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Using Retinal Imaging to Study Dementia

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Deep Learning-Based Vascular Aging Prediction From Retinal Fundus Images.

Ruohong Wang¹, Yuhe Tan¹, Zheng Zhong¹

¹Department of Ophthalmology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, People's Republic of China.

Translational Vision Science & Technology

|July 10, 2024

Summary

A new deep learning model, Reti-aging score, screens vascular aging using retinal images. This method shows promise for predicting cardiovascular diseases, especially in underserved regions.

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Area of Science:

Ophthalmology
Cardiology
Artificial Intelligence

Background:

Vascular aging is a significant cardiovascular risk factor.
Current assessment methods for vascular aging are limited and geographically restricted.
Retinal fundus images offer a potential window into systemic vascular health.

Purpose of the Study:

To develop and validate a deep learning model for screening vascular aging from retinal fundus images.
To compare the diagnostic performance of the deep learning model against clinical parameters and human specialists.
To investigate the predictive capability of the model for new-onset cardiovascular conditions.

Main Methods:

A deep learning algorithm was trained on 8865 retinal fundus images from 4376 patients across two datasets.
Vascular aging was defined by pulse wave velocity ≥1400 cm/s.
The model's performance was evaluated using Area Under the Receiver Operating Characteristics Curve (AUC) and compared with clinical parameters and specialist assessments.

Main Results:

The Reti-aging score model achieved AUCs of 0.826 (internal) and 0.779 (external), outperforming clinical parameters.
The model's accuracy surpassed that of ophthalmologists, while geriatricians could not predict vascular aging from the images.
The Reti-aging score was significantly associated with the risk of new-onset hypertension and carotid artery plaque.

Conclusions:

The Reti-aging score model offers a novel and effective method for screening vascular aging via retinal fundus image analysis.
This deep learning approach provides a new indicator for predicting new-onset cardiovascular diseases.
The model's robust performance makes it suitable for widespread screening, particularly in resource-limited settings.