Standalone performance of artificial intelligence for upper GI neoplasia: a meta-analysis

• Type: Article in International Scientific Journal
• Date: 2021

Authors

Arribas, J (Author) Other; Antonelli, G (Author) Other; Frazzoni, L (Author) Other; Fuccio, L (Author) Other; Ebigbo, A (Author) Other; van der Sommen, F (Author) Other; Ghatwary, N (Author) Other; Palm, C (Author) Other; Coimbra, M (Author) FCUP; Renna, F (Author) FCUP; Bergman, JJGHM (Author) Other; Sharma, P (Author) Other; Messmann, H (Author) Other; Hassan, C (Author) Other; Mario Dinis Ribeiro (Author) FMUP

Journal

Title: Gut

Vol. 70

Pages: 1458-1468

ISSN: 0017-5749

Publisher: BMJ PUBLISHING GROUP

Indexing

ISI Web of Knowledge - 42 Citations

Scopus - 37 Citations

Other information

Authenticus ID: P-00T-12F

DOI: 10.1136/gutjnl-2020-321922

Abstract (EN): Objective Artificial intelligence (AI) may reduce underdiagnosed or overlooked upper GI (UGI) neoplastic and preneoplastic conditions, due to subtle appearance and low disease prevalence. Only disease-specific AI performances have been reported, generating uncertainty on its clinical value. Design We searched PubMed, Embase and Scopus until July 2020, for studies on the diagnostic performance of AI in detection and characterisation of UGI lesions. Primary outcomes were pooled diagnostic accuracy, sensitivity and specificity of AI. Secondary outcomes were pooled positive (PPV) and negative (NPV) predictive values. We calculated pooled proportion rates (%), designed summary receiving operating characteristic curves with respective area under the curves (AUCs) and performed metaregression and sensitivity analysis. Results Overall, 19 studies on detection of oesophageal squamous cell neoplasia (ESCN) or Barrett's esophagus-related neoplasia (BERN) or gastric adenocarcinoma (GCA) were included with 218, 445, 453 patients and 7976, 2340, 13 562 images, respectively. AI-sensitivity/specificity/PPV/NPV/positive likelihood ratio/negative likelihood ratio for UGI neoplasia detection were 90% (CI 85% to 94%)/89% (CI 85% to 92%)/87% (CI 83% to 91%)/91% (CI 87% to 94%)/8.2 (CI 5.7 to 11.7)/0.111 (CI 0.071 to 0.175), respectively, with an overall AUC of 0.95 (CI 0.93 to 0.97). No difference in AI performance across ESCN, BERN and GCA was found, AUC being 0.94 (CI 0.52 to 0.99), 0.96 (CI 0.95 to 0.98), 0.93 (CI 0.83 to 0.99), respectively. Overall, study quality was low, with high risk of selection bias. No significant publication bias was found. Conclusion We found a high overall AI accuracy for the diagnosis of any neoplastic lesion of the UGI tract that was independent of the underlying condition. This may be expected to substantially reduce the miss rate of precancerous lesions and early cancer when implemented in clinical practice.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 11