The amputation and survival of patients with diabetic foot based on establishment of prediction model

Advances in biomaterials and regenerative medicine for diabetic foot ulcer therapy

Diabetic foot ulcer (DFU), a severe complication of diabetes mellitus, presents significant clinical challenges due to its rapid deterioration and high morbidity rates. While conventional therapies exist kinds of limitations, their clinical utility is frequently constrained. Recent advancements in biomedical engineering have introduced innovative therapeutic modalities, particularly nanomaterials and hydrogels. However, emerging technologies face translational barriers including immature manufacturing processes leading to elevated costs, and insufficient long-term safety data due to limited clinical validation periods. Current clinical studies remain constrained by small cohort sizes and preliminary-stage investigations. The purpose of this study was to review traditional primary treatment and simultaneously combine clinical data to increase the speed of innovative safety, cost, and effectiveness indicator testing.

An interpreting machine learning models to predict amputation risk in patients with diabetic foot ulcers: a multi-center study

Background

Diabetic foot ulcers (DFUs) constitute a significant complication among individuals with diabetes and serve as a primary cause of nontraumatic lower-extremity amputation (LEA) within this population. We aimed to develop machine learning (ML) models to predict the risk of LEA in DFU patients and used SHapley additive explanations (SHAPs) to interpret the model.

Methods

In this retrospective study, data from 1,035 patients with DFUs at Sun Yat-sen Memorial Hospital were utilized as the training cohort to develop the ML models. Data from 297 patients across multiple tertiary centers were used for external validation. We then used least absolute shrinkage and selection operator analysis to identify predictors of amputation. We developed five ML models [logistic regression (LR), support vector machine (SVM), random forest (RF), k-nearest neighbors (KNN) and extreme gradient boosting (XGBoost)] to predict LEA in DFU patients. The performance of these models was evaluated using several metrics, including the area under the receiver operating characteristic curve (AUC), decision curve analysis (DCA), precision, recall, accuracy, and F1 score. Finally, the SHAP method was used to ascertain the significance of the features and to interpret the model.

Results

In the final cohort comprising 1332 individuals, 600 patients underwent amputation. Following hyperparameter optimization, the XGBoost model achieved the best amputation prediction performance with an accuracy of 0.94, a precision of 0.96, an F1 score of 0.94 and an AUC of 0.93 for the internal validation set on the basis of the 17 features. For the external validation set, the model attained an accuracy of 0.78, a precision of 0.93, an F1 score of 0.78, and an AUC of 0.83. Through SHAP analysis, we identified white blood cell counts, lymphocyte counts, and blood urea nitrogen levels as the model's main predictors.

Conclusion

The XGBoost algorithm-based prediction model can be used to dynamically estimate the risk of LEA in DFU patients, making it a valuable tool for preventing the progression of DFUs to amputation.

Advancing Diabetic Foot Ulcer Care: AI and Generative AI Approaches for Classification, Prediction, Segmentation, and Detection

Background: Diabetic foot ulcers (DFUs) represent a significant challenge in managing diabetes, leading to higher patient complications and increased healthcare costs. Traditional approaches, such as manual wound assessment and diagnostic tool usage, often require significant resources, including skilled clinicians, specialized equipment, and extensive time. Artificial intelligence (AI) and generative AI offer promising solutions for improving DFU management. This study systematically reviews the role of AI in DFU classification, prediction, segmentation, and detection. Furthermore, it highlights the role of generative AI in overcoming data scarcity and potential of AI-based smartphone applications for remote monitoring and diagnosis. Methods: A systematic literature review was conducted following the PRISMA guidelines. Relevant studies published between 2020 and 2025 were identified from databases including PubMed, IEEE Xplore, Scopus, and Web of Science. The review focused on AI and generative AI applications in DFU and excluded non-DFU-related medical imaging articles. Results: This study indicates that AI-powered models have significantly improved DFU classification accuracy, early detection, and predictive modeling. Generative AI techniques, such as GANs and diffusion models, have demonstrated potential in addressing dataset limitations by generating synthetic DFU images. Additionally, AI-powered smartphone applications provide cost-effective solutions for DFU monitoring, potentially improving diagnosis. Conclusions: AI and generative AI are transforming DFU management by enhancing diagnostic accuracy and predictive capabilities. Future research should prioritize explainable AI frameworks and diverse datasets for AI-driven healthcare solutions to facilitate broader clinical adoption.

