9results about How to "Avoid misdiagnosis" patented technology

The utility model relates to the technical field of livestock medical equipment, and disclose livestock veterinarian multifunctional animal holding frame, including base, the surface fixedly connected with support frame at the four corners of base upper end, and the outer surface hinged connection of base one side is connected with the treading plate, the upper end of support frame is provided with the holding frame, and one end of holding frame is provided with the open -and -shut structure. The utility model discloses by pulling down the pull rod, make first hinged link and second hinged link hinged rotation, and drive first positioning frame hinged rotation and reset, when the other end of first hinged link and hinged block hinged rotation, and drive the other end of hinged block and go up and hinged rotation, one end of hinged block drives second positioning frame hinged rotation and reset simultaneously, thereby can bind the head of animal, avoid the condition that animal struggles or escapes because of discomfort or panic, and the practicability of equipment is improved to some extent.

The application discloses an ELISA kit for detecting anti-human laminin gamma 1 IgA autoantibody and application thereof. The kit comprises an enzyme-labeled plate coated with human laminin gamma 1 recombinant protein. Preferably, the human laminin gamma 1 recombinant protein is deglycosylated human laminin gamma 1 recombinant protein. Further, the application also provides application of the kit in preparation of a reagent for diagnosing mucous membrane pemphigoid, in particular Laminin gamma 1 IgA type pemphigoid. The kit is the first kit for detecting human laminin gamma 1 IgA antibody by using the ELISA method, has the advantages of simple operation, short time consumption and low cost compared with traditional immunoblotting method, and adopts full-length deglycosylated laminin gamma 1 recombinant protein as an antigen, thereby solving the problem of incomplete antigen epitope coverage in the prior art, and early detection of diseases can be realized, early treatment is achieved, and the life quality of patients is improved.

This invention relates to a machine vision-based system and method for recognizing and assessing gait abnormalities in children, belonging to the field of action recognition, and more specifically to the field of recognizing human actions or behaviors in image or video data. The system includes: a model building device for building an AI gait abnormality recognition and assessment model corresponding to a side-view imaging device; and an intelligent assessment device for intelligently assessing, based on selectively chosen multi-source basic data, whether the child currently being assessed has gait / crawling abnormalities and the number of months of such abnormalities. This invention addresses the technical problem of simultaneously failing to balance the quality and efficiency of gait / crawling abnormality assessment and the inability to simultaneously provide the number of months of abnormalities. It employs a custom-designed AI gait abnormality recognition and assessment model, based on selectively chosen multi-source basic data, to simultaneously complete both the status assessment and the assessment of the number of months of abnormalities, thereby solving the aforementioned technical problems.

This utility model discloses a fully automated biochemical analyzer, including a biochemical analyzer body. A top cover is rotatably connected to the top of the analyzer body. Mounting plates are symmetrically fixed to one side of the top cover. A collection tube is fixedly installed between the two mounting plates. A water inlet pipe is fixedly installed to one side of the collection tube. Multiple diverter heads are installed on one side of each collection tube, and a spray head is fixedly installed at the bottom of each collection tube. This design, by setting up a collection tube, water inlet pipe, diverter heads, spray heads, and ultraviolet disinfection lamp, uses the water inlet pipe to transmit water through the collection tube to the interior of the diverter heads. The spray heads spray disinfectant water onto the top of the partition, disinfecting the interior of the biochemical analyzer body. The ultraviolet disinfection lamp then dries and disinfects the disinfectant water, effectively reducing cross-contamination between samples, ensuring the independence of each sample, and avoiding misdiagnosis or missed diagnosis due to contamination.

This invention provides a low-dose CT image reconstruction method, belonging to the field of medical image processing technology. The method is characterized by the following steps: Step A, obtaining two different low-dose, high-noise CT image data, one as input image I and the other as a self-supervised image T; Step B, randomly dividing input image I into proportionally sized image blocks; Step C, randomly removing 60% of the proportionally sized image blocks from Step B, randomly sorting the remaining 40% of the image blocks, and inputting the sorted image block sequence position information into the algorithm model; Step D, after reconstruction by the algorithm model, obtaining the reconstructed image pairs and calculating the L1 loss with T. This reconstruction method requires convenient image data acquisition, allows for active adjustment of the image noise reduction ratio, and the reconstructed image does not reduce the image signal-to-noise ratio, nor weaken details and textures, resulting in high image quality.

A SiC MOSFET gate oxide layer degradation comprehensive diagnosis method and system, the method comprises the following steps: constructing a data set, and training to obtain a junction temperature estimation function; obtaining actual precursor parameters under actual operating conditions through a measurement circuit, substituting the actual precursor parameters into the junction temperature estimation function to obtain a current actual junction temperature estimation value, substituting the current actual junction temperature estimation value into a linear relationship between the precursor parameters and the junction temperature before the gate oxide layer degrades to obtain the flat band voltage and the threshold voltage of the gate oxide layer without degradation at the junction temperature; calculating the difference between the actual precursor parameters and the precursor parameters at the junction temperature without degradation of the gate oxide layer to obtain a precursor parameter drift amount, and performing comprehensive diagnosis of the gate oxide layer degradation according to the precursor parameter drift amount to obtain a comprehensive diagnosis result. The present application solves the misdiagnosis problem caused by temperature drift in the prior art, improves the accuracy and robustness of the degradation diagnosis, and realizes real-time monitoring and thermal state management of the device working junction temperature state.

The application discloses a kind of based on digital pathology section local high-precision magnifying glass system, comprising: main view module, for loading and display digital pathology whole section image;Magnifying glass module, including nested sub-viewport, for specified local area in the current field of view of the main view module is carried out high-power display;Wherein, the magnifying glass module is shared with the main view module by cloning mechanism Image resources;Display area layer, which is overlaid on the display interface of the main view module, is used to visually mark the corresponding position of the local area in the main view module;Control handle layer, integrated in the display area layer, is used to receive user input to interactively adjust the local area, realize local magnification based on original high-resolution data;Non-repeated loading of image resources significantly reduces server and network pressure;Main view and local view are highly consistent, to avoid misdiagnosis.

This invention relates to the field of material damage diagnosis technology and discloses a hierarchical diagnosis system for aircraft material damage based on transfer learning. The system includes first and second data acquisition units, a mechanism-driven bridging transformation module, a lightweight feature extraction network, a hierarchical diagnosis module, and an output module. The first unit acquires labeled source domain damage signals from the laboratory, while the second unit only acquires target aircraft health status scan data. The mechanism-driven module extracts the damage evolution invariant manifold, minimizes the Wasserstein distance between the source domain and the target domain health distribution, and trains a cross-model transferable projection matrix. The lightweight network outputs transfer features, which are then processed by a cascaded three-level sub-network for anomaly detection, type recognition, and level evaluation, combined with a physical constraint rule base to output the existence, type, and levels 1-4 of the damage. This invention achieves high-precision hierarchical diagnosis across models without requiring labeled samples of damage in the target domain and has online adaptive capabilities.