TY - JOUR
T1 - Cardiovascular/Stroke Risk Stratification in Diabetic Foot Infection Patients Using Deep Learning-Based Artificial Intelligence
T2 - An Investigative Study
AU - Khanna, Narendra N.
AU - Maindarkar, Mahesh A.
AU - Viswanathan, Vijay
AU - Puvvula, Anudeep
AU - Paul, Sudip
AU - Bhagawati, Mrinalini
AU - Ahluwalia, Puneet
AU - Ruzsa, Zoltan
AU - Sharma, Aditya
AU - Kolluri, Raghu
AU - Krishnan, Padukone R.
AU - Singh, Inder M.
AU - Laird, John R.
AU - Fatemi, Mostafa
AU - Alizad, Azra
AU - Dhanjil, Surinder K.
AU - Saba, Luca
AU - Balestrieri, Antonella
AU - Faa, Gavino
AU - Paraskevas, Kosmas I.
AU - Misra, Durga Prasanna
AU - Agarwal, Vikas
AU - Sharma, Aman
AU - Teji, Jagjit S.
AU - Al-Maini, Mustafa
AU - Nicolaides, Andrew
AU - Rathore, Vijay
AU - Naidu, Subbaram
AU - Liblik, Kiera
AU - Johri, Amer M.
AU - Turk, Monika
AU - Sobel, David W.
AU - Miner, Martin
AU - Viskovic, Klaudija
AU - Tsoulfas, George
AU - Protogerou, Athanasios D.
AU - Mavrogeni, Sophie
AU - Kitas, George D.
AU - Fouda, Mostafa M.
AU - Kalra, Mannudeep K.
AU - Suri, Jasjit S.
N1 - Publisher Copyright:
© 2022 by the authors.
PY - 2022/11
Y1 - 2022/11
N2 - 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.
AB - 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.
KW - AI bias
KW - cardiovascular/stroke risk stratification
KW - deep learning
KW - diabetics
KW - diabetic’s foot infection
UR - http://www.scopus.com/inward/record.url?scp=85142602846&partnerID=8YFLogxK
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U2 - 10.3390/jcm11226844
DO - 10.3390/jcm11226844
M3 - Review article
AN - SCOPUS:85142602846
SN - 2077-0383
VL - 11
JO - Journal of Clinical Medicine
JF - Journal of Clinical Medicine
IS - 22
M1 - 6844
ER -