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Endoscopy 2022; 54(04): 403-411
DOI: 10.1055/a-1500-3730
Systematic Review

Endoscopistsʼ diagnostic accuracy in detecting upper gastrointestinal neoplasia in the framework of artificial intelligence studies

Leonardo Frazzoni  ‡   *
 1   Department of Medical and Surgical Sciences (DIMEC), University of Bologna, S. Orsola-Malpighi Hospital, Bologna, Italy
,
Julia Arribas
 2   CIDES/CINTESIS, Faculty of Medicine, University of Porto, Porto, Portugal
,
Giulio Antonelli‡   *
 3   Gastroenterology Unit, Nuovo Regina Margherita Hospital, Rome, Italy
 4   Department of Translational and Precision Medicine, “Sapienza” University of Rome, Rome, Italy
,
Diogo Libanio 
 2   CIDES/CINTESIS, Faculty of Medicine, University of Porto, Porto, Portugal
,
Alanna Ebigbo
 5   III Medizinische Klinik, Universitatsklinikum Augsburg, Augsburg, Germany
,
Fons van der Sommen 
 6   Department of Electrical Engineering, VCA group, Eindhoven University of Technology, Eindhoven, The Netherlands
,
Albert Jeroen de Groof
 7   Department of Gastroenterology and Hepatology, Amsterdam UMC, Amsterdam, The Netherlands
,
Hiromu Fukuda
 8   Department of Gastrointestinal Oncology, Osaka International Cancer Institute, Osaka, Japan
,
Masayasu Ohmori 
 8   Department of Gastrointestinal Oncology, Osaka International Cancer Institute, Osaka, Japan
,
Ryu Ishihara
 8   Department of Gastrointestinal Oncology, Osaka International Cancer Institute, Osaka, Japan
,
Lianlian Wu
 9   Department of Gastroenterology, Renmin Hospital of Wuhan University, Institute for Gastroenterology and Hepatology, Wuhan University, Wuhan, China
,
Honggang Yu 
 9   Department of Gastroenterology, Renmin Hospital of Wuhan University, Institute for Gastroenterology and Hepatology, Wuhan University, Wuhan, China
,
Yuichi Mori
10   Clinical Effectiveness Research Group, University of Oslo, Oslo, Norway
11   Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan
,
Alessandro Repici
12   Digestive Endoscopy Unit, Humanitas Research Hospital – IRCCS, Milan, Italy
13   Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Milan, Italy
,
Jacques J. G. H. M. Bergman
 7   Department of Gastroenterology and Hepatology, Amsterdam UMC, Amsterdam, The Netherlands
,
Prateek Sharma
14   Department of Gastroenterology and Hepatology, University of Kansas Medical Center, Kansas City, Kansas, USA
,
Helmut Messmann
 5   III Medizinische Klinik, Universitatsklinikum Augsburg, Augsburg, Germany
,
Cesare Hassan
 3   Gastroenterology Unit, Nuovo Regina Margherita Hospital, Rome, Italy
,
Lorenzo Fuccio 
 1   Department of Medical and Surgical Sciences (DIMEC), University of Bologna, S. Orsola-Malpighi Hospital, Bologna, Italy
,
Mário Dinis-Ribeiro
15   Gastroenterology Department, Portuguese Oncology Institute of Porto, Porto, Portugal
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Referred to by:

Author commentary on Leonardo Frazzoni et al.Endoscopy 2022; 54(04): v17-v17
DOI: 10.1055/a-1540-3373

Corrected by:

Correction: Endoscopistsʼ diagnostic accuracy in detecting upper gastrointestinal neoplasia in the framework of artificial intelligence studiesEndoscopy 2022; 54(04): C12-C12
DOI: 10.1055/a-1907-9242

Abstract

Background 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 owing to the lack of infrastructure to measure endoscopists’ competence. We aimed to assess endoscopists’ accuracy for the recognition of UGIN exploiting the framework of artificial intelligence (AI) validation studies.

Methods Literature searches of databases (PubMed/MEDLINE, EMBASE, Scopus) up to August 2020 were performed to identify articles evaluating the accuracy of individual endoscopists for the recognition of UGIN within studies validating AI against a histologically verified expert-annotated ground-truth. The main outcomes were endoscopists’ pooled sensitivity, specificity, positive and negative predictive value (PPV/NPV), and area under the curve (AUC) for all UGIN, for esophageal squamous cell neoplasia (ESCN), Barrett esophagus-related neoplasia (BERN), and gastric adenocarcinoma (GAC).

Results Seven studies (2 ESCN, 3 BERN, 1 GAC, 1 UGIN overall) with 122 endoscopists were included. The pooled endoscopists’ sensitivity and specificity for UGIN were 82 % (95 % confidence interval [CI] 80 %–84 %) and 79 % (95 %CI 76 %–81 %), respectively. Endoscopists’ accuracy was higher for GAC detection (AUC 0.95 [95 %CI 0.93–0.98]) than for ESCN (AUC 0.90 [95 %CI 0.88–0.92]) and BERN detection (AUC 0.86 [95 %CI 0.84–0.88]). Sensitivity was higher for Eastern vs. Western endoscopists (87 % [95 %CI 84 %–89 %] vs. 75 % [95 %CI 72 %–78 %]), and for expert vs. non-expert endoscopists (85 % [95 %CI 83 %–87 %] vs. 71 % [95 %CI 67 %–75 %]).

Conclusion We show suboptimal accuracy of endoscopists for the recognition of UGIN even within a framework that included a higher prevalence and disease awareness. Future AI validation studies represent a framework to assess endoscopist competence.

* Joint first authors


Appendices 1s–3s, Figs. 1s–6s, Tables 1s–3s



Publication History

Received: 05 January 2021

Accepted after revision: 05 May 2021

Accepted Manuscript online:
05 May 2021

Article published online:
17 June 2021

© 2021. Thieme. All rights reserved.

Georg Thieme Verlag KG
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