Deep learning-based digestive tract mucosa staining detection system and method

Abstract

The invention discloses a deep learning-based digestive tract mucosa staining detection system and method. The system comprises an image acquisition device, a host, an intelligent prompt device, a staining device and a staining action capture device, the output end of the image collecting device is connected with the input end of the host, the output end of the host is connected with the input end of the intelligent prompting device, the output end of the intelligent prompting device is connected with the input end of the staining device, and the staining output end of the staining device outputs staining medicine; and the signal output end of the staining device outputs a staining action signal to the staining action capturing device. On the basis of following a clinical guide or expert consensus for screening the early cancer of the digestive tract, the system and method can be suitable for basic medical institutions and most of existing gastrointestinal endoscopes, and can guide, supervise and urge doctors to do digestive endoscopy examination and improve the examination and diagnosis level and efficiency of the doctors.

Description

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AI Technical Summary

Benefits of technology

This patented device helps healthcare providers inspect their patients during an upper abdomen procedure without having them wait until they are fully excreted outdoors afterward. It uses advanced imagery techniques like computer tomography (CT) scans to identify diseases such as cancerous tissue hidden within bone structures called lymph nodes. These images help make accurate assessments about how well these organoid-affected areas were being treated while minimizing patient discomfort from unnecessary treatment procedures. Overall this innovation enhances the accuracy and effectiveness of colonoscope testing systems used at home settings.

Problems solved by technology

This patented technical solution described by this patents relates to improving diagnoses caused by regular inspections performed during surgery or other procedures where there may be hidden areas inside organs like bowels lining up behind them. These techniques involve analyzing grayscale images taken from different angles - called histology analysis (HCA) data obtained through X ray fluorescence scanning). By comparing these graylevel patterns against standard histograms stored within databases, they are able to identify specific abnormalities associated with diseases related to inflammatory processes involved in digestion.

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