Julia Arribas
University of Porto
Likelihood ratios in diagnostic testingArtificial intelligencePrevalenceMedical physicsPublication biasUpper GI endoscopyGI NEOPLASIAUpper gastrointestinalGastric adenocarcinomaMiss rateRegistry dataStudy qualityPredictive valueDiagnostic accuracyGold standard (test)Confidence intervalMedicineArea under the curveMeta-analysis
3Publications
1H-index
6Citations
Publications 3
Newest
#1Julia Arribas (University of Porto)H-Index: 1
#2Giulio Antonelli (Sapienza University of Rome)H-Index: 13
Last. Mário Dinis-Ribeiro (University of Porto)H-Index: 57
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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...
12 CitationsSource
AIMS Estimates on miss-rates for upper gastrointestinal neoplasia (UGIN) rely on registry data or old studies. Quality assurance programs for upper-GI endoscopy are not fully established due to the lack of the infrastructure to measure endoscopists' competence. We aimed at assessing endoscopists' accuracy for the recognition of UGIN exploiting the framework of Artificial Intelligence (AI) validation studies. METHODS Literature search among databases (PubMed/MEDLINE, EMBASE, Scopus) up to August ...
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#1Leonardo Frazzoni (UNIBO: University of Bologna)H-Index: 21
#2Julia Arribas (University of Porto)H-Index: 1
Last. Mário Dinis-Ribeiro (University of Porto)H-Index: 57
view all 6 authors...
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