Portal de investigação
Standalone performance of artificial intelligence for upper GI neoplasia: a meta-analysis
Autores da FMUP
Participantes de fora da FMUP
- Arribas, J
- Antonelli, G
- Frazzoni, L
- Fuccio, L
- Ebigbo, A
- van der Sommen, F
- Ghatwary, N
- Palm, C
- Coimbra, M
- Renna, F.
- Bergman, JJGHM
- Sharma, P
- Messmann, H
- Hassan, C
Unidades de investigação
Abstract
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.
© Author(s) (or their employer(s)) 2020. No commercial re-use. See rights and permissions. Published by BMJ.
Dados da publicação
- ISSN/ISSNe:
- 1468-3288, 0017-5749
- Tipo:
- Article
- Páginas:
- 1458-1468
- Link para outro recurso:
- www.scopus.com
Gut BMJ Publishing Group
Citações Recebidas na Web of Science: 40
Citações Recebidas na Scopus: 41
Documentos
- Não há documentos
Filiações
Keywords
- diagnostic and therapeutic endoscopy; gastrointesinal endoscopy; gastric pre-cancer; Barrett's oesophagus; oesophageal lesions
Proyectos asociados
Effectiveness of endoscopic resection of colonic lesions > 20mm
Investigador Principal: Mário Jorge Dinis Ribeiro
Estudo Clínico Académico (Colonic lesions) . 2020
Citar a publicação
Arribas J,Antonelli G,Frazzoni L,Fuccio L,Ebigbo A,van der Sommen F,Ghatwary N,Palm C,Coimbra M,Renna F,Bergman JJGHM,Sharma P,Messmann H,Hassan C,Dinis M. Standalone performance of artificial intelligence for upper GI neoplasia: a meta-analysis. Gut. 2021. 70. (8):p. 1458-1468. IF:31,793. (1).
