Machine Learning Prediction of Cardiovascular Risk in Type 1 Diabetes Mellitus Using Radiomic Features from Multimodal Retinal Images

Purpose

To develop a machine learning (ML) algorithm capable of determining cardiovascular (CV) risk in multimodal retinal images from patients with type 1 diabetes mellitus (T1DM), distinguishing between moderate, high, and very high-risk levels.

Design

Cross-sectional analysis of a retinal image data set from a previous prospective OCT angiography (OCTA) study (ClinicalTrials.gov NCT03422965).

Participants

Patients with T1DM included in the progenitor study.

Methods

Radiomic features were extracted from color fundus photographs (CFPs), OCT, and OCTA images, and ML models were trained using these features either individually or combined with clinical data (demographics and systemic data, OCT + OCTA commercial software metrics, ocular data, blood data). Different data combinations were tested to determine the CV risk stages, defined according to international classifications.

Main Outcome Measures

Area under the receiver operating characteristic curve mean and standard deviation for each ML model and each data combination.

Results

A data set of 597 eyes (359 individuals) was analyzed. Models trained only with the radiomic features achieved area under the curve (AUC) values of (0.79 ± 0.03) to identify moderate risk cases from high and very high-risk cases, and (0.73 ± 0.07) for distinguishing between high and very high-risk cases. The addition of clinical variables improved all AUC values, obtaining (0.99 ± 0.01) for identifying moderate cases, and (0.95 ± 0.02) for differentiating between high and very high-risk cases. For very high CV risk, radiomics combined with OCT + OCTA metrics and ocular data achieved an AUC of (0.89 ± 0.02) without systemic data input. The performance of the models was similar in unilateral and bilateral eye image data sets.

Conclusions

Radiomic features obtained from retinal images are helpful to discriminate and classify CV risk labels, differentiating risk categories. The addition of demographics and systemic data combined with ocular data differentiate high from very high CV risk cases, and interestingly OCT + OCTA metrics with ocular data identify very high CV risk cases without systemic data input. These results reflect the potential of this oculomics approach for CV risk assessment.

Financial Disclosure(s)

Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.