Predictors of diabetic foot ulcers & infections

NewsBot · Jul 19, 2025

Research progress on risk prediction models for the diabetic foot
Haixia Qi
Acta Diabetologica

Objective

This study aimed to comprehensively review the latest advancements in diabetic foot risk prediction models over the past four years to address the severe challenges posed by diabetic foot ulcers, which are among the leading causes of disability and mortality among diabetic patients. Diabetic foot ulcers are characterized by their complex aetiology, pose a grave threat to life and impose enormous social and economic burdens, thus becoming a critical issue in public health that urgently requires attention. By accurately predicting the risk of diabetic foot and implementing early intervention strategies, this study aimed to reduce its incidence and mortality rates.
Methods

This study employed a systematic review and comprehensive analysis framework, conducted extensive searches of electronic databases (including PubMed, EMBASE, the Cochrane Library, CNKI, etc.) and supplemented these searches with manual literature collection to ensure comprehensive information coverage. During the literature screening and evaluation phase, strict adherence to the predetermined inclusion and exclusion criteria was maintained to guarantee the high quality of the included studies. Further detailed quality assessments, data extraction, and analysis of the selected literature were conducted, with a focus on exploring the construction strategies of risk prediction models, the selection of key variables, the evaluation indicators of model performance, and the validation methods.
Results

By comparing and analysing the differences among studies in terms of methodology, model effectiveness, and practical application potential, this study summarized the development trends of diabetic foot risk prediction models and anticipated future research directions. These findings indicate that with the assistance of advanced diabetic foot risk prediction models, potential risk factors can be identified and addressed early on, thereby effectively reducing the incidence of diabetic foot and significantly improving patients’ quality of life.
Conclusion

This study revealed that diabetic foot risk prediction models have significant effects on accurately identifying risk factors and guiding early interventions, serving as effective tools to reduce the incidence of diabetic foot. Through early identification and intervention, the prognosis and quality of life of patients can be significantly improved, providing important references and guidance for the field of public health.
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NewsBot · Jul 24, 2025

Risk Factors for First-Ever Diabetes-Related Foot Ulcer: A Systematic Review and Meta-Analysis
Tao Yan et al
Int Wound J. 2025 Aug;22(8):e70728.

We aimed to systematically review and quantify risk factors for first-ever diabetes-related foot ulcer (DFU). Four English and three Chinese electronic databases were searched for cohort and case-control studies reporting risk factors for first-ever DFU. Two researchers independently screened titles, abstracts and full text, extracted data and assessed the quality of included studies. Meta-analyses were performed for risk factors reported in at least two studies, using unadjusted odds ratios and standardised mean differences for dichotomous and continuous variables. Of 6736 potential studies screened, 23 were included in the meta-analysis and 24 in the systematic review. Twenty-eight significant risk factors for first-ever DFU were identified, including older age, obesity, male gender, unmarried status, alcohol consumption, current smoking, insufficient physical activity, longer diabetes duration, increased HbA1c, fasting plasma glucose, creatinine and triglyceride, decreased eGFR and high-density lipoprotein, high vibration perception threshold, albuminuria, low ankle-brachial pressure index ratio, cardiovascular, cerebrovascular and peripheral artery disease, retinopathy, nephropathy, neuropathy, myocardial infarction, foot deformity, skin dryness, insulin treatment and anti-hypertensive treatment. This study provides the first comprehensive synthesis of risk factors for first-ever DFU. Identifying high-risk individuals based on these factors can enhance early intervention strategies, reducing the burden of DFU in diabetes management.
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NewsBot · Aug 29, 2025

Enhancing Early Detection of Diabetic Foot Ulcers Using Deep Neural Networks
A Sharaf Eldin et al
Diagnostics (Basel). 2025 Aug 9;15(16):1996.

