Cardiovascular Disease Diagnosis from DXA Scan and Retinal Images Using Deep Learning

Hamada R. H. Al-Absi, Mohammad Tariqul Islam, Mahmoud Ahmed Refaee, Muhammad E. H. Chowdhury, Tanvir Alam

Sensors 2022 article cited by 89

Source Open-access PDF More in Medicine & Health

importance 10/10 partially solved Artificial Intelligence in Healthcare cardiovascular diagnostics stage: research

Problem

Cardiovascular disease often goes undiagnosed until serious events; non-invasive diagnosis from retinal images and DXA scans was lacking in Qatar.

Why it matters

Cardiovascular diseases are the leading cause of death worldwide.

What the AI did

Deep learning models, including a novel multi-modal approach combining retinal images and DXA data, were trained to distinguish CVD patients from controls in 500 Qatari adults.

Results

Uni-modal accuracy 75.6% (retinal images) and 77.4% (DXA); multi-modal result not stated in abstract.

Impact

Moderate-accuracy non-invasive CVD discrimination demonstrated in a 500-participant case-control study.

Record

Venue	Sensors
Published	2022
Work type	article
Authors	Hamada R. H. Al-Absi, Mohammad Tariqul Islam, Mahmoud Ahmed Refaee, Muhammad E. H. Chowdhury, Tanvir Alam
Citations	89 (OpenAlex, at import time)
References	74
Model or method	not stated in source
Organization	Qatar Biobank (cohort source)
Primary source	https://doi.org/10.3390/s22124310
Open access	gold
Keywords	cardiovascular disease, deep learning, retinal images, DXA, multi-modal diagnosis

Importance rating

10 / 10

existential / global scale

Abstract states CVD is the leading cause of death worldwide, matching rubric's 10 for leading global causes of death; undiagnosed CVD until serious events is the core problem.

Basis: stated. The source itself states the scale. Stated scale: not stated in source. Method and limits.

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