Use of artificial intelligence in submucosal vessel detection during third-space endoscopy

Abstract

Background

While artificial intelligence (AI) shows high potential in decision support for diagnostic gastrointestinal endoscopy, its role in therapeutic endoscopy remains unclear. Third-space endoscopic procedures pose the risk of intraprocedural bleeding. Therefore, we aimed to develop an AI algorithm for intraprocedural blood vessel detection.

Methods

Using a test dataset of 101 standardized video clips containing 200 predefined submucosal blood vessels, 19 endoscopists were evaluated for vessel detection rate (VDR) and time (VDT) with and without support of an AI algorithm. Endoscopists were grouped according to experience in endoscopic submucosal dissection.

Results

With AI support, endoscopist VDR increased from 56.4% (95%CI CI 54.1–58.6) to 72.4% (95%CI CI 70.3–74.4). Endoscopist VDT dropped from 6.7 seconds (95%CI 6.2–7.1) to 5.2 seconds (95%CI 4.8–5.7). False-positive readings appeared in 4.5% of frames and were marked for a significantly shorter time than true positives (0.7 seconds [95%CI 0.55–0.87] vs. 6.0 seconds [95%CI 5.28–6.70]).

Conclusions

AI improved the VDR and VDT of endoscopists during third-space endoscopy. While these data need to be corroborated by clinical trials, AI may prove to be an invaluable tool for improving safety and speed of endoscopic interventions.

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