Impact of User's Background Knowledge and Polyp Characteristics in Colonoscopy with Computer-Aided Detection.

Autor:	Lee J; Department of Internal Medicine and Healthcare Research Institute, Healthcare System Gangnam Center, Seoul National University Hospital, Seoul, Korea., Cho WS; Interdisciplinary Program in Bioengineering, Graduate School, Seoul National University, Seoul, Korea., Kim BS; Interdisciplinary Program in Bioengineering, Graduate School, Seoul National University, Seoul, Korea., Yoon D; Interdisciplinary Program in Bioengineering, Graduate School, Seoul National University, Seoul, Korea., Kim J; Department of Internal Medicine and Healthcare Research Institute, Healthcare System Gangnam Center, Seoul National University Hospital, Seoul, Korea., Song JH; Department of Internal Medicine and Healthcare Research Institute, Healthcare System Gangnam Center, Seoul National University Hospital, Seoul, Korea., Yang SY; Department of Internal Medicine and Healthcare Research Institute, Healthcare System Gangnam Center, Seoul National University Hospital, Seoul, Korea., Lim SH; Department of Internal Medicine and Healthcare Research Institute, Healthcare System Gangnam Center, Seoul National University Hospital, Seoul, Korea., Chung GE; Department of Internal Medicine and Healthcare Research Institute, Healthcare System Gangnam Center, Seoul National University Hospital, Seoul, Korea., Choi JM; Department of Internal Medicine and Healthcare Research Institute, Healthcare System Gangnam Center, Seoul National University Hospital, Seoul, Korea., Han YM; Department of Internal Medicine and Healthcare Research Institute, Healthcare System Gangnam Center, Seoul National University Hospital, Seoul, Korea., Kong HJ; Department of Biomedical Engineering, Seoul National University College of Medicine, Seoul, Korea.; Medical Big Data Research Center, Seoul National University College of Medicine, Seoul, Korea.; Artificial Intelligence Institute, Seoul National University, Seoul, Korea.; Transdisciplinary Department of Medicine and Advanced Technology, Seoul National University Hospital, Seoul, Korea., Lee JC; Department of Biomedical Engineering, Seoul National University College of Medicine, Seoul, Korea.; Institute of Medical and Biological Engineering, Medical Research Center, Seoul National University, Seoul, Korea.; Institute of Bioengineering, Seoul National University, Seoul, Korea., Kim S; Department of Biomedical Engineering, Seoul National University College of Medicine, Seoul, Korea.; Institute of Medical and Biological Engineering, Medical Research Center, Seoul National University, Seoul, Korea.; Institute of Bioengineering, Seoul National University, Seoul, Korea., Bae JH; Department of Internal Medicine and Healthcare Research Institute, Healthcare System Gangnam Center, Seoul National University Hospital, Seoul, Korea.
Jazyk:	angličtina
Zdroj:	Gut and liver [Gut Liver] 2024 Sep 15; Vol. 18 (5), pp. 857-866. Date of Electronic Publication: 2024 Jul 26.
DOI:	10.5009/gnl240068
Abstrakt:	Background/aims: We investigated how interactions between humans and computer-aided detection (CADe) systems are influenced by the user's experience and polyp characteristics. Methods: We developed a CADe system using YOLOv4, trained on 16,996 polyp images from 1,914 patients and 1,800 synthesized sessile serrated lesion (SSL) images. The performance of polyp detection with CADe assistance was evaluated using a computerized test module. Eighteen participants were grouped by colonoscopy experience (nurses, fellows, and experts). The value added by CADe based on the histopathology and detection difficulty of polyps were analyzed. Results: The area under the curve for CADe was 0.87 (95% confidence interval [CI], 0.83 to 0.91). CADe assistance increased overall polyp detection accuracy from 69.7% to 77.7% (odds ratio [OR], 1.88; 95% CI, 1.69 to 2.09). However, accuracy decreased when CADe inaccurately detected a polyp (OR, 0.72; 95% CI, 0.58 to 0.87). The impact of CADe assistance was most and least prominent in the nurses (OR, 1.97; 95% CI, 1.71 to 2.27) and the experts (OR, 1.42; 95% CI, 1.15 to 1.74), respectively. Participants demonstrated better sensitivity with CADe assistance, achieving 81.7% for adenomas and 92.4% for easy-to-detect polyps, surpassing the standalone CADe performance of 79.7% and 89.8%, respectively. For SSLs and difficult-to-detect polyps, participants' sensitivities with CADe assistance (66.5% and 71.5%, respectively) were below those of standalone CADe (81.1% and 74.4%). Compared to the other two groups (56.1% and 61.7%), the expert group showed sensitivity closest to that of standalone CADe in detecting SSLs (79.7% vs 81.1%, respectively). Conclusions: CADe assistance boosts polyp detection significantly, but its effectiveness depends on the user's experience, particularly for challenging lesions.
Databáze:	MEDLINE
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