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Forecasting eye disease progression from longitudinal images

Published on:

14 May 2024

Primary Category:

Computer Vision and Pattern Recognition

Paper Authors:

Gregory Holste,

Mingquan Lin,

Ruiwen Zhou,

Fei Wang,

Lei Liu,

Qi Yan,

Sarah H. Van Tassel,

Kyle Kovacs,

Emily Y. Chew,

Zhiyong Lu,

Zhangyang Wang,

Yifan Peng

Bullets

Key Details

Proposes model to forecast eye disease risk from longitudinal images

Validated on major studies for AMD and glaucoma prognosis

Significantly outperforms baseline model using only last image

Suggests prior images still provide prognostic value

Attention analysis reveals most influential visits

AI generated summary

Forecasting eye disease progression from longitudinal images

This study proposes a deep learning model to predict patients' future risk of developing vision-threatening eye diseases like age-related macular degeneration and glaucoma. By analyzing sequences of retinal images captured over time, the model learns to assess disease progression rates and make personalized risk forecasts to inform care plans.

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