ReviewJournal of clinical medicine2022
Cardiovascular/Stroke Risk Stratification in Diabetic Foot Infection Patients Using Deep Learning-Based Artificial Intelligence: An Investigative Study.
Review in Journal of clinical medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
20 citing papers in PubMed, 34 citations in OpenAlex.
- Women's Cardiovascular Disease and Stroke Risk Stratification Using a Precision and Personalized Framework Embedded with an Explainable Artificial Intelligence Paradigm: A Narrative Review.Diagnostics (Basel, Switzerland) · 2026Review
- Explainable machine learning for differential diagnosis of diabetic foot infection and osteomyelitis: a two-center study and clinically applicable web calculator using routine blood biomarkers.BMC medical informatics and decision making · 2025Article
- Artificial intelligence-driven transformative applications in disease diagnosis technology.Medical review (2021) · 2025Review
- AI-based Assessment of Risk Factors for Coronary Heart Disease in Patients With Diabetes Mellitus and Construction of a Prediction Model for a Treatment Regimen.Reviews in cardiovascular medicine · 2025Article
- Transformer and Attention-Based Architectures for Segmentation of Coronary Arterial Walls in Intravascular Ultrasound: A Narrative Review.Diagnostics (Basel, Switzerland) · 2025Review
- From Serendipity to Precision: Integrating AI, Multi-Omics, and Human-Specific Models for Personalized Neuropsychiatric Care.Biomedicines · 2025Review
- Artificial intelligence-based cardiovascular/stroke risk stratification in women affected by autoimmune disorders: a narrative survey.Rheumatology international · 2025Review
- Molecular and pathophysiological intersections between arterial hypertension, pulmonary inflammation and diabetes mellitus.Frontiers in pharmacology · 2025Review
- Unravelling the cardio-renal-metabolic-foot connection in people with diabetes-related foot ulceration: a narrative review.Cardiovascular diabetology · 2024Review
- Cardiovascular Disease Risk Stratification Using Hybrid Deep Learning Paradigm: First of Its Kind on Canadian Trial Data.Diagnostics (Basel, Switzerland) · 2024Article
- Deep learning approach for cardiovascular disease risk stratification and survival analysis on a Canadian cohort.The international journal of cardiovascular imaging · 2024Article
- UltraAIGenomics: Artificial Intelligence-Based Cardiovascular Disease Risk Assessment by Fusion of Ultrasound-Based Radiomics and Genomics Features for Preventive, Personalized and Precision Medicine: A Narrative Review.Reviews in cardiovascular medicine · 2024Review
- Dermoscopy-based Radiomics Help Distinguish Basal Cell Carcinoma and Actinic Keratosis: A Large-scale Real-world Study Based on a 207-combination Machine Learning Computational Framework.Journal of Cancer · 2024Article
- The role of machine learning in advancing diabetic foot: a review.Frontiers in endocrinology · 2024Review
- Deep Learning Paradigm and Its Bias for Coronary Artery Wall Segmentation in Intravascular Ultrasound Scans: A Closer Look.Journal of cardiovascular development and disease · 2023Review
- Polygenic Risk Score for Cardiovascular Diseases in Artificial Intelligence Paradigm: A Review.Journal of Korean medical science · 2023Review
- Artificial intelligence-based preventive, personalized and precision medicine for cardiovascular disease/stroke risk assessment in rheumatoid arthritis patients: a narrative review.Rheumatology international · 2023Review
- Artificial Intelligence in Cardiology: Why So Many Great Promises and Expectations, but Still a Limited Clinical Impact?Journal of clinical medicine · 2023Article
- Human Digital Twin for Personalized Elderly Type 2 Diabetes Management.Journal of clinical medicine · 2023Article
- A deep learning model adjusting for infant gender, age, height, and weight to determine whether the individual infant suit ultrasound examination of developmental dysplasia of the hip (DDH).Frontiers in pediatrics · 2023Article
Corrections and comments
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Authors and funding
41 authors at 20 institutions in 10 countries.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.