Artificial intelligence-assisted colonic endocytoscopy for cancer recognition: a multicenter study

Yuichi Mori; Shin-ei Kudo; Masashi Misawa; Kinichi Hotta; Ohtsuka Kazuo; Shoichi Saito; Hiroaki Ikematsu; Yutaka Saito; Takahisa Matsuda; Takeda Kenichi; Toyoki Kudo; Tetsuo Nemoto; Hayato Itoh; Kensaku Mori

doi:10.1055/a-1475-3624

Endoscopy International Open, Inhaltsverzeichnis

CC BY-NC-ND 4.0 · Endosc Int Open 2021; 09(07): E1004-E1011
DOI: 10.1055/a-1475-3624

Original article

Artificial intelligence-assisted colonic endocytoscopy for cancer recognition: a multicenter study

Yuichi Mori

¹Clinical Effectiveness Research Group, Institute of Health and Society, University of Oslo, Oslo, Norway

²Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan

,

Shin-ei Kudo

²Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan

,

Masashi Misawa

²Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan

,

Kinichi Hotta

³Division of Endoscopy, Shizuoka Cancer Center Hospital, Shizuoka, Japan

,

Ohtsuka Kazuo

⁴Department of Endoscopy, Tokyo Medical and Dental University, Tokyo, Japan

,

Shoichi Saito

⁵Department of Gastroenterology, The Cancer Institute Hospital, Tokyo, Japan

,

Hiroaki Ikematsu

⁶Department of Gastroenterology and Endoscopy, National Cancer Center Hospital East, Kashiwa, Japan

,

Yutaka Saito

⁷Endoscopy Division, National Cancer Center Hospital, Tokyo, Japan

,

Takahisa Matsuda

⁷Endoscopy Division, National Cancer Center Hospital, Tokyo, Japan

⁸Cancer Screening Center, National Cancer Center Hospital, Tokyo, Japan

⁹Division of Screening Technology, Center for Public Health Sciences, National Cancer Center, Tokyo, Japan

,

Takeda Kenichi

²Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan

,

Toyoki Kudo

²Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan

,

Tetsuo Nemoto

¹⁰Department of Diagnostic Pathology, School of Medicine, Showa University, Yokohama Northern Hospital, Kanagawa, Japan

,

Hayato Itoh

¹¹Graduate School of Informatics, Nagoya University, Nagoya, Japan

,

Kensaku Mori

¹¹Graduate School of Informatics, Nagoya University, Nagoya, Japan

› Institutsangaben

Abstract

Background and study aims Large adenomas are sometimes misidentified as cancers during colonoscopy and are surgically removed. To address this overtreatment, we developed an artificial intelligence (AI) tool that identified cancerous pathology in vivo with high specificity. We evaluated our AI tool under the supervision of a government agency to obtain regulatory approval.

Patients and methods The AI tool outputted three pathological class predictions (cancer, adenoma, or non-neoplastic) for endocytoscopic images obtained at 520-fold magnification and previously trained on 68,082 images from six academic centers. A validation test was developed, employing 500 endocytoscopic images taken from various parts of randomly selected 50 large (≥ 20 mm) colorectal lesions (10 images per lesion). An expert board labelled each of the 500 images with a histopathological diagnosis, which was made using endoscopic and histopathological images. The validation test was performed using the AI tool under a controlled environment. The primary outcome measure was the specificity in identifying cancer.

Results The validation test consisted of 30 cancers, 15 adenomas, and five non-neoplastic lesions. The AI tool could analyze 83.6 % of the images (418/500): 231 cancers, 152 adenomas, and 35 non-neoplastic lesions. Among the analyzable images, the AI tool identified the three pathological classes with an overall accuracy of 91.9 % (384/418, 95 % confidence interval [CI]: 88.8 %–94.3 %). Its sensitivity and specificity for differentiating cancer was 91.8 % (212/231, 95 % CI: 87.5 %–95.0 %) and 97.3 % (182/187, 95 % CI: 93.9 %–99.1 %), respectively.

Conclusions The newly developed AI system designed for endocytoscopy showed excellent specificity in identifying colorectal cancer.

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