Jasjit S Suri1, Mrinalini Bhagawati2, Sudip Paul2, Athanasios Protogeron3, Petros P Sfikakis4, George D Kitas5, Narendra N Khanna6, Zoltan Ruzsa7, Aditya M Sharma8, Sanjay Saxena9, Gavino Faa10, Kosmas I Paraskevas11, John R Laird12, Amer M Johri13, Luca Saba14, Manudeep Kalra15. 1. Stroke Diagnostic and Monitoring Division, AtheroPoint™, Roseville, CA, USA. Electronic address: jasjit.suri@atheropoint.com. 2. Department of Biomedical Engineering, North-Eastern Hill University, Shillong, India. 3. Department of Cardiovascular Prevention & Research Unit Clinic & Laboratory of Pathophysiology, National and Kapodistrian University of Athens, Greece. 4. Rheumatology Unit, National Kapodistrian University of Athens, Greece. 5. Arthritis Research UK Centre for Epidemiology, Manchester University, Manchester, UK. 6. Department of Cardiology, Indraprastha APOLLO Hospitals, New Delhi, India. 7. Semmelweis University, Budapest, Hungary. 8. Division of Cardiovascular Medicine, University of Virginia, Charlottesville, VA, USA. 9. Department of CSE, International Institute of Information Technology, Bhubaneswar, India. 10. Department of Pathology, A.O.U., di Cagliari -Polo di Monserrato s.s, Cagliari, Italy. 11. Department of Vascular Surgery, Central Clinic of Athens, N. Iraklio, Athens, 14122, Greece. 12. Cardiology Department, St. Helena Hospital, St. Helena, CA, USA. 13. Department of Medicine, Division of Cardiology, Queen's University, Kingston, Canada. 14. Department of Radiology, University of Cagliari, Italy. 15. Department of Radiology, Massachusetts General Hospital, Boston, MA, USA.
Abstract
BACKGROUND: Artificial Intelligence (AI), in particular, machine learning (ML) has shown promising results in coronary artery disease (CAD) or cardiovascular disease (CVD) risk prediction. Bias in ML systems is of great interest due to its over-performance and poor clinical delivery. The main objective is to understand the nature of risk-of-bias (RoB) in ML and non-ML studies for CVD risk prediction. METHODS: PRISMA model was used to shortlisting 117 studies, which were analyzed to understand the RoB in ML and non-ML using 46 and 32 attributes, respectively. The mean score for each study was computed and then ranked into three ML and non-ML bias categories, namely low-bias (LB), moderate-bias (MB), and high-bias (HB), derived using two cutoffs. Further, bias computation was validated using the analytical slope method. RESULTS: Five types of the gold standard were identified in the ML design for CAD/CVD risk prediction. The low-moderate and moderate-high bias cutoffs for 24 ML studies (5, 10, and 9 studies for each LB, MB, and HB) and 14 non-ML (3, 4, and 7 studies for each LB, MB, and HB) were in the range of 1.5 to 1.95. BiasML< Biasnon-ML by ∼43%. A set of recommendations were proposed for lowering RoB. CONCLUSION: ML showed a lower bias compared to non-ML. For a robust ML-based CAD/CVD prediction design, it is vital to have (i) stronger outcomes like death or CAC score or coronary artery stenosis; (ii) ensuring scientific/clinical validation; (iii) adaptation of multiethnic groups while practicing unseen AI; (iv) amalgamation of conventional, laboratory, image-based and medication-based biomarkers.
BACKGROUND: Artificial Intelligence (AI), in particular, machine learning (ML) has shown promising results in coronary artery disease (CAD) or cardiovascular disease (CVD) risk prediction. Bias in ML systems is of great interest due to its over-performance and poor clinical delivery. The main objective is to understand the nature of risk-of-bias (RoB) in ML and non-ML studies for CVD risk prediction. METHODS: PRISMA model was used to shortlisting 117 studies, which were analyzed to understand the RoB in ML and non-ML using 46 and 32 attributes, respectively. The mean score for each study was computed and then ranked into three ML and non-ML bias categories, namely low-bias (LB), moderate-bias (MB), and high-bias (HB), derived using two cutoffs. Further, bias computation was validated using the analytical slope method. RESULTS: Five types of the gold standard were identified in the ML design for CAD/CVD risk prediction. The low-moderate and moderate-high bias cutoffs for 24 ML studies (5, 10, and 9 studies for each LB, MB, and HB) and 14 non-ML (3, 4, and 7 studies for each LB, MB, and HB) were in the range of 1.5 to 1.95. BiasML< Biasnon-ML by ∼43%. A set of recommendations were proposed for lowering RoB. CONCLUSION: ML showed a lower bias compared to non-ML. For a robust ML-based CAD/CVD prediction design, it is vital to have (i) stronger outcomes like death or CAC score or coronary artery stenosis; (ii) ensuring scientific/clinical validation; (iii) adaptation of multiethnic groups while practicing unseen AI; (iv) amalgamation of conventional, laboratory, image-based and medication-based biomarkers.
