Artificial Intelligence in Detecting Subclinical Cardiovascular Disease: A New Frontier in Preventive Cardiology

Atiq Ur Rehman; Faiz Ullah; Rahim Dil Khan; Ijaz Mahmood; Ajmal Khan; Khushal Khan

doi:10.70749/ijbr.v3i5.1263

Authors

Atiq Ur Rehman Peshawar Institute of Cardiology, KP, Pakistan.
Faiz Ullah Department of Cardiology, Hayatabad Medical Complex, Peshawar, KP, Pakistan.
Rahim Dil Khan Department of Cardiology, Hayatabad Medical Complex, Peshawar, KP, Pakistan.
Ijaz Mahmood Hayatabad Medical Complex, Peshawar, KP, Pakistan.
Ajmal Khan Rehman College of Allied Health Science, Rehman Medical Institute, Peshawar, KP, Pakistan.
Khushal Khan Institute of Paramedical Sciences, Khyber Medical University Peshawar, KP, Pakistan.

DOI:

https://doi.org/10.70749/ijbr.v3i5.1263

Keywords:

Artificial Intelligence, Subclinical Cardiovascular Disease, Early Detection, Preventive Cardiology, Machine Learning, Tertiary Care

Abstract

Introduction: The condition commonly referred to as subclinical cardiovascular disease (SCVD) presents in most instances when the disease is not easily diagnosed and tends to progress to a more severe phase. Early detection has become one of the most promising trends in cardiology using AI technologies. Objective: To determine the usefulness of artificial intelligence in identifying subclinical cardiovascular disease and its usefulness in early diagnosis and prevention in tertiary care. Materials and Methods: A survey was conducted on 200 patients visiting the Department of Cardiology, Hayatabad Medical Complex Peshawar, Pakistan. Information from ECGs, echocardiograms, and biomarkers was analyzed using AI algorithms to establish signs of early cardiovascular disease. This was done to compare it with the traditional diagnosing technique. Results: AI models are highly accurate in identifying early signs of left ventricular dysfunction, arterial stiffness, and endothelial dysfunction in asymptomatic persons. It was found that the overall detection rate of AI was 85%, which was higher than the traditional approaches to diagnosis. Conclusion: AI presents an early and accurate approach towards identifying SCVD, especially for use in preventive cardiology, especially in low-resource environments.

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