A method for real-time identification of lesions based on digestive endoscopy

Abstract

The invention relates to a method for real-time identification of lesions based on digestive endoscopy, comprising the following steps: processing artificial intelligence algorithms to obtain preliminary lesion results inside the digestive tract; online filtering algorithms to remove potential false positives and false positives in the preliminary lesion results to obtain effective Lesion result; the sliding window mechanism calculates the lesion ratio value of the effective lesion result within a period of time; the exponential moving average algorithm calculates the average lesion degree of suspicion through the lesion ratio value; based on the average lesion degree of suspicion, an alarm and a screenshot of the lesion area are displayed; the present invention is After the lesion results are identified based on the artificial intelligence algorithm, the online filtering algorithm, the sliding window calculation of the lesion ratio value and the average lesion suspicion algorithm are used to post-process the lesion results to highlight the real lesions to improve accuracy and reduce the false alarm rate , improve the work efficiency of doctors, and greatly improve the user experience, and the algorithm is calculated by CPU, which has no impact on the real-time performance of the system.

Description

technical field [0001] The invention belongs to a method for identifying lesions, in particular to a method for identifying lesions in real time based on digestive endoscopy. Background technique [0002] Artificial intelligence is being widely used in various fields of medical and health care. Among them, in the application of artificial intelligence algorithms for real-time lesions (for example, polyps, ulcers, cancer) identification of gastrointestinal endoscopic surgery videos, the current algorithms are generally The pictures in the video stream are processed frame by frame, analyzed, and the results are displayed. The analysis results are affected by various factors such as the amount of data trained by its artificial intelligence algorithm, the accuracy of data annotation, algorithm selection and optimization, etc. [0003] In the clinical environment, due to the different shapes of lesions in the digestive tract, folds of the intestinal wall similar to lesions, possi...

AI Technical Summary

Problems solved by technology

[0003] In the clinical environment, due to the different shapes of lesions in the digestive tract, folds of the intestinal wall similar to lesions, possible foreign bodies (feces, blisters), image quality (motion blur, lens out of focus, light spots, reflections), etc., artificial The accuracy of intelligent algorithms will decrease

[0004] The usual practice in medical applications is to improve the sensitivity of the algorithm and reduce false negatives through tuning, but at the same time it will also lead to a decrease in the specificity of the algorithm, and more false positives and false positives will be generated in the actual clinical examination process. Judging the non-lesion area as the lesion area, these false positives and false positives require doctors to consume extra attention to eliminate them, thus increasing the work intensity and workload of doctors

Images