Cardiovascular/Stroke Risk Stratification in Diabetic Foot Infection Patients Using Deep Learning-Based Artificial Intelligence: An Investigative Study
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 pa...
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MDPI AG
2022-11-01
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| Series: | Journal of Clinical Medicine |
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| Online Access: | https://www.mdpi.com/2077-0383/11/22/6844 |
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| author | 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 |
| author_facet | 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 |
| author_sort | Narendra N. Khanna |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T18:15:03Z |
| format | Article |
| id | doaj.art-c128f36f5d3944c9a17a24b1ad9a345c |
| institution | Directory Open Access Journal |
| issn | 2077-0383 |
| language | English |
| last_indexed | 2024-03-09T18:15:03Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Clinical Medicine |
| spelling | doaj.art-c128f36f5d3944c9a17a24b1ad9a345c2023-11-24T08:51:06ZengMDPI AGJournal of Clinical Medicine2077-03832022-11-011122684410.3390/jcm11226844Cardiovascular/Stroke Risk Stratification in Diabetic Foot Infection Patients Using Deep Learning-Based Artificial Intelligence: An Investigative StudyNarendra N. Khanna0Mahesh A. Maindarkar1Vijay Viswanathan2Anudeep Puvvula3Sudip Paul4Mrinalini Bhagawati5Puneet Ahluwalia6Zoltan Ruzsa7Aditya Sharma8Raghu Kolluri9Padukone R. Krishnan10Inder M. Singh11John R. Laird12Mostafa Fatemi13Azra Alizad14Surinder K. Dhanjil15Luca Saba16Antonella Balestrieri17Gavino Faa18Kosmas I. Paraskevas19Durga Prasanna Misra20Vikas Agarwal21Aman Sharma22Jagjit S. Teji23Mustafa Al-Maini24Andrew Nicolaides25Vijay Rathore26Subbaram Naidu27Kiera Liblik28Amer M. Johri29Monika Turk30David W. Sobel31Martin Miner32Klaudija Viskovic33George Tsoulfas34Athanasios D. Protogerou35Sophie Mavrogeni36George D. Kitas37Mostafa M. Fouda38Mannudeep K. Kalra39Jasjit S. Suri40Department of Cardiology, Indraprastha APOLLO Hospitals, New Delhi 110001, IndiaStroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA 95661, USAMV Diabetes Centre, Royapuram, Chennai 600013, IndiaStroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA 95661, USADepartment of Biomedical Engineering, North Eastern Hill University, Shillong 793022, IndiaDepartment of Biomedical Engineering, North Eastern Hill University, Shillong 793022, IndiaMax Institute of Cancer Care, Max Super Specialty Hospital, New Delhi 110017, IndiaInvasive Cardiology Division, Faculty of Medicine, University of Szeged, 6720 Szeged, HungaryDivision of Cardiovascular Medicine, University of Virginia, Charlottesville, VA 22904, USAOhio Health Heart and Vascular, Columbus, OH 43214, USANeurology Department, Fortis Hospital, Bangalore 560076, IndiaStroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA 95661, USAHeart and Vascular Institute, Adventist Health St. Helena, St Helena, CA 94574, USADepartment of Physiology & Biomedical Engineering, Mayo Clinic College of Medicine and Science, Rochester, MN 55905, USADepartment of Radiology, Mayo Clinic College of Medicine and Science, Rochester, MN 55905, USAStroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA 95661, USADepartment of Radiology, Azienda Ospedaliero Universitaria, 40138 Cagliari, ItalyCardiovascular Prevention and Research Unit, Department of Pathophysiology, National & Kapodistrian University of Athens, 15772 Athens, GreeceDepartment of Pathology, Azienda Ospedaliero Universitaria, 09124 Cagliari, ItalyDepartment of Vascular Surgery, Central Clinic of Athens, 15772 Athens, GreeceDepartment of Immunology, SGPGIMS, Lucknow 226014, IndiaDepartment of Immunology, SGPGIMS, Lucknow 226014, IndiaDepartment of Immunology, SGPGIMS, Lucknow 226014, IndiaAnn and Robert H. Lurie Children’s Hospital of Chicago, Chicago, IL 60611, USAAllergy, Clinical Immunology and Rheumatology Institute, Toronto, ON L4Z 4C4, CanadaVascular Screening and Diagnostic Centre, University of Nicosia Medical School, Egkomi 2408, CyprusAtheroPoint™, Roseville, CA 95661, USAElectrical Engineering Department, University of Minnesota, Duluth, MN 55812, USADepartment of Medicine, Division of Cardiology, Queen’s University, Kingston, ON K7L 3N6, CanadaDepartment of Medicine, Division of Cardiology, Queen’s University, Kingston, ON K7L 3N6, CanadaThe Hanse-Wissenschaftskolleg Institute for Advanced Study, 27753 Delmenhorst, GermanyRheumatology Unit, National Kapodistrian University of Athens, 15772 Athens, GreeceMen’s Health Centre, Miriam Hospital Providence, Providence, RI 02906, USADepartment of Radiology and Ultrasound, University Hospital for Infectious Diseases, 10000 Zagreb, CroatiaDepartment of Surgery, Aristoteleion University of Thessaloniki, 54124 Thessaloniki, GreeceCardiovascular Prevention and Research Unit, Department of Pathophysiology, National & Kapodistrian University of Athens, 15772 Athens, GreeceCardiology Clinic, Onassis Cardiac Surgery Centre, 17674 Athens, GreeceAcademic Affairs, Dudley Group NHS Foundation Trust, Dudley DY1 2HQ, UKDepartment of Electrical and Computer Engineering, Idaho State University, Pocatello, ID 83209, USADepartment of Radiology, Harvard Medical School, Boston, MA 02115, USAStroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA 95661, USAA 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.https://www.mdpi.com/2077-0383/11/22/6844diabeticsdiabetic’s foot infectioncardiovascular/stroke risk stratificationdeep learningAI bias |
| spellingShingle | 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 Cardiovascular/Stroke Risk Stratification in Diabetic Foot Infection Patients Using Deep Learning-Based Artificial Intelligence: An Investigative Study Journal of Clinical Medicine diabetics diabetic’s foot infection cardiovascular/stroke risk stratification deep learning AI bias |
| title | Cardiovascular/Stroke Risk Stratification in Diabetic Foot Infection Patients Using Deep Learning-Based Artificial Intelligence: An Investigative Study |
| title_full | Cardiovascular/Stroke Risk Stratification in Diabetic Foot Infection Patients Using Deep Learning-Based Artificial Intelligence: An Investigative Study |
| title_fullStr | Cardiovascular/Stroke Risk Stratification in Diabetic Foot Infection Patients Using Deep Learning-Based Artificial Intelligence: An Investigative Study |
| title_full_unstemmed | Cardiovascular/Stroke Risk Stratification in Diabetic Foot Infection Patients Using Deep Learning-Based Artificial Intelligence: An Investigative Study |
| title_short | Cardiovascular/Stroke Risk Stratification in Diabetic Foot Infection Patients Using Deep Learning-Based Artificial Intelligence: An Investigative Study |
| title_sort | cardiovascular stroke risk stratification in diabetic foot infection patients using deep learning based artificial intelligence an investigative study |
| topic | diabetics diabetic’s foot infection cardiovascular/stroke risk stratification deep learning AI bias |
| url | https://www.mdpi.com/2077-0383/11/22/6844 |
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