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*
  • *此作品的通信作者

研究成果: 期刊稿件文章同行評審

7 引文 斯高帕斯(Scopus)

摘要

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).

原文英語
文章編號CD015522
期刊Cochrane Database of Systematic Reviews
2023
發行號1
DOIs
出版狀態已出版 - 06 01 2023

文獻附註

Publisher Copyright:
Copyright © 2023 The Cochrane Collaboration. Published by John Wiley & Sons, Ltd.

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