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Quantification of Vascular Parameters in Whole Mount Retinas of Mice with Non-Proliferative and Proliferative Retinopathies

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A Deep Learning Architecture for Vascular Area Measurement in Fundus Images.

Kanae Fukutsu¹, Michiyuki Saito¹, Kousuke Noda^1,2

¹Department of Ophthalmology, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, Sapporo, Japan.

Ophthalmology Science

|October 17, 2022

Summary

A deep learning system accurately measures retinal vascular areas, identifying hypertension-related changes. This novel approach correlates retinal arteriolar and venular areas with blood pressure and age.

Keywords:

AA, total arteriolar area AUC, area under the receiver operating characteristic curve AVR, arteriovenous ratio Arteriosclerosis BP, blood pressure DBP, diastolic blood pressure DRIVE, Digital Retinal Images for Vessel Extraction Deep learning system FN, false-negative FP, false-positive FPa, FP arterioles FPv, FP venules Hypertensive retinopathy Imaging MISCa, misclassification rates of arterioles MISCv, misclassification rates of venules RGB, red-green-blue Retinal arteriolar narrowing SBP, systolic blood pressure TN, true-negative TP, true-positive TPa, TP arterioles TPv, TP venules VA, total venular area

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Area of Science:

Ophthalmology
Medical Imaging
Artificial Intelligence

Background:

Hypertension is a leading cause of cardiovascular disease.
Retinal microvasculature changes are early indicators of hypertensive damage.
Accurate, automated assessment of retinal vessels is crucial for early detection.

Purpose of the Study:

To develop and validate a deep learning system for evaluating retinal vessel alterations.
To assess the correlation between retinal vascular areas and hypertension.
To establish a novel index for hypertension-related vascular changes.

Main Methods:

A retrospective study analyzed 10,571 fundus photographs from 5,598 participants.
A deep learning algorithm automatically segmented and classified retinal arterioles and venules.
Total arteriolar area (AA) and total venular area (VA) were measured and correlated with age and blood pressure.

Main Results:

The deep learning algorithm showed high accuracy in vessel segmentation and classification.
Significant positive correlation found between AA and VA.
Both AA and VA showed negative correlations with age and blood pressure, with SBP correlating more strongly with AA than AVR.

Conclusions:

The deep learning system provides a novel, automated method for assessing retinal vascular changes.
Retinal vascular area measurements using this system can serve as a new index for hypertension.
This technology has potential for early hypertension detection and monitoring.