Classification of pachychoroid disease on ultrawide-field indocyanine green angiography using auto-machine learning platform.

Journal: The British journal of ophthalmology

Published Date: Jun 1, 2021

Abstract

AIMS: Automatic identification of pachychoroid maybe used as an adjunctive method to confirm the condition and be of help in treatment for macular diseases. This study investigated the feasibility of classifying pachychoroid disease on ultra-widefield indocyanine green angiography (UWF ICGA) images using an automated machine-learning platform.

Authors

In Ki Kim

Department of Ophthalmology, Bucheon St Mary's Hospital, College of Medicine, The Catholic University of Korea, Gyeonggi-do, Republic of Korea.
Kook Lee

Department of Ophthalmology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea.
Jae Hyun Park

Department of Food Science, Sun Moon University, Natural Science 118, 70 Sunmoon-ro 221, Tangjeong-myeon, Asan-si 336-708, Korea.
Jiwon Baek

Department of Ophthalmology, Bucheon St Mary's Hospital, College of Medicine, The Catholic University of Korea, Gyeonggi-do, Republic of Korea md.jiwon@gmail.com.
Won Ki Lee

Nune Eye Center, Seoul, Republic of Korea.

Keywords

Aged Choroid Diseases Coloring Agents Female Fluorescein Angiography Follow-Up Studies Fundus Oculi Humans Indocyanine Green Machine Learning Male Middle Aged Retinal Pigment Epithelium Retrospective Studies Tomography, Optical Coherence

External Resources

View on PubMed Access via DOI PubMed (32620684)

Classification of pachychoroid disease on ultrawide-field indocyanine green angiography using auto-machine learning platform.

Abstract

Authors

Keywords

External Resources

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