TY - JOUR
T1 - Artificial intelligence for diagnosing exudative age-related macular degeneration
AU - Kang, Chaerim
AU - Lin, John C.
AU - Zhang, Helen
AU - Scott, Ingrid U.
AU - Kalpathy-Cramer, Jayashree
AU - Liu, Su Hsun
AU - Greenberg, Paul B.
N1 - Funding Information:
Cochrane Eyes and Vision US Project, supported by grant UG1EY020522 (PI: Tianjing Li, MD, MHS, PhD) Queen’sUniversity Belfast, UK The work of Gianni Virgili, Co-ordinating Editor for Cochrane Eyes and Vision, is funded by the Centre for Public Health, Queen’s University of Belfast, Northern Ireland. Public HealthAgency, UK The HSC Research and Development (R&D) Division of the Public Health Agency funds the Cochrane Eyes and Vision editorial base at Queen's University Belfast.
Publisher Copyright:
Copyright © 2023 The Cochrane Collaboration. Published by John Wiley & Sons, Ltd.
PY - 2023/1/6
Y1 - 2023/1/6
N2 - Objectives: This is a protocol for a Cochrane Review (diagnostic). The objectives are as follows:. To determine the diagnostic accuracy of artificial intelligence (AI) as a triaging tool for exudative age-related macular degeneration (eAMD). Secondary objectives To compare the performance of different AI algorithms with respect to eAMD diagnosis. To explore potential causes of heterogeneity in diagnostic performance according to the following: index test methodology (core AI method); sources of input to train algorithms (number of training and testing cases); imaging modality (optical coherence tomography, fundus photos, optical coherence tomography angiography, etc, or any combination); characteristics of test set (difficulty of test set, proportion of positive versus negative cases); population characteristics (symptomatic versus asymptomatic, age, etc.); study design (cross-sectional versus longitudinal studies).
AB - Objectives: This is a protocol for a Cochrane Review (diagnostic). The objectives are as follows:. To determine the diagnostic accuracy of artificial intelligence (AI) as a triaging tool for exudative age-related macular degeneration (eAMD). Secondary objectives To compare the performance of different AI algorithms with respect to eAMD diagnosis. To explore potential causes of heterogeneity in diagnostic performance according to the following: index test methodology (core AI method); sources of input to train algorithms (number of training and testing cases); imaging modality (optical coherence tomography, fundus photos, optical coherence tomography angiography, etc, or any combination); characteristics of test set (difficulty of test set, proportion of positive versus negative cases); population characteristics (symptomatic versus asymptomatic, age, etc.); study design (cross-sectional versus longitudinal studies).
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U2 - 10.1002/14651858.CD015522
DO - 10.1002/14651858.CD015522
M3 - Article
AN - SCOPUS:85145905862
SN - 1465-1858
VL - 2023
JO - Cochrane Database of Systematic Reviews
JF - Cochrane Database of Systematic Reviews
IS - 1
M1 - CD015522
ER -