Deep Ensemble Learning to Detect Retinal Vascular Leakage on Ultrawide-Field Fundus Photographs of Patients With Uveitis

Jongwoo Kim; Nam V. Nguyen; Matias A. Soifer; Ayman G. Elnahry; Mutaz Al-Nawaflh; Mélanie Hébert; H Nida Sen; Sunil Bellur; Shilpa Kodati

doi:10.1167/tvst.15.4.15

April 15, 2026Translational Vision Science & TechnologyOpen Access

Deep Ensemble Learning to Detect Retinal Vascular Leakage on Ultrawide-Field Fundus Photographs of Patients With Uveitis

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Key Points

The aim is to develop an algorithm to detect retinal vascular leakage on ultrawide-field images.
Utilized deep ensemble learning techniques
Assessed images from patients with uveitis
Evaluated detection performance against standard screening methods
Algorithm accurately identifies retinal vascular leakage in ultrawide-field images
Shows potential as a screening tool in resource-limited settings
May reduce the need for fluorescein angiography in certain patients

Implication

Algorithm detects retinal vascular leakage in uveitis patients, suggesting a useful tool for screening.

Abstract

This algorithm model can be a potential screening tool to detect the presence of RVL on UWF images, thus determining the need for UWFFA, as this would be especially helpful in resource-limited settings or in patients with known adverse effects to the fluorescein dye.

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Cite This Study

Kim et al. (2026) studied this question.

synapsesocial.com/papers/69e1cecc5cdc762e9d857c6b https://doi.org/10.1167/tvst.15.4.15

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Deep Ensemble Learning to Detect Retinal Vascular Leakage on Ultrawide-Field Fundus Photographs of Patients With Uveitis

Key Points

Implication

Abstract

Discussion

Cite This Study

Deep Ensemble Learning to Detect Retinal Vascular Leakage on Ultrawide-Field Fundus Photographs of Patients With Uveitis

Key Points

Implication

Abstract

Discussion

Cite This Study

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