Development of a real-time endoscopic image diagnosis support system using deep learning technology in colonoscopy

TLDR

Colonoscopy skill gaps, largely due to experience, necessitate new solutions. The study develops a real‑time AI system to automatically detect early colorectal cancer during colonoscopy, aiming to reduce missed lesions. The AI system achieved 97.3 % sensitivity and 99.0 % specificity overall, 98.0 % in polypoid and 93.7 % in non‑polypoid lesions, and can predict cancerous regions in 21.9 ms per image, supporting real‑time detection of often‑missed non‑polypoid polyps.

Abstract

Abstract Gaps in colonoscopy skills among endoscopists, primarily due to experience, have been identified, and solutions are critically needed. Hence, the development of a real-time robust detection system for colorectal neoplasms is considered to significantly reduce the risk of missed lesions during colonoscopy. Here, we develop an artificial intelligence (AI) system that automatically detects early signs of colorectal cancer during colonoscopy; the AI system shows the sensitivity and specificity are 97.3% (95% confidence interval [CI] = 95.9%–98.4%) and 99.0% (95% CI = 98.6%–99.2%), respectively, and the area under the curve is 0.975 (95% CI = 0.964–0.986) in the validation set. Moreover, the sensitivities are 98.0% (95% CI = 96.6%–98.8%) in the polypoid subgroup and 93.7% (95% CI = 87.6%–96.9%) in the non-polypoid subgroup; To accelerate the detection, tensor metrics in the trained model was decomposed, and the system can predict cancerous regions 21.9 ms/image on average. These findings suggest that the system is sufficient to support endoscopists in the high detection against non-polypoid lesions, which are frequently missed by optical colonoscopy. This AI system can alert endoscopists in real-time to avoid missing abnormalities such as non-polypoid polyps during colonoscopy, improving the early detection of this disease.