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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.

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|September 19, 2025
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Summary
This summary is machine-generated.

We developed AMD-Mamba, a new AI framework and biomarker for predicting 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
  • Artificial Intelligence
  • Genetics

Background:

  • Age-related macular degeneration (AMD) is a primary cause of irreversible vision loss.
  • Accurate prognosis of AMD is essential for prompt clinical intervention.
  • Existing prognostic models often lack comprehensive data integration and advanced feature extraction.

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 disease progression prediction.
  • To enhance the early detection of high-risk AMD patients.

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 scale scores for richer feature representation.
  • Utilized Vision Mamba for fused local and global information extraction, alongside multi-scale fusion of image and clinical data.

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 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 age-related macular degeneration.