inflation body position pad combine, satisfy the need after femur fracture operation, and clinical practicality is strong, can good help patient, prevent the dislocation caused by improper body position, improve patient's comfort, promote the disease recovery, and the inflation type structure is convenient to carry, and the double air bag setting in U type inflation slope pad satisfies the demand of different patients to the affected limb elevation.

This invention provides a deep learning-based intelligent analysis method for cardiovascular medical images, comprising the following steps: S1, heterogeneous data fusion and acquisition of multimodal medical images and physiological signals; S2, intelligent extraction and quantitative analysis of vascular structural features based on deep learning; S3, hemodynamic parameter modeling and risk region segmentation based on fused structural features; S4, texture feature mining and intelligent identification of lesion types in high-risk areas; S5, cardiovascular disease risk stratification prediction based on multi-dimensional feature fusion; S6, federated learning optimization and closed-loop update of the model. This invention's deep learning-based intelligent analysis method for cardiovascular medical images achieves, for the first time, heterogeneous fusion of medical images, millimeter-wave radar-PPG physiological signals, and clinical molecular data, and achieves precise spatiotemporal alignment through GPS-controlled crystal oscillators, overcoming the limitations of single image analysis and providing a more comprehensive feature foundation for subsequent feature extraction and risk prediction.

The application relates to the technical field of image processors, and discloses a tumor incisal edge design method based on a skin local image. The method comprises the following steps: establishing a global space coordinate system associated with the skin of a patient; collecting a local image and a six-degree-of-freedom pose by using a handheld device integrated with a pose tracking function; projecting and fusing the image to a digital surface model in real time to generate a panoramic image; segmenting a tumor boundary by using a deep learning model, and generating a safe incisal edge path by outward expansion based on a geometric algorithm; and finally projecting the incisal edge to the skin surface in the form of structured light by using a built-in projection unit of the device. The application realizes high-precision and low-delay dynamic planning and visualized guidance of the tumor incisal edge, and improves the precision and efficiency of the operation.

This invention discloses a prognostic model and construction method for cervical squamous cell carcinoma, belonging to the fields of bioinformatics and oncology. The construction method is based on a set of genes related to programmed cell death, integrating multi-omics data of cervical squamous cell carcinoma patients from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), and the Cancer Genome Characterization Project (CGCI). After standardized preprocessing, differential expression analysis, bootstrap resampling combined with univariate Cox regression for initial screening, consensus screening using multiple machine learning algorithms, and finally, multivariate Cox proportional hazards regression analysis to identify three core independent prognostic genes and construct a risk scoring model. This model has undergone multi-dimensional validation and optimization, demonstrating robust predictive performance. The method of this invention is standardized and highly reproducible, and the constructed model has high accuracy and strong generalization ability, providing a reliable tool for individualized prognostic assessment and clinical decision-making for cervical squamous cell carcinoma patients.

The application discloses a sleep apnea severity prediction method based on a single-channel blood oxygen signal and belongs to the technical field of medical artificial intelligence. The prediction method comprises the following steps: acquiring a single-channel blood oxygen saturation signal and demographic characteristics of a target object; performing preprocessing and signal feature extraction operations on the single-channel blood oxygen saturation signal to obtain a signal feature set of the target object; inputting the feature set of the target object into a trained double-branch joint sleep apnea severity prediction model based on multi-scale width learning; outputting a preliminary apnea hypopnea index estimation value through a regression branch; outputting an interval probability distribution through a classification branch; fusing features through a joint regression unit to obtain a final prediction value and a severity classification. The application can realize accurate prediction of sleep apnea severity based on a single-channel blood oxygen signal, has the advantages of low model complexity, strong interpretability and efficient operation on a CPU, and is suitable for clinical portable screening scenes.

This invention discloses a medical image analysis method for early screening of cardiovascular diseases, belonging to the field of medical image analysis technology. The method includes acquiring chest X-ray medical image data; inputting the preprocessed images into a hybrid architecture neural network model for feature extraction and analysis; employing a multi-scale dilated convolution structure and an axial attention module to enhance the extraction capability of pathological features at different scales and directions; aggregating multi-scale features through a pyramid pooling strategy; and outputting early screening results for cardiovascular diseases. This invention systematically solves the problems of existing methods in early screening of cardiovascular diseases, such as weak global modeling capabilities, heavy computational burden, insufficient multi-scale feature capture, and incomplete utilization of prior knowledge of medical image structures. Therefore, it significantly improves the detection rate and diagnostic accuracy of early lesions while achieving lightweight and efficient inference, providing reliable technical support for large-scale population screening and primary healthcare auxiliary diagnosis.