Prediction models for amputation after diabetic foot: systematic review and critical appraisal

BACKGROUND

Numerous studies have developed or validated prediction models aimed at estimating the likelihood of amputation in diabetic foot (DF) patients. However, the quality and applicability of these models in clinical practice and future research remain uncertain. This study conducts a systematic review and assessment of the risk of bias and applicability of amputation prediction models among individuals with DF.

METHODS

A comprehensive search was conducted across multiple databases, including PubMed, Web of Science, EBSCO CINAHL Plus, Embase, Cochrane Library, China National Knowledge Infrastructure (CNKI), Wanfang, Chinese Biomedical Literature Database (CBM), and Weipu (VIP) from their inception to December 24, 2023. Two investigators independently screened the literature and extracted data using the checklist for critical appraisal and data extraction for systematic reviews of prediction modeling studies. The Prediction Model Risk of Bias Assessment Tool (PROBAST) checklist was employed to evaluate both the risk of bias and applicability.

RESULTS

A total of 20 studies were included in this analysis, comprising 17 development studies and three validation studies, encompassing 20 prediction models and 11 classification systems. The incidence of amputation in patients with DF ranged from 5.9 to 58.5%. Machine learning-based methods were employed in more than half of the studies. The reported area under the curve (AUC) varied from 0.560 to 0.939. Independent predictors consistently identified by multivariate models included age, gender, HbA1c, hemoglobin, white blood cell count, low-density lipoprotein cholesterol, diabetes duration, and Wagner's Classification. All studies were found to exhibit a high risk of bias, primarily attributed to inadequate handling of outcome events and missing data, lack of model performance assessment, and overfitting.

CONCLUSIONS

The assessment using PROBAST revealed a notable risk of bias in the existing prediction models for amputation in patients with DF. It is imperative for future studies to concentrate on enhancing the robustness of current prediction models or constructing new models with stringent methodologies.

Risk Prediction of Diabetic Foot Amputation Using Machine Learning and Explainable Artificial Intelligence

BACKGROUND

Diabetic foot ulcers (DFUs) are serious complications of diabetes which can lead to lower extremity amputations (LEAs). Risk prediction models can identify high-risk patients who can benefit from early intervention. Machine learning (ML) methods have shown promising utility in medical applications. Explainable modeling can help its integration and acceptance. This study aims to develop a risk prediction model using ML algorithms with explainability for LEA in DFU patients.

METHODS

This study is a retrospective review of 2559 inpatient DFU episodes in a tertiary institution from 2012 to 2017. Fifty-one features including patient demographics, comorbidities, medication, wound characteristics, and laboratory results were reviewed. Outcome measures were the risk of major LEA, minor LEA and any LEA. Machine learning models were developed for each outcome, with model performance evaluated using receiver operating characteristic (ROC) curves, balanced-accuracy and F1-score. SHapley Additive exPlanations (SHAP) was applied to interpret the model for explainability.

RESULTS

Model performance for prediction of major, minor, and any LEA event achieved ROC of 0.820, 0.637, and 0.756, respectively, with XGBoost, XGBoost, and Gradient Boosted Trees algorithms demonstrating best results for each model, respectively. Using SHAP, key features that contributed to the predictions were identified for explainability. Total white cell (TWC) count, comorbidity score and red blood cell count contributed highest weightage to major LEA event. Total white cell, eosinophils, and necrotic eschar in the wound contributed most to any LEA event.

CONCLUSIONS

Machine learning algorithms performed well in predicting the risk of LEA in a patient with DFU. Explainability can help provide clinical insights and identify at-risk patients for early intervention.

