Artificial intelligence for diagnosing exudative age-related macular degeneration

Chaerim Kang, John C. Lin, Helen Zhang, Ingrid U. Scott, Jayashree Kalpathy-Cramer, Su Hsun Liu, Paul B. Greenberg

Research output: Contribution to journalArticlepeer-review


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).

Original languageEnglish (US)
Article numberCD015522
JournalCochrane Database of Systematic Reviews
Issue number1
StatePublished - Jan 6 2023

All Science Journal Classification (ASJC) codes

  • Pharmacology (medical)

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