The application discloses a self-locking bracket unlocking tool, relates to the technical field of oral orthodontics, and comprises an operating rod, the two ends of the operating rod are connected with axially extending connecting rods, the end of one connecting rod is fixed with a working sheet, the working sheet extends in the axial direction of the operating rod and is inclined, the end of the other connecting rod is fixed with an arc-shaped sheet, and the arc opening of the arc-shaped sheet faces away from the connecting rod. The application has the advantages of simple structure, convenient and fast operation, difficulty in damage, strong clinical practicability, and suitability for any passive self-locking bracket, and does not need to frequently replace different instruments in work, and work efficiency is improved.

The application belongs to the technical field of nanometer hole single molecule detection and biochemistry, and provides a nanometer hole based on zebra fish protein and application thereof in single molecule detection. The zebra fish protein is wild type JAC5 protein, and the nanometer hole based on the wild type JAC5 protein is applied to single molecule detection. Compared with other nanometer hole proteins, the wild type JAC5 protein has significant advantages in cloning expression and purification process, and is suitable for detecting nucleic acid, protein and pathogen, and has wide application scenarios. Compared with the wild type JAC5 protein, the mutant zebra fish protein after amino acid mutation of the wild type JAC5 protein can normally express, enhance structural stability and improve detection performance by eliminating the steric hindrance of the sugar binding region to enhance the affinity, or by introducing positive charges to enhance the electrostatic interaction between the transmembrane region and the phospholipid bilayer membrane.

The present application relates to the field of orthopedic rehabilitation, and in particular to a progressive correction method for rheumatoid arthritis metacarpophalangeal joint ulnar deviation deformity, aiming at the technical pain points of existing correction technology, such as large trauma, poor adaptability, easy secondary synovial membrane injury, high deformity rebound rate, poor function recovery, etc., and combining biological microdynamics, flexible material science, industrial control time sequence regulation and physical rehabilitation technology, a non-invasive time sequence closed loop correction system is constructed. Through the multi-link collaborative implementation of preoperative global accurate evaluation and mechanical anchoring, low-temperature non-inductive fascia targeted pre-loosening, flexible phase change time sequence gradient force, extensor tendon targeted homing linkage, low-frequency micro-vibration microcirculation promotion, active micro-movement compensation balance training, etc., the pathological characteristics of rheumatoid arthritis and the physiological rhythm of the hand are matched throughout the whole process, and there is no rigid compression and surgical trauma. The present application can simultaneously achieve deformity reduction, ligament release, tendon homing, inflammation relief and muscle strength reconstruction, effectively avoid surgical risks and traditional brace complications, and significantly improve the correction success rate.

This invention provides a method and system for depicting brain midline CT images based on a 2.5D architecture and multiple attention mechanisms. The method includes: constructing a brain midline depiction network, namely GUP-Net (Global Uncertainty-Location Network), and introducing a three-dimensional spatial correction strategy, a Global Slice Attention (GSA) module, a Dynamic Uncertainty Awareness (DUA) module, and a Position Attention (PA) module; the GSA module is used to fuse cross-slice features to enhance key layer recognition; the DUA module optimizes feature extraction during downsampling to reduce lesion interference; the PA module improves spatial localization accuracy during upsampling; and the GUP-Net is trained using three-dimensionally corrected brain CT images to obtain the depiction model. This invention effectively solves the problems of distortion, blurring, and displacement of the brain midline caused by lesions, improving depiction accuracy and robustness.

This invention relates to the field of medical data analysis technology, specifically to a method for constructing an early warning model for urosepsis. The method includes: collecting multimodal time-series clinical data of patients; performing time-series alignment and imputation of missing values ​​on the data; fusing features from different modalities and time points based on a dynamic weight allocation mechanism to generate a comprehensive feature representation of the patient; employing a hierarchical cascade classification strategy, first using a lightweight rapid screening model to preliminarily identify high-risk patients, then applying a refined assessment model to conduct a detailed risk assessment of high-risk patients; and finally generating early warning signals and clinical decision recommendations. This invention effectively integrates multimodal time-series data, focuses on key information through dynamic weights and attention mechanisms, and balances screening efficiency and assessment accuracy by combining a two-stage classification strategy. It solves the problems of low sensitivity, neglect of dynamic time-series changes, and difficulty in clinical deployment in existing technologies, enabling early and accurate risk warning.