The study on artificial intelligence (AI) colonoscopy in affecting the rate of polyp detection in colonoscopy: A single centre retrospective study

• Published in: Surgical practice 2022-05, Vol.26 (2), p.115-119
• Main Authors: Wong, Yuen Ting, Tai, Tze Fung, Wong, Ka Fai, Leung, Siu Kee, Lam, Shuk Man, Wong, Shun Yi, Lo, Yik Yu, Yan, Kit Man, Tam, Shuk Kwan, Wong, Man Fan, Chan, Hing Lung
• Format: Article
• Language: English
• Subjects: Artificial intelligence; Colonoscopy; Colorectal cancer
• ISSN: 1744-1625; 1744-1633
• DOI: 10.1111/1744-1633.12559

Aim The aim of this study was to evaluate if the application of Artificial Intelligence (AI) Colonoscopy CLN (ENDO‐AID) could increase the polyp detection rate (PDR). Methods and Materials A single center retrospective study was performed in Tin Shui Wai Hospital. PDR in CLN from 11/2020 to 03/2021 after the application of ENDO‐AID (AI group) was compared to the cases from 12/2019 to 11/2020 before the application of the practice (non‐AI group). Procedures were performed by a single endoscopist with experience in performing > 3,000 CLN. Variables, such as patients' demographic data, indications, incidence of PDR, Boston Bowel Preparation Scale BBPS, withdrawal time, post CLN complication rate between the 2 groups, were compared. Categorical and continuous variables were analyzed by using the Chi‐Square test (Fisher exact test if appropriate) and Mann‐Whitney test respectively. Results were considered to be significant if p‐value < 0.05. Results Total 234 patients were recruited. 115 patients (49.1%) were in the non‐AI group while 119 patients (50.9%) were in the AI group. The mean age of the non‐AI was higher than the AI group (65.3 vs 59.2, p< 0.001*), otherwise, there was no significant difference in sex (p = 0.05), percentage of smokers (20.8% vs 27.7%, p = 0.22), past medical history of IBD (0 vs 0, p = 1.0), family history of colorectal cancer (9 vs 9, p = 0.94), indications for CLN (e.g. follow up CLN for polyp/ cancer, per‐rectal bleeding, altered bowel habit etc. p > 0.05), BBPS (7.88 vs 8.04, p = 0.217), withdrawal time (7.65 min vs 7.48 min, p = 0.935), completion rate (95.6% vs 98.3%, p = 0.27) and complication rate (0% in both groups,p=1.0) between groups. In the contrary, PDR was significantly higher in the AI group than the non‐AI group (64.7% vs 46.0%, p = 0.003*). Besides, adenoma detection rate was also found significantly higher in the AI group than the non‐AI group (52.9% vs 37.4%, p = 0.017*). Conclusions AI CLN can improve the PDR.