Effects of evidence-based care on diabetic foot ulcers: A meta-analysis

This analysis systematically reviewed the efficacy of evidence-based care on diabetic foot ulcers. A computerised literature search was conducted for randomised controlled studies (RCTs) of evidence-based care interventions for the treatment of diabetic foot ulcers using the PubMed, Embase, Cochrane Library, Web of Science, China National Knowledge Infrastructure (CNKI), China Biomedical Literature Database (CBM) and Wanfang databases from the date of inception of each database to June 2023. The articles were independently screened, data were extracted by two researchers, and the quality of each study was assessed using the Cochrane bias assessment tool. Meta-analysis of the data was performed using RevMan 5.4 software. Twenty-five RCTs with a total of 2272 patients were included. Meta-analysis showed that, compared with other care methods, evidence-based care significantly improved the treatment efficacy of diabetic foot ulcers (odds ratio: 3.91, 95% confidence interval [CI]: 2.76 to 5.53, p < 0.001) and significantly reduced their fasting plasma glucose (mean difference [MD]: -1.10, 95% CI: -1.24 to -0.96, p < 0.001), 2-h postprandial glucose (2hPG) (MD: -1.69, 95% CI: -2.07 to -1.31, p < 0.001) and glycated haemoglobin (HbA1c) (MD: -0.71, 95% CI: -0.94 to -0.48, p < 0.001). Evidence-based care intervention is effective at reducing FPG, 2hPG and HbA1c levels and improving treatment efficacy in patients with diabetic foot ulcers.

Predicting amputation using machine learning: A systematic review

Amputation is an irreversible, last-line treatment indicated for a multitude of medical problems. Delaying amputation in favor of limb-sparing treatment may lead to increased risk of morbidity and mortality. This systematic review aims to synthesize the literature on how ML is being applied to predict amputation as an outcome. OVID Embase, OVID Medline, ACM Digital Library, Scopus, Web of Science, and IEEE Xplore were searched from inception to March 5, 2023. 1376 studies were screened; 15 articles were included. In the diabetic population, models ranged from sub-optimal to excellent performance (AUC: 0.6-0.94). In trauma patients, models had strong to excellent performance (AUC: 0.88-0.95). In patients who received amputation secondary to other etiologies (e.g.: burns and peripheral vascular disease), models had similar performance (AUC: 0.81-1.0). Many studies were found to have a high PROBAST risk of bias, most often due to small sample sizes. In conclusion, multiple machine learning models have been successfully developed that have the potential to be superior to traditional modeling techniques and prospective clinical judgment in predicting amputation. Further research is needed to overcome the limitations of current studies and to bring applicability to a clinical setting.

Multicomponent prediction of 2-year mortality and amputation in patients with diabetic foot using a random survival forest model: Uric acid, alanine transaminase, urine protein and platelet as important predictors

The current methods for the prediction of mortality and amputation for inpatients with diabetic foot (DF) use only conventional, simple variables, which limits their performance. Here, we used a random survival forest (RSF) model and multicomponent variables to improve the prediction of mortality and amputation for these patients. We performed a retrospective cohort study of 175 inpatients with DF who were recruited between 2014 and 2021. Thirty-one predictors in six categories were considered as potential covariates. Seventy percent (n = 122) of the participants were randomly selected to constitute a training set, and 30% (n = 53) were assigned to a testing set. The RSF model was used to screen appropriate variables for their value as predictors of 2-year all-cause mortality and amputation, and a multicomponent prediction model was established. Model performance was evaluated using the area under the curve (AUC) and the Hosmer-Lemeshow test. The AUCs were compared using the Delong test. Seventeen variables were selected to predict mortality and 23 were selected to predict amputation. Uric acid and alanine transaminase were the top two most useful variables for the prediction of mortality, whereas urine protein and platelet were the top variables for the prediction of amputation. The AUCs were 0.913 and 0.851 for the prediction of mortality for the training and testing sets, respectively; and the equivalent AUCs were 0.963 and 0.893 for the prediction of amputation. There were no significant differences between the AUCs for the training and testing sets for both the mortality and amputation models. These models showed a good degree of fit. Thus, the RSF model can predict mortality and amputation in inpatients with DF. This multicomponent prediction model could help clinicians consider predictors of different dimensions to effectively prevent DF from clinical outcomes .

A scientometrics analysis and visualisation of diabetic foot research from 1955 to 2022

Diabetic foot (DF) has become a serious health problem in modern society, and it has been a hotspot of research for a long time. However, little scientometric analysis has been carried out on DF. In the present study, we analysed 8633 literature reports on DF in the Web of Science Core Collection from database inception until April 23, 2022. VOSviewer (Centre for Science and Technology Studies at Leