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AMD-Mamba: A Phenotype-Aware Multi-modal Framework for Robust AMD Prognosis.

Puzhen Wu1, Mingquan Lin2, Qingyu Chen3

  • 1Department of Population Health Sciences, Weill Cornell Medicine, New York, NY 10022, USA.

Machine Learning in Medical Imaging. MLMI (Workshop)
|January 13, 2026
PubMed
Summary
This summary is machine-generated.

We developed AMD-Mamba, a new AI framework, and a novel biomarker to predict age-related macular degeneration (AMD) progression. This tool integrates imaging, genetic, and demographic data for earlier detection of high-risk patients.

Keywords:
Age-related macular degeneration (AMD)Metric learningSurvival predictionVision Mamba

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Area of Science:

  • Ophthalmology and Artificial Intelligence
  • Biomedical Informatics

Background:

  • Age-related macular degeneration (AMD) is a primary cause of irreversible vision loss, necessitating accurate prognosis for timely intervention.
  • Current prognostic methods often focus on limited local features, potentially missing crucial disease progression patterns.

Purpose of the Study:

  • To introduce AMD-Mamba, a novel multi-modal framework for AMD prognosis.
  • To develop and validate a new AMD biomarker for improved early detection and risk stratification.

Main Methods:

  • Developed AMD-Mamba, a multi-modal framework integrating color fundus images, genetic variants, and socio-demographic data.
  • Employed a novel metric learning strategy using AMD severity scales for richer feature representation.
  • Utilized Vision Mamba to fuse local and global image information, enhancing analysis beyond traditional CNNs.
  • Implemented multi-scale fusion combining imaging and clinical variables at different resolutions.

Main Results:

  • The proposed AMD biomarker demonstrated significant predictive power for AMD progression.
  • AMD-Mamba achieved improved detection of high-risk AMD patients in early stages when combined with existing variables.
  • Experimental validation on the AREDS dataset (45,818 images, 52 genetic variants, 3 socio-demographic variables from 2,741 subjects) confirmed the framework's efficacy.

Conclusions:

  • AMD-Mamba offers a promising multi-modal approach for more precise AMD prognosis.
  • The novel biomarker significantly contributes to identifying individuals at high risk for AMD progression.
  • This framework facilitates proactive and personalized management strategies for AMD patients.