Cardiovascular/Stroke Risk Assessment in Patients with Erectile Dysfunction-A Role of Carotid Wall Arterial Imaging and Plaque Tissue Characterization Using Artificial Intelligence Paradigm: A Narrative Review.

Abstract

PURPOSE:

The role of erectile dysfunction (ED) has recently shown an association with the risk of stroke and coronary heart disease (CHD) via the atherosclerotic pathway. Cardiovascular disease (CVD)/stroke risk has been widely understood with the help of carotid artery disease (CTAD), a surrogate biomarker for CHD. The proposed study emphasizes artificial intelligence-based frameworks such as machine learning (ML) and deep learning (DL) that can accurately predict the severity of CVD/stroke risk using carotid wall arterial imaging in ED patients.

METHODS:

Using the PRISMA model, 231 of the best studies were selected. The proposed study mainly consists of two components: (i) the pathophysiology of ED and its link with coronary artery disease (COAD) and CHD in the ED framework and (ii) the ultrasonic-image morphological changes in the carotid arterial walls by quantifying the wall parameters and the characterization of the wall tissue by adapting the ML/DL-based methods, both for the prediction of the severity of CVD risk. The proposed study analyzes the hypothesis that ML/DL can lead to an accurate and early diagnosis of the CVD/stroke risk in ED patients. Our finding suggests that the routine ED patient practice can be amended for ML/DL-based CVD/stroke risk assessment using carotid wall arterial imaging leading to fast, reliable, and accurate CVD/stroke risk stratification.

SUMMARY:

We conclude that ML and DL methods are very powerful tools for the characterization of CVD/stroke in patients with varying ED conditions. We anticipate a rapid growth of these tools for early and better CVD/stroke risk management in ED patients.

• Authors: Narendra N Khanna, Mahesh Maindarkar, Ajit Saxena, Puneet Ahluwalia, Sudip Paul, Saurabh K Srivastava, Elisa Cuadrado-Godia, Aditya Sharma, Tomaz Omerzu, Luca Saba, Sophie Mavrogeni, Monika Turk, John R Laird, George D Kitas, Mostafa Fatemi, Al Baha Barqawi, Martin Miner, Inder M Singh, Amer Johri, Mannudeep M Kalra, Vikas Agarwal, Kosmas I Paraskevas, Jagjit S Teji, Mostafa M Fouda, Gyan Pareek, Jasjit S Suri
• Journal: Diagnostics (Basel, Switzerland) (Diagnostics (Basel)) Vol. 12 Issue 5 (May 17 2022) ISSN: 2075-4418 [Print] Switzerland
• PMID: 35626404 (Publication Type: Journal Article, Review)