ArticleFrontiers in medicine2026
Machine-learning for age-related macular degeneration using multimodal fundus data.
Article in Frontiers in medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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7 authors.
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Abstract
Objective: To develop an integrated model that combines multimodal fundus image features for accurately predicting the individualized risk of progression from early to late stages of age-related macular degeneration (AMD). Methods: A retrospective analysis was conducted on the data of 324 patients with AMD. The patients were randomly divided into a training set ( Results: There were no significant differences in the baseline characteristics between the training set and the validation set patients ( Conclusion: In this study, an AMD progression prediction model based on multimodal fundus images was successfully developed, which can effectively identify patients at high risk of progression and provide a new paradigm for clinical individualized precision medicine.
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