Gut Liver.
2024 Sep;18(5):857-866. 10.5009/gnl240068.
Impact of User’s Background Knowledge and Polyp Characteristics in Colonoscopy with Computer-Aided Detection
- Lee J
1 - Cho WS2
- Kim BS2
- Yoon D2
- Kim J1
- Song JH1
- Yang SY1
- Lim SH1
- Chung GE1
- Choi JM1
- Han YM1
- Kong HJ3,4,5,6
- Lee JC3,7,8
- Kim S3,7,8
- Bae JH1
- Affiliations
-
- 1Department of Internal Medicine and Healthcare Research Institute, Healthcare System Gangnam Center, Seoul National University Hospital, Seoul, Korea
- 2Interdisciplinary Program in Bioengineering, Graduate School, Seoul National University, Seoul, Korea
- 3Department of Biomedical Engineering, Seoul National University College of Medicine, Seoul, Korea
- 4Medical Big Data Research Center, Seoul National University College of Medicine, Seoul, Korea
- 5Artificial Intelligence Institute, Seoul National University, Seoul, Korea
- 6Transdisciplinary Department of Medicine and Advanced Technology, Seoul National University Hospital, Seoul, Korea
- 7Institute of Medical and Biological Engineering, Medical Research Center, Seoul National University, Seoul, Korea
- 8Institute of Bioengineering, Seoul National University, Seoul, Korea
- KMID: 2559094
- DOI: http://doi.org/10.5009/gnl240068
Abstract
- 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.
Keyword
- Full Text Links
-
- Actions
-
Cited
- CITED
-
- Close
- Share
-
- Similar articles
-
- Computer-aided polyp characterization in colonoscopy: sufficient performance or not?
- Performance comparison between two computer-aided detection colonoscopy models by trainees using different false positive thresholds: a cross-sectional study in Thailand
- Virtual CT Colonoscopy and Virtual CT Barium Enema using Multidetector-row CT
- Fabricating a Ceramic-Pressed-to-Metal Restoration with Computer-Aided Design, Computer-Aided Manufacturing and Selective Laser Sintering: A Case Report
- Does computer-aided diagnostic endoscopy improve the detection of commonly missed polyps? A meta-analysis
