Journal of Clinical Medicine (Nov 2022)
Cardiovascular/Stroke Risk Stratification in Diabetic Foot Infection Patients Using Deep Learning-Based Artificial Intelligence: An Investigative Study
- Narendra N. Khanna,
- Mahesh A. Maindarkar,
- Vijay Viswanathan,
- Anudeep Puvvula,
- Sudip Paul,
- Mrinalini Bhagawati,
- Puneet Ahluwalia,
- Zoltan Ruzsa,
- Aditya Sharma,
- 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 S. Teji,
- Mustafa Al-Maini,
- Andrew Nicolaides,
- Vijay Rathore,
- Subbaram Naidu,
- Kiera Liblik,
- Amer M. Johri,
- Monika Turk,
- David W. Sobel,
- Martin Miner,
- Klaudija Viskovic,
- George Tsoulfas,
- Athanasios D. Protogerou,
- Sophie Mavrogeni,
- George D. Kitas,
- Mostafa M. Fouda,
- Mannudeep K. Kalra,
- Jasjit S. Suri
Affiliations
- Narendra N. Khanna
- Department of Cardiology, Indraprastha APOLLO Hospitals, New Delhi 110001, India
- Mahesh A. Maindarkar
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA 95661, USA
- Vijay Viswanathan
- MV Diabetes Centre, Royapuram, Chennai 600013, India
- Anudeep Puvvula
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA 95661, USA
- Sudip Paul
- Department of Biomedical Engineering, North Eastern Hill University, Shillong 793022, India
- Mrinalini Bhagawati
- Department of Biomedical Engineering, North Eastern Hill University, Shillong 793022, India
- Puneet Ahluwalia
- Max Institute of Cancer Care, Max Super Specialty Hospital, New Delhi 110017, India
- Zoltan Ruzsa
- Invasive Cardiology Division, Faculty of Medicine, University of Szeged, 6720 Szeged, Hungary
- Aditya Sharma
- Division of Cardiovascular Medicine, University of Virginia, Charlottesville, VA 22904, USA
- Raghu Kolluri
- Ohio Health Heart and Vascular, Columbus, OH 43214, USA
- Padukone R. Krishnan
- Neurology Department, Fortis Hospital, Bangalore 560076, India
- Inder M. Singh
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA 95661, USA
- John R. Laird
- Heart and Vascular Institute, Adventist Health St. Helena, St Helena, CA 94574, USA
- Mostafa Fatemi
- Department of Physiology & Biomedical Engineering, Mayo Clinic College of Medicine and Science, Rochester, MN 55905, USA
- Azra Alizad
- Department of Radiology, Mayo Clinic College of Medicine and Science, Rochester, MN 55905, USA
- Surinder K. Dhanjil
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA 95661, USA
- Luca Saba
- Department of Radiology, Azienda Ospedaliero Universitaria, 40138 Cagliari, Italy
- Antonella Balestrieri
- Cardiovascular Prevention and Research Unit, Department of Pathophysiology, National & Kapodistrian University of Athens, 15772 Athens, Greece
- Gavino Faa
- Department of Pathology, Azienda Ospedaliero Universitaria, 09124 Cagliari, Italy
- Kosmas I. Paraskevas
- Department of Vascular Surgery, Central Clinic of Athens, 15772 Athens, Greece
- Durga Prasanna Misra
- Department of Immunology, SGPGIMS, Lucknow 226014, India
- Vikas Agarwal
- Department of Immunology, SGPGIMS, Lucknow 226014, India
- Aman Sharma
- Department of Immunology, SGPGIMS, Lucknow 226014, India
- Jagjit S. Teji
- Ann and Robert H. Lurie Children’s Hospital of Chicago, Chicago, IL 60611, USA
- Mustafa Al-Maini
- Allergy, Clinical Immunology and Rheumatology Institute, Toronto, ON L4Z 4C4, Canada
- Andrew Nicolaides
- Vascular Screening and Diagnostic Centre, University of Nicosia Medical School, Egkomi 2408, Cyprus
- Vijay Rathore
- AtheroPoint™, Roseville, CA 95661, USA
- Subbaram Naidu
- Electrical Engineering Department, University of Minnesota, Duluth, MN 55812, USA
- Kiera Liblik
- Department of Medicine, Division of Cardiology, Queen’s University, Kingston, ON K7L 3N6, Canada
- Amer M. Johri
- Department of Medicine, Division of Cardiology, Queen’s University, Kingston, ON K7L 3N6, Canada
- Monika Turk
- The Hanse-Wissenschaftskolleg Institute for Advanced Study, 27753 Delmenhorst, Germany
- David W. Sobel
- Rheumatology Unit, National Kapodistrian University of Athens, 15772 Athens, Greece
- Martin Miner
- Men’s Health Centre, Miriam Hospital Providence, Providence, RI 02906, USA
- Klaudija Viskovic
- Department of Radiology and Ultrasound, University Hospital for Infectious Diseases, 10000 Zagreb, Croatia
- George Tsoulfas
- Department of Surgery, Aristoteleion University of Thessaloniki, 54124 Thessaloniki, Greece
- Athanasios D. Protogerou
- Cardiovascular Prevention and Research Unit, Department of Pathophysiology, National & Kapodistrian University of Athens, 15772 Athens, Greece
- Sophie Mavrogeni
- Cardiology Clinic, Onassis Cardiac Surgery Centre, 17674 Athens, Greece
- George D. Kitas
- Academic Affairs, Dudley Group NHS Foundation Trust, Dudley DY1 2HQ, UK
- Mostafa M. Fouda
- Department of Electrical and Computer Engineering, Idaho State University, Pocatello, ID 83209, USA
- Mannudeep K. Kalra
- Department of Radiology, Harvard Medical School, Boston, MA 02115, USA
- Jasjit S. Suri
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA 95661, USA
- DOI
- https://doi.org/10.3390/jcm11226844
- Journal volume & issue
-
Vol. 11,
no. 22
p. 6844
Abstract
A diabetic foot infection (DFI) is among the most serious, incurable, and costly to treat conditions. The presence of a DFI renders machine learning (ML) systems extremely nonlinear, posing difficulties in CVD/stroke risk stratification. In addition, there is a limited number of well-explained ML paradigms due to comorbidity, sample size limits, and weak scientific and clinical validation methodologies. Deep neural networks (DNN) are potent machines for learning that generalize nonlinear situations. The objective of this article is to propose a novel investigation of deep learning (DL) solutions for predicting CVD/stroke risk in DFI patients. The Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) search strategy was used for the selection of 207 studies. We hypothesize that a DFI is responsible for increased morbidity and mortality due to the worsening of atherosclerotic disease and affecting coronary artery disease (CAD). Since surrogate biomarkers for CAD, such as carotid artery disease, can be used for monitoring CVD, we can thus use a DL-based model, namely, Long Short-Term Memory (LSTM) and Recurrent Neural Networks (RNN) for CVD/stroke risk prediction in DFI patients, which combines covariates such as office and laboratory-based biomarkers, carotid ultrasound image phenotype (CUSIP) lesions, along with the DFI severity. We confirmed the viability of CVD/stroke risk stratification in the DFI patients. Strong designs were found in the research of the DL architectures for CVD/stroke risk stratification. Finally, we analyzed the AI bias and proposed strategies for the early diagnosis of CVD/stroke in DFI patients. Since DFI patients have an aggressive atherosclerotic disease, leading to prominent CVD/stroke risk, we, therefore, conclude that the DL paradigm is very effective for predicting the risk of CVD/stroke in DFI patients.
Keywords