Background/Objectives: Diabetic foot ulcers (DFUs) remain a critical complication of diabetes, with high rates of amputation when not diagnosed early. Despite advancements in medical imaging, current DFU detection methods are often limited by their computational complexity, poor generalizability, and delayed diagnostic performance. This study presents a novel hybrid diagnostic framework that integrates traditional feature extraction methods with deep learning (DL) to improve the early real-time computer-aided detection (CAD) of DFUs. Methods: The proposed model leverages plantar thermograms to detect early thermal asymmetries associated with DFUs. It uniquely combines the oriented FAST and rotated BRIEF (ORB) algorithm with the Bag of Features (BOF) method to extract robust handcrafted features while also incorporating deep features from pretrained convolutional neural networks (ResNet50, AlexNet, and EfficientNet). These features were fused and input into a lightweight deep neural network (DNN) classifier designed for binary classification. Results: Our model demonstrated an accuracy of 98.51%, precision of 100%, sensitivity of 98.98%, and AUC of 1.00 in a publicly available plantar thermogram dataset (n = 1670 images). An ablation study confirmed the superiority of ORB + DL fusion over standalone approaches. Unlike previous DFU detection models that rely solely on either handcrafted or deep features, our study presents the first lightweight hybrid framework that integrates ORB-based descriptors with deep CNN representations (e.g., ResNet50 and EfficientNet). Compared with recent state-of-the-art models, such as DFU_VIRNet and DFU_QUTNet, our approach achieved a higher diagnostic performance (accuracy = 98.51%, AUC = 1.00) while maintaining real-time capability and a lower computational overhead, making it highly suitable for clinical deployment. Conclusions: This study proposes the first integration of ORB-based handcrafted features with deep neural representations for DFU detection from thermal images. The model delivers high accuracy, robustness to noise, and real-time capabilities, outperforming existing state-of-the-art approaches and demonstrating strong potential for clinical deployment.
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NewsBot · Sep 24, 2025

Prevalence and predictors of diabetic foot risk factors among elderly diabetic patients in family medicine clinics: a systematic review.
Alenezi AI, Alotaibi NR, Tirad RSM, Mohamed OAS, Ghandorah HK, Alshammari AH, Baabdullah MY.
IJMDC. 2025; 9(7): 1692-1699.

Among the most debilitating consequences of diabetes mellitus, diabetic foot ulcers (DFUs) and diabetes-related stress (DD) contribute significantly to cause significant morbidity, healthcare expenses, and decreased quality of life. Emphasizing behavioral, clinical, and socioeconomic determinants, this review seeks to integrate recent data on the sample demographics, DFU/DD prevalence, behavioral and clinical results, and statistically significant risk factors, and clinical correlations of DFUs and DD across several populations, especially from Middle Eastern, African, and South Asian groups. Ten observational studies published between 2013 and 2025 were descriptively synthesized. The dually examined studies showed DFU incidence ranging from 1.2% in vast population screenings to more than 80% in clinical groups with a pooled prevalence of 52% (95% CI: 21%-82%). Up to 48.6% of patients, particularly older adults and women, suffered from diabetes-related stress. Common risk factors for DFUs include peripheral neuropathy, poor foot hygiene, obesity, deformities, smoking, rural residence, and lack of family or educational support. Poor glycemic control, advanced age, and multiple comorbidities were consistently associated with increased risk of foot complications and microvascular sequelae. Behavioral factors such as sedentary lifestyle, irregular foot inspection, and infrequent clinical follow-up also significantly contributed to poor outcomes. Early detection of high-risk people, thorough foot treatment education, blood pressure and glucose optimization, and family and community support system participation are all essential for prevention and better outcomes; hence, diabetic foot complications and diabetes distress are prevalent, multifactorial problems requiring integrated and proactive management strategies.
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NewsBot · Oct 8, 2025

The predictive model and risk factor identification for peripheral vascular disease and diabetic foot in diabetes based on machine learning models and explainable algorithms
Yifeng Pan et al
Medicine (Baltimore). 2025 Oct 3;104(40):e44871.

