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Interpretability of multimodal neural networks for prediction of visual acuity in patients with branch retinal vein occlusion.

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Bibliographic Details
Title:	Interpretability of multimodal neural networks for prediction of visual acuity in patients with branch retinal vein occlusion.
Authors:	Won S; School of Computing and Information Systems, The University of Melbourne, Parkville, VIC, 3052, Australia., Kim K; Department of Ophthalmology, Kyung Hee University Medical Center, Kyung Hee University, Dongdaemun-gu, Seoul, 02447, South Korea., Won Y; Department of Computer Science and Engineering, Kyung Hee University, Yongin-si, Gyeonggi-do, 17104, South Korea., Irshad S; Department of Computer Science and Engineering, Kyung Hee University, Yongin-si, Gyeonggi-do, 17104, South Korea., Lee S; Department of Computer Science and Engineering, Kyung Hee University, Yongin-si, Gyeonggi-do, 17104, South Korea., Yu SY; Department of Ophthalmology, Kyung Hee University Medical Center, Kyung Hee University, Dongdaemun-gu, Seoul, 02447, South Korea. syyu@khu.ac.kr., Kim ST; Department of Computer Science and Engineering, Kyung Hee University, Yongin-si, Gyeonggi-do, 17104, South Korea. st.kim@khu.ac.kr.; G-LAMP NEXUS Institute, Kyung Hee University, 17104, Gyeonggi-do, Yongin-si, South Korea. st.kim@khu.ac.kr.
Source:	Scientific reports [Sci Rep] 2026 Jun 23. Date of Electronic Publication: 2026 Jun 23.
Publication Type:	Journal Article
Journal Info:	Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101563288 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 2045-2322 (Electronic) Linking ISSN: 20452322 NLM ISO Abbreviation: Sci Rep Subsets: MEDLINE
Database:	MEDLINE Ultimate

Description
ISSN:	2045-2322
DOI:	10.1038/s41598-026-58583-y