| Autor: |
Satoshi Sugino, Naohisa Yoshida, Zhe Guo, Ruiyao Zhang, Ken Inoue, Ryohei Hirose, Osamu Dohi, Yoshito Itoh, Daiki Nemoto, Kazutomo Togashi, Hironori Yamamoto, Xin Zhu |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
|
| Zdroj: |
Journal of the Anus, Rectum and Colon, Vol 8, Iss 3, Pp 212-220 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2432-3853 |
| DOI: |
10.23922/jarc.2023-070 |
| Popis: |
Objectives: Artificial intelligence (AI) with white light imaging (WLI) is not enough for detecting non-polypoid colorectal polyps and it still has high false positive rate (FPR). We developed AIs using blue laser imaging (BLI) and linked color imaging (LCI) to detect them with specific learning sets (LS). Methods: The contents of LS were as follows, LS (WLI): 1991 WLI images of lesion of 2-10 mm, LS (IEE): 5920 WLI, BLI, and LCI images of non-polypoid and small lesions of 2-20 mm. LS (IEE) was extracted from videos and included both in-focus and out-of-focus images. We designed three AIs as follows: AI (WLI) finetuned by LS (WLI), AI (IEE) finetuned by LS (WLI)+LS (IEE), and AI (HQ) finetuned by LS (WLI)+LS (IEE) only with images in focus. Polyp detection using a test set of WLI, BLI, and LCI videos of 100 non-polypoid or non-reddish lesions of 2-20 mm and FPR using movies of 15 total colonoscopy were analyzed, compared to 2 experts and 2 trainees. Results: The sensitivity for LCI in AI (IEE) (83%) was compared to that for WLI in AI (IEE) (76%: p=0.02), WLI in AI (WLI) (57%: p |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
|