Diabetic peripheral vascular disease (DPVD) and diabetic foot (DF) are major complications that lead to disability in diabetic patients, severely impaired their quality of life. Firstly, this study gathered cross-sectional data from 1240 patients with type 2 diabetes and its complications in the the department of vascular surgery and endocrinology of the second affiliated hospital of zhejiang university school of medicine. In the pre-processing part, samples with serious data loss are eliminated, and the data are processed by methods such as MICEforest. After that, random forest (RF), support vector machine (SVM), backpropagation neural network (BPNN), extreme gradient boosting (XGBoost), and SHapley Additive exPlanation (SHAP) were employed to rank the importance of the 27 indicators. The entropy weight method was then applied to comprehensively assign weights to all indexes. Finally, the genetic neural network algorithm (GA-BPNN) was introduced to construct a prediction model for diabetes complications. In addition, the SHAP algorithm was applied to obtain the weight and importance ranking of each risk factor in the prediction model. This study identified the top 17 key indicators through a comprehensive weighting approach. Among the 5 classification models evaluated, the GA-BPNN algorithm exhibited the best performance in both diabetes and DPVD (G1), DPVD and DF (G2), achieving the area under the receiver operating characteristic curve (AUC) values of 0.79 and 0.89, accuracy rates of 0.78 and 0.80, and F1-scores of 0.77 and 0.83, respectively. Furthermore, hypothesis testing results indicate that indicators such as fibrinogen and c-reactive protein show statistically significant differences between groups. SHAP feature importance analysis also highlights the significant influence of these features in identifying diabetic complications. GA-BPNN can be employed as a prediction model for DPVD and DF. In feature selection, the comprehensive weighting method and SHAP analysis identified key features. In summary, this study constructed a comprehensive prediction model based on machine learning and interpretable algorithms, integrating diabetes-specific indicators, traditional cardiovascular risk factors, coagulation function, inflammatory markers, and cardiac structural parameters. It can effectively identify high-risk patients for diabetic complications, uncover potential features, and thereby assist in subsequent efforts to reduce the incidence of these complications.
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NewsBot · Nov 7, 2025

A Robust DFU_CVENETB3 Deep Learning Model for Improved Accuracy in Early Ulcer Prediction Based on Diabetic Foot Images
Zainab H. Zalaan, Shaymaa Kennedy, Dalia Adil Almuhsin, Zaid Ameen Abduljabbar, Vincent Omollo Nyangaresi, Ali Noori Gatea, Hamid Ali Abed AL-Asadi & Abdulla J. Y. Aldarwish
Source

In many facets of healthcare, including diagnosis, treatment, and even epidemiology, deep learning (DL) plays a significant role. In the past, medical practice was determined exclusively by the training that physicians had received. However, the importance of artificial intelligence applications has increased along with the number of databases, and the advantages of using DL in medicine have gained more recognition. Diabetes has become a major medical concern and is a fairly common disease. This metabolic disorder increases the chance of developing numerous conditions, including foot diseases, ulcers, amputations, etc. when blood glucose levels are not properly managed. Significant morbidity is a result of diabetic foot ulcers and amputations. Diabetic foot can be avoided by identifying at-risk patients and putting preventative measures in place. We presented a novel model in this paper that combines the characteristics of Vgg16 with EfficientNetB3 and DFU_CVENETB3 Convolutional Neural Network (CNN), a very powerful potential game-changer towards improved prevention of diabetic foot ulcers. This study will introduce deep neural network models to automatically classify early diabetic foot images into normal (healthy) and diseased (DFU) categories. A set of images of diabetic foot ulcers will also be used, and the results will show us that the model based on EfficientNetB3 performed better than traditional CNN models such as VGG16. EfficientNetB3 produced the highest accuracy results, compared to previous works mentioned in the study.
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Predictors of diabetic foot ulcers & infections

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Predictors of diabetic foot ulcers & infections

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Severity and duration of diabetic foot ulcer (DFU) before seeking care as predictors of healing time

Predictors of treatment failure for diabetic foot complications

Predictors of the Biomechanical Effects of Foot Orthoses

Kinematic predictors of loading during running

Predictors of the Biomechanical Effects of Customized Foot Orthoses in Adults With Flat-Arched Feet.

Age-related reduction and independent predictors of toe flexor strength in middle-aged men

Predictors of Response to Physical Therapy Intervention for Plantar Heel Pain

Share This Page

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