| Autor: |
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 |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Předmět: |
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| Zdroj: |
Diagnostics, Vol 12, Iss 5, p 1249 (2022) |
| Druh dokumentu: |
article |
| ISSN: |
2075-4418 |
| DOI: |
10.3390/diagnostics12051249 |
| Popis: |
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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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