Detecting colon polyps in endoscopic images using artificial intelligence constructed with automated collection of annotated images from an endoscopy reporting system. Issue 5 (30th November 2021)

• Record Type: Journal Article
• Authors: Hori, Keisuke; Ikematsu, Hiroaki; Yamamoto, Yoichi; Matsuzaki, Hiroki; Takeshita, Nobuyoshi; Shinmura, Kensuke; Yoda, Yusuke; Kiuchi, Takayoshi; Takemoto, Satoko; Yokota, Hideo; Yano, Tomonori
• Is Part Of: Digestive endoscopy. Volume 34:Issue 5(2022)
• Page Start: 1021
• Page End: 1029
• Publication Date: 2021-11-30
• Subjects: artificial intelligence -- automated collection system -- colon polyps -- endoscopic images; Digestive organs -- Diseases -- Periodicals; Digestive organs -- Diseases -- Diagnosis -- Periodicals; Endoscopy -- Periodicals; Digestive System Diseases -- diagnosis -- Periodicals; Digestive System Diseases -- therapy -- Periodicals; Endoscopy -- Periodicals; 616.3
• DOI: 10.1111/den.14185
• Languages: English
• ISSNs: 0915-5635

Abstract

Abstract : Background: Artificial intelligence (AI) has made considerable progress in image recognition, especially in the analysis of endoscopic images. The availability of large‐scale annotated datasets has contributed to the recent progress in this field. Datasets of high‐quality annotated endoscopic images are widely available, particularly in Japan. A system for collecting annotated data reported daily could aid in accumulating a significant number of high‐quality annotated datasets. Aim: We assessed the validity of using daily annotated endoscopic images in a constructed reporting system for a prototype AI model for polyp detection. Methods: We constructed an automated collection system for daily annotated datasets from an endoscopy reporting system. The key images were selected and annotated for each case only during daily practice, not to be performed retrospectively. We automatically extracted annotated endoscopic images of diminutive colon polyps that had been diagnosed (study period March–September 2018) using the keywords of diagnostic information, and additionally collect the normal colon images. The collected dataset was devised into training and validation to build and evaluate the AI system. The detection model was developed using a deep learning algorithm, RetinaNet. Results: The automated system collected endoscopic images (47, 391) from colonoscopies (745), and extracted key colon polyp images (1356) with localized annotations. The sensitivity, specificity, and accuracy of our AI model were 97.0%, 97.7%, and 97.3% ( n = 300), respectively. Conclusion: The automated system enabled the development of a high‐performance colon polyp detector using images in endoscopy reporting system without the efforts of retrospective annotation works.