지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 소화기인터벤션의학회 International Journal of Gastrointestinal Intervention International Journal of Gastrointestinal Intervention Vol.13 No.3
- 발행연도
- 2024.7
- 수록면
- 65 - 73 (9page)
- DOI
- https://doi.org/10.18528/ijgii240013
이용수
초록· 키워드
오류제보하기
Colorectal cancer has substantial morbidity and mortality. Approximately one-quarter of cases are overlooked during screening colonoscopy, leading to interval colorectal cancer. The use of artificial intelligence (AI) through deep learning systems has demonstrated promising results in the detection of polyps and adenomas. Consequently, our objective was to evaluate the impact of AI on adenoma detection. To identify relevant studies, we searched the PubMed, MEDLINE, and Cochrane Library databases without restrictions on publication date. Ultimately, we analyzed 16 randomized controlled trials involving 13,685 participants. The primary outcome assessed was the effect of AI-assisted colonoscopy (AIAC) on the adenoma detection rate (ADR). Secondary outcomes included the polyp detection rate (PDR) and adenomas per colonoscopy (APC). A random-effects model was used to calculate pooled effect sizes, and statistical heterogeneity was evaluated using the Higgins I2 statistic, with I2 cutoff points of 25%, 50%, and 75% indicating low, moderate, and high heterogeneity, respectively. Publication bias was investigated using a funnel plot, and the quality of evidence was appraised using the Grading of Recommendations, Assessment, Development, and Evaluation framework. The findings indicated a 26% greater ADR with AIAC than with standard colonoscopy (40.4% vs. 31.9%). Additionally, AIAC was associated with a 30% greater PDR (52.9% vs. 40.1%) and a 44% higher APC. The findings demonstrate that the integration of AI in colonoscopy improves ADR, PDR, and APC, potentially reducing the incidence of interval colorectal cancer.
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