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.
- diagnostic and therapeutic endoscopy
- gastrointesinal endoscopy
- gastric pre-cancer
- Barrett's oesophagus
- oesophageal lesions
Data availability statement
Data are available upon reasonable request. Complete dataset used for meta-analysis available with the corresponding author upon request.
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JA and GA are joint first authors.
CH and MJD-R are joint senior authors.
Twitter @fvdsommen, @ReMIC_OTH
JA and GA contributed equally.
CH and MJD-R contributed equally.
Contributors JA, GA, MJD-R and CH: conception and design. JA, GA, CH and MJD-R: data extraction and interpretation; drafting of the article. LFr and LF: statistical analysis. AE, FvdS, CP, MC, FR, HM, NG, LF, LFr, JJGHMB and PS: critical revision of the article for important intellectual content. All authors read and approved the final version of the manuscript. JA and GA, first authors, equally contributed to this manuscriot. CH and MJD-R, senior authors, equally contributed to this manuscript.
Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.
Competing interests JB reports grants and personal fees from Olympus, Fujifilm, Pentax Endoscopy outside the submitted work; PS reports personal fees from Olympus and Boston Scientific, grants from CDx, US Endoscopy, Medtronic, Ironwood, Erbe, Fujifilm, outside the submitted work; CH reports personal fees from Medtronic, Fujifilm, Olympus, outside the submitted work; MJD-R reports grants from Olympus, Fujifulm, outside the submitted work. JA, GA, LF, LFr, AE, FVDS, NG, CP, MC, FR, HM have no COI to declare.
Provenance and peer review Not commissioned; externally peer reviewed.
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