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Feasibility of Multimodal Deep Learning for Automated Staging of Familial Exudative Vitreoretinopathy Using Color Fundus Photographs and Fluorescein Angiography.

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Title: Feasibility of Multimodal Deep Learning for Automated Staging of Familial Exudative Vitreoretinopathy Using Color Fundus Photographs and Fluorescein Angiography.
Authors: Yuan, Mingzhen1 (AUTHOR), Wang, Tianyu1,2 (AUTHOR), Liu, Zirong1,2 (AUTHOR), Liu, Jinghua1,2 (AUTHOR), Ma, Jing1 (AUTHOR), Deng, Guangda1 (AUTHOR), Li, Liang1 (AUTHOR), Li, Songfeng1 (AUTHOR), Hu, Yan2 (AUTHOR), Lu, Hai1 (AUTHOR)
Source: Diagnostics (2075-4418). Nov2025, Vol. 15 Issue 21, p2752. 18p.
Database: Academic Search Ultimate
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  Data: Feasibility of Multimodal Deep Learning for Automated Staging of Familial Exudative Vitreoretinopathy Using Color Fundus Photographs and Fluorescein Angiography.
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  Data: <searchLink fieldCode="JN" term="%22Diagnostics+%282075-4418%29%22">Diagnostics (2075-4418)</searchLink>. Nov2025, Vol. 15 Issue 21, p2752. 18p.
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=asn&AN=189601785
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        Value: 10.3390/diagnostics15212752
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      – TitleFull: Feasibility of Multimodal Deep Learning for Automated Staging of Familial Exudative Vitreoretinopathy Using Color Fundus Photographs and Fluorescein Angiography.
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              M: 11
              Text: Nov2025
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              Y: 2025
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