AI Can Help Identify Heart Disease...

 

    (Credit: Retinal scan courtesy UK Biobank)


Artificial Intelligence can now identify patients with a high risk of heart attack just by an eye scan that can be taken at an optician or eye clinic.

Sounds too good to be true?

The size and pumping efficiency of the left ventricle (one of the four chambers) of the heart is linked to risk of heart attack. An oversized left-ventricle leads to higher risk of heart disease. In turn, the retinal scans are linked to the size and pumping efficiency of the left ventricle.

Currently, risk of heart disease can be determined through expensive diagnostic tests using MRI and ECG which are available only in a hospital setting. These tests are also unavailable in less well-resourced healthcare systems in developing countries and unnecessarily increase healthcare costs in developed countries. 

Retinal scans on the other hand are comparatively cheap and available in many optician practices. Patients who are found to be at high risk of heart disease can be referred for further expensive tests. Preventative treatments can help the patient avoid premature cardiovascular disease.

Researchers from the UK, China, France, US and Belgium collaborated on this study. They have created and used an AI system based on Deep Learning. During the deep learning process, the AI system analyzed the retinal scans and cardiac scans from more than 5,000 people. The UK Biobank provided data for the study. 

Once the image patterns were learned, the AI system could estimate the size and pumping efficiency of the left ventricle. With information on the estimated size of the left ventricle and its pumping efficiency combined with basic demographic data about the patient, their age and sex, the AI system could make a prediction about their risk of a heart attack over the subsequent 12 months.  

The AI methods used were: multichannel variational autoencoder and deep regressor model. The results look promising with an AUC of around 80%, sensitivity of 74% and specificity of 71%. 

This is the link to the published research work.

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