Key result
Deep learning and ensemble learning algorithms provide highly accurate, non-invasive automatic recognition and classification of coronary atherosclerosis, outperforming traditional diagnostic classifiers.
Why the study?
Coronary atherosclerosis is a leading cause of stroke and progressive heart disease, and prediction models using AI have emerged to assist in medical decision-making.
Do artificial intelligence algorithms improve the automatic recognition and classification of coronary atherosclerosis?
Systematic Review (n=39)
Do artificial intelligence algorithms improve the automatic recognition and classification of coronary atherosclerosis?
Deep learning and ensemble learning algorithms offer promising, accurate, and non-invasive methods for the detection and classification of coronary atherosclerosis.
Cardiovascular diseases have a high morbidity rate and per year it leads to 17 million deaths worldwide. Coronary Atherosclerosis is one of the chief causes of stroke and progressive heart disease marked by lipids and fibrous elements accumulation in the arteries. Artificial intelligence (AI) has established remarkable progress in recent times in clinical practice helping patients and healthcare professionals in the accurate and faster diagnosis of diseases. Prediction models in the identification of Atherosclerosis have set foot in the academic literature to assist in making medical decisions during urgent circumstances. This paper aimed to systematically review automatic atherosclerotic plaque detection algorithms. The advantages of these latest techniques in automatic recognition of atherosclerotic plaque, its composition, classification strategy and future predictions in terms of severity are elucidated in this review with limitations and research gaps. The findings suggest that deep learning models are the future of diagnosis and ensemble learning algorithms are best in non-invasive accurate detection of cardiovascular diseases.
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Prajapati et al. (2022) conducted a systematic review in Coronary Atherosclerosis (n=39). Deep learning and ensemble learning algorithms vs. Conventional diagnostic methods was evaluated on Diagnostic accuracy and classification performance of coronary atherosclerosis. Deep learning and ensemble learning algorithms provide highly accurate, non-invasive automatic recognition and classification of coronary atherosclerosis, outperforming traditional diagnostic classifiers.
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