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) Date: 2022-05-17
Authors: Mohit Agarwal; Sushant Agarwal; Luca Saba; Gian Luca Chabert; Suneet Gupta; Alessandro Carriero; Alessio Pasche; Pietro Danna; Armin Mehmedovic; Gavino Faa; Saurabh Shrivastava; Kanishka Jain; Harsh Jain; Tanay Jujaray; Inder M Singh; Monika Turk; Paramjit S Chadha; Amer M Johri; Narendra N Khanna; Sophie Mavrogeni; John R Laird; David W Sobel; Martin Miner; Antonella Balestrieri; Petros P Sfikakis; George Tsoulfas; Durga Prasanna Misra; Vikas Agarwal; George D Kitas; Jagjit S Teji; Mustafa Al-Maini; Surinder K Dhanjil; Andrew Nicolaides; Aditya Sharma; Vijay Rathore; Mostafa Fatemi; Azra Alizad; Pudukode R Krishnan; Rajanikant R Yadav; Frence Nagy; Zsigmond Tamás Kincses; Zoltan Ruzsa; Subbaram Naidu; Klaudija Viskovic; Manudeep K Kalra; Jasjit S Suri Journal: Comput Biol Med Date: 2022-05-21 Impact factor: 6.698
Authors: Narendra N Khanna; Mahesh Maindarkar; Anudeep Puvvula; Sudip Paul; Mrinalini Bhagawati; Puneet Ahluwalia; Zoltan Ruzsa; Aditya Sharma; Smiksha Munjral; Raghu Kolluri; Padukone R Krishnan; Inder M Singh; John R Laird; Mostafa Fatemi; Azra Alizad; Surinder K Dhanjil; Luca Saba; Antonella Balestrieri; Gavino Faa; Kosmas I Paraskevas; Durga Prasanna Misra; Vikas Agarwal; Aman Sharma; Jagjit Teji; Mustafa Al-Maini; Andrew Nicolaides; Vijay Rathore; Subbaram Naidu; Kiera Liblik; Amer M Johri; Monika Turk; David W Sobel; Gyan Pareek; Martin Miner; Klaudija Viskovic; George Tsoulfas; Athanasios D Protogerou; Sophie Mavrogeni; George D Kitas; Mostafa M Fouda; Manudeep K Kalra; Jasjit S Suri Journal: J Cardiovasc Dev Dis Date: 2022-08-15
Authors: Jasjit S Suri; Mahesh A Maindarkar; Sudip Paul; Puneet Ahluwalia; Mrinalini Bhagawati; Luca Saba; Gavino Faa; Sanjay Saxena; Inder M Singh; Paramjit S Chadha; Monika Turk; Amer Johri; Narendra N Khanna; Klaudija Viskovic; Sofia Mavrogeni; John R Laird; Martin Miner; David W Sobel; Antonella Balestrieri; Petros P Sfikakis; George Tsoulfas; Athanase D Protogerou; Durga Prasanna Misra; Vikas Agarwal; George D Kitas; Raghu Kolluri; Jagjit S Teji; Mustafa Al-Maini; Surinder K Dhanjil; Meyypan Sockalingam; Ajit Saxena; Aditya Sharma; Vijay Rathore; Mostafa Fatemi; Azra Alizad; Padukode R Krishnan; Tomaz Omerzu; Subbaram Naidu; Andrew Nicolaides; Kosmas I Paraskevas; Mannudeep Kalra; Zoltán Ruzsa; Mostafa M Fouda Journal: Diagnostics (Basel) Date: 2022-06-24