Gastrointestinal endoscopy · 2026
Efficacy of artificial intelligence-assisted upper gastrointestinal endoscopy for neoplasm detection: a systematic review and meta-analysis of randomized controlled trials.
СтатьяСистематический обзорМетаанализНазвание и аннотация: Перевод пока не подготовлен
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Al Hayek Mohammad, Barberio Brigida, Al Hayek Osamah, Rugge Massimo, Di Pietro Massimiliano, Nounou Mohamedhen Vall, Elhadi Muhammed, Savarino Edoardo Vincenzo
Аннотация
BACKGROUND AND AIMS: EGD remains the primary diagnostic modality for evaluating lesions of the upper gastrointestinal (UGI) tract. This meta-analysis assessed the diagnostic performance of artificial intelligence-EGD (AI-EGD) compared with conventional EGD (Co-EGD) for the detection of UGI neoplasms.
METHODS: A systematic search of major bibliographic databases identified randomized controlled trials (RCTs) comparing AI-EGD and Co-EGD in adults undergoing EGD. The primary outcome was neoplasm detection rate (NDR) per patient. Secondary outcomes included NDR per lesion and stratification by lesion size (<10 and ≥10 mm), as well as NDR by histologic classification: low-grade intraepithelial neoplasia (LGIN), high-grade intraepithelial neoplasia (HGIN), and carcinoma. Risk ratios (RRs) with 95% CIs were pooled using a random-effects model.
RESULTS: Eleven RCTs involving 57,512 participants were included. AI-EGD demonstrated higher detection rates per patient (RR, 1.57; 95% CI, 1.23-2.01) and per lesion (RR, 1.55; 95% CI, 1.33-2.18). The benefit was more evident for lesions ≤10 mm (RR, 2.24; 95% CI, 1.72-2.91), whereas detection rates for lesions >10 mm were comparable. In addition, AI-EGD achieved higher detection rates across histologic subtypes, including LGIN (RR, 1.73; 95% CI, 1.30-2.32), HGIN (RR, 1.61; 95% CI, 1.39-1.87), and carcinoma (RR, 1.54; 95% CI, 1.32-1.79).
CONCLUSIONS: AI-EGD demonstrated superior performance compared with Co-EGD in detecting UGI neoplasms, particularly for lesions ≥10 mm. The greater detection rate of LGIN highlights the potential clinical value of AI support. Further trials are warranted to assess the impact of AI across varying levels of endoscopist experience.
METHODS: A systematic search of major bibliographic databases identified randomized controlled trials (RCTs) comparing AI-EGD and Co-EGD in adults undergoing EGD. The primary outcome was neoplasm detection rate (NDR) per patient. Secondary outcomes included NDR per lesion and stratification by lesion size (<10 and ≥10 mm), as well as NDR by histologic classification: low-grade intraepithelial neoplasia (LGIN), high-grade intraepithelial neoplasia (HGIN), and carcinoma. Risk ratios (RRs) with 95% CIs were pooled using a random-effects model.
RESULTS: Eleven RCTs involving 57,512 participants were included. AI-EGD demonstrated higher detection rates per patient (RR, 1.57; 95% CI, 1.23-2.01) and per lesion (RR, 1.55; 95% CI, 1.33-2.18). The benefit was more evident for lesions ≤10 mm (RR, 2.24; 95% CI, 1.72-2.91), whereas detection rates for lesions >10 mm were comparable. In addition, AI-EGD achieved higher detection rates across histologic subtypes, including LGIN (RR, 1.73; 95% CI, 1.30-2.32), HGIN (RR, 1.61; 95% CI, 1.39-1.87), and carcinoma (RR, 1.54; 95% CI, 1.32-1.79).
CONCLUSIONS: AI-EGD demonstrated superior performance compared with Co-EGD in detecting UGI neoplasms, particularly for lesions ≥10 mm. The greater detection rate of LGIN highlights the potential clinical value of AI support. Further trials are warranted to assess the impact of AI across varying levels of endoscopist experience.