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
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
Affiliations
Narendra N. Khanna
Department of Cardiology, Indraprastha APOLLO Hospitals, New Delhi 110076, India
Mahesh Maindarkar
Department of Biomedical Engineering, North Eastern Hill University, Shillong 793022, India
Ajit Saxena
Department of Urology, Indraprastha APOLLO Hospitals, New Delhi 110076, India
Puneet Ahluwalia
Max Institute of Cancer Care, Max Super Specialty Hospital, New Delhi 110017, India
Sudip Paul
Department of Biomedical Engineering, North Eastern Hill University, Shillong 793022, India
Saurabh K. Srivastava
College of Computing Sciences and IT, Teerthanker Mahaveer University, Moradabad 244001, India
Elisa Cuadrado-Godia
Department of Neurology, Hospital del Mar Medical Research Institute, 08003 Barcelona, Spain
Aditya Sharma
Division of Cardiovascular Medicine, University of Virginia, Charlottesville, VA 22908, USA
Tomaz Omerzu
Department of Neurology, University Medical Centre Maribor, 2000 Maribor, Slovenia
Luca Saba
Department of Radiology, University of Cagliari, 09124 Cagliari, Italy
Sophie Mavrogeni
Cardiology Clinic, Onassis Cardiac Surgery Centre, 176 74 Athens, Greece
Monika Turk
Department of Neurology, University Medical Centre Maribor, 2000 Maribor, Slovenia
John R. Laird
Heart and Vascular Institute, Adventist Health St. Helena, St. Helena, CA 94574, USA
George D. Kitas
Academic Affairs, Dudley Group NHS Foundation Trust, Dudley DY1 2HQ, UK
Mostafa Fatemi
Department of Physiology & Biomedical Engineering, Mayo Clinic College of Medicine and Science, Rochester, NY 55905, USA
Al Baha Barqawi
Division of Urology, Department of Surgery, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
Martin Miner
Men’s Health Centre, Miriam Hospital Providence, Providence, RI 02906, USA
Inder M. Singh
Stroke Monitoring and Diagnostic Division, AtheroPoint<sup>TM</sup>, Roseville, CA 95661, USA
Amer Johri
Department of Medicine, Division of Cardiology, Queen’s University, Kingston, ON K7L 3N6, Canada
Mannudeep M. Kalra
Department of Radiology, Harvard Medical School, Boston, MA 02115, USA
Vikas Agarwal
Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow 226014, India
Kosmas I. Paraskevas
Department of Vascular Surgery, Central Clinic of Athens, 106 80 Athens, Greece
Jagjit S. Teji
Ann and Robert H. Lurie Children’s Hospital of Chicago, Chicago, IL 60611, USA
Mostafa M. Fouda
Department of Electrical and Computer Engineering, Idaho State University, Pocatello, ID 83209, USA
Gyan Pareek
Minimally Invasive Urology Institute, Brown University, Providence, RI 02912, USA
Jasjit S. Suri
Stroke Monitoring and Diagnostic Division, AtheroPoint<sup>TM</sup>, Roseville, CA 95661, USA
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.
