Clin Endosc.  2025 Mar;58(2):327-330. 10.5946/ce.2024.213.

Usefulness of an artificial intelligence-based colonoscopy report generation support system

• Naito T
• Nosaka T
• Tanaka T
• Akazawa Y
• Takahashi K
• Ohtani M
• Nakamoto Y

• KMID: 2566156
• DOI: http://doi.org/10.5946/ce.2024.213

Figure

• 

  Fig. 1. Flow chart of the AR-C1 system (FUJIFILM Corp.) used during colonoscopy. (A) AR-C1 automatically recognizes insertion and removal of the endoscope and use of instruments such as snares and biopsy forceps during the examination. (B) At the completion of the examination, details of the procedure recognized by AR-C1, and pictures of the procedure as well as the specimen number and site are temporarily registered on the report card. (C) Contents of the report card.

• 

  Fig. 2. (A) Report generation time per case without using AR-C1 (without artificial intelligence [AI]) and using AR-C1 (with AI). (B) Report generation time per 100 characters without using AR-C1 (without AI) and using AR-C1 (with AI).

Reference

1. Choi J, Shin K, Jung J, et al. Convolutional neural network technology in endoscopic imaging: artificial intelligence for endoscopy. Clin Endosc. 2020; 53:117–126.

2. Hassan C, Misawa M, Rizkala T, et al. Computer-aided diagnosis for leaving colorectal polyps in situ : a systematic review and meta-analysis. Ann Intern Med. 2024; 177:919–928.

3. Djinbachian R, Haumesser C, Taghiakbari M, et al. Autonomous artificial intelligence vs artificial intelligence-assisted human optical diagnosis of colorectal polyps: a randomized controlled trial. Gastroenterology. 2024; 167:392–399.

4. Park JB, Bae JH. Effectiveness of a novel artificial intelligence-assisted colonoscopy system for adenoma detection: a prospective, propensity score-matched, non-randomized controlled study in Korea. Clin Endosc. 2025; 58:112–120.

5. Lou S, Du F, Song W, et al. Artificial intelligence for colorectal neoplasia detection during colonoscopy: a systematic review and meta-analysis of randomized clinical trials. EClinicalMedicine. 2023; 66:102341.

6. Lux TJ, Saßmannshausen Z, Kafetzis I, et al. Assisted documentation as a new focus for artificial intelligence in endoscopy: the precedent of reliable withdrawal time and image reporting. Endoscopy. 2023; 55:1118–1123.

7. Rey JF. As how artificial intelligence is revolutionizing endoscopy. Clin Endosc. 2024; 57:302–308.

8. Taghiakbari M, Hamidi Ghalehjegh S, Jehanno E, et al. Automated detection of anatomical landmarks during colonoscopy using a deep learning model. J Can Assoc Gastroenterol. 2023; 6:145–151.

9. Kim BS, Cho M, Chung GE, et al. Density clustering-based automatic anatomical section recognition in colonoscopy video using deep learning. Sci Rep. 2024; 14:872.

10. Gong EJ, Bang CS. Revolutionizing gastrointestinal endoscopy: the emerging role of large language models. Clin Endosc. 2024; 57:759–762.