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

  • 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.

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Metadaten
Verfasserangaben:Julia Arribas, Giulio AntonelliORCiD, Leonardo Frazzoni, Lorenzo FuccioORCiD, Alanna EbigboORCiD, Fons van der Sommen, Noha Ghatwary, Christoph PalmORCiDGND, Miguel Coimbra, Francesco Renna, Jacques J.G.H.M. BergmanORCiD, Prateek Sharma, Helmut Messmann, Cesare HassanORCiD, Mario J. Dinis-RibeiroORCiD
DOI:https://doi.org/10.1136/gutjnl-2020-321922
Titel des übergeordneten Werkes (Englisch):Gut
Verlag:BMJ
Verlagsort:London
Dokumentart:Artikel aus einer Zeitschrift/Zeitung
Sprache der Veröffentlichung:Englisch
Jahr der Veröffentlichung:2021
Datum der Freischaltung:11.11.2020
Freies Schlagwort / Tag:Artificial Intelligence
Jahrgang:70
Ausgabe / Heft:8
Erste Seite:1458
Letzte Seite:1468
Fakultäten / Institute / Einrichtungen:Fakultät Informatik und Mathematik
Regensburg Center of Health Sciences and Technology - RCHST
Fakultät Informatik und Mathematik / Regensburg Medical Image Computing (ReMIC)
Begutachtungsstatus:peer-reviewed
Forschungsschwerpunkt:Lebenswissenschaften und Ethik
Lizenz (Deutsch):Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG