Multicenter evaluation of machine and deep learning methods to predict glaucoma surgical outcomes

ML and DL models can predict glaucoma outcomes using preoperative EHR data. Translational relevance: prediction models may support clinical decision-making by identifying glaucoma patients at risk of poor postoperative outcomes.

Authors

Sophia Ying Wang (ORCID: https://orcid.org/0000-0003-0916-9403)
Samuel Barry

Institutions

Stanford University (US)

Publication Details

Journal: Frontiers in Artificial Intelligence
Published: 2025-10-22
DOI: https://doi.org/10.3389/frai.2025.1636410
Primary Topic: Glaucoma and retinal disorders
Type: article
Field-Weighted Citation Impact: 0.00

Funders

Research to Prevent Blindness

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article

Multicenter evaluation of machine and deep learning methods to predict glaucoma surgical outcomes

Sophia Ying Wang, Samuel Barry

Oct 22, 2025

Frontiers in Artificial Intelligence

Glaucoma and retinal disorders

article

Multicenter evaluation of machine and deep learning methods to predict glaucoma surgical outcomes

Sophia Ying Wang, Samuel Barry

October 22, 2025article en

Abstract

Frontiers in Artificial IntelligenceVol. 8

Stanford University (US)

Research to Prevent Blindness

Openalex Percentile: Top 56%

Glaucoma and retinal disorders

DOI Open Access PDF OpenAlex

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Multicenter evaluation of machine and deep learning methods to predict glaucoma surgical outcomes — Sophia Ying Wang, Samuel Barry · Frontiers in Artificial Intelligence (2025) | TGRS Research Map | TGRS