Scandinavian journal of gastroenterology · 2022
Impact of artificial intelligence on colorectal polyp detection for early-career endoscopists: an international comparative study.
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Ainechi Diba, Misawa Masashi, Barua Ishita, Larsen Solveig Linnea Veen, Paulsen Vemund, Garborg Kjetil Kjeldstad, Aabakken Lars, Tønnesen Christer Julseth, Løberg Magnus, Kalager Mette, Kudo Shin-Ei, Hotta Kinichi, Ohtsuka Kazuo, Saito Shoichi, Ikematsu Hiroaki, Saito Yutaka, Matsuda Takahisa, Itoh Hayato, Mori Kensaku, Bretthauer Michael, Mori Yuichi
Аннотация
BACKGROUND: Artificial intelligence (AI) for polyp detection is being introduced to colonoscopy, but there is uncertainty how this affects endoscopists' ability to detect polyps and neoplasms. We performed a video-based study to address whether AI improved the endoscopists' performance to detect polyps.
METHODS: We established a dataset of 200 colonoscopy videos (length 5 s; 100 without polyps and 100 with one polyp). About 33 early-career endoscopists (50-400 colonoscopies performed) from 10 European countries classified each video as either 'polyp present' or 'polyp not present'. The video assessment was performed twice with a four-week interval. The first assessment was performed without any AI tool, whereas the second was performed with an AI tool for polyp detection. The primary endpoint was early-career endoscopists' sensitivity to detect polyps. Gold standard for presence and histology of polyps were confirmed by two expert endoscopists and pathologists, respectively. McNemar's test was used for statistical significance.
RESULTS: There were 86 neoplastic and 14 non-neoplastic polyps (mean size 5.6 mm) in the 100 videos with polyps. Early-career endoscopists' sensitivity to detect polyps increased from 86.3% (95% confidence interval [CI]: 85.1-87.5%) to 91.7% (95%CI: 90.7-92.6%) with the AI aid (p < .0001). Their sensitivity to detect neoplastic polyps increased from 85.4% (95% CI: 84.0-86.7%) to 92.1% (95%CI: 91.1-93.1%) with the AI aid (p < .0001).
CONCLUSION: The polyp detection AI tool helped early-career endoscopists to increase their sensitivity to identify all polyps and neoplastic polyps during colonoscopy.
METHODS: We established a dataset of 200 colonoscopy videos (length 5 s; 100 without polyps and 100 with one polyp). About 33 early-career endoscopists (50-400 colonoscopies performed) from 10 European countries classified each video as either 'polyp present' or 'polyp not present'. The video assessment was performed twice with a four-week interval. The first assessment was performed without any AI tool, whereas the second was performed with an AI tool for polyp detection. The primary endpoint was early-career endoscopists' sensitivity to detect polyps. Gold standard for presence and histology of polyps were confirmed by two expert endoscopists and pathologists, respectively. McNemar's test was used for statistical significance.
RESULTS: There were 86 neoplastic and 14 non-neoplastic polyps (mean size 5.6 mm) in the 100 videos with polyps. Early-career endoscopists' sensitivity to detect polyps increased from 86.3% (95% confidence interval [CI]: 85.1-87.5%) to 91.7% (95%CI: 90.7-92.6%) with the AI aid (p < .0001). Their sensitivity to detect neoplastic polyps increased from 85.4% (95% CI: 84.0-86.7%) to 92.1% (95%CI: 91.1-93.1%) with the AI aid (p < .0001).
CONCLUSION: The polyp detection AI tool helped early-career endoscopists to increase their sensitivity to identify all polyps and neoplastic polyps during colonoscopy.