A Novel Retinal Vascular Feature and Machine Learning-based Brain White Matter Lesion Prediction Model

White matter lesion (WML) is one of the common cerebral abnormalities, it indicates changes in the white matter of human brain and have shown significant association with stroke, dementia and deaths. Magnetic resonance imaging (MRI) of the brain is frequently used to diagnose white matter lesion (WML) volume. Regular screening can detect WML in early stage and save from severe consequences. Current option of MRI based diagnosis is impractical for regular screening because of its high expense and unavailability. Thus, earlier screening and prediction of the WML volume/load specially in the rural and remote areas becomes extremely difficult. Research has shown that changes in the retinal micro vascular system reflect changes in the cerebral micro vascular system. Using this information, we have proposed a retinal image based WML volume and severity prediction model which is very convenient and easy to operate. Our proposed model can help the physicians to detect the patients who need immediate and further MRI based detail diagnosis of WML. Our model uses quantified measurement of retinal micro-vascular signs (such as arteriovenular nicking (AVN), Opacity (OP) and focal arteriolar narrowing (FAN)) as input and estimate the WML volume/load and classify its severity. We evaluate our proposed model on a dataset of 111 patients taken from the ENVISion study which have retinal and MRI images for each patient. Our model shows high accuracy in estimating the WML volume, mean square error (MSE) between our predicted WML load and manually annotated WML load is 0.15. The proposed model achieves an F1 score of 0.92 in classifying the patients having mild and severe WML load. The preliminary results of our study indicate that quantified measurement of retinal micro-vascular features (AVN, OP and FAN) can more accurately identify the patients who have high risk of cardio-vascular diseases and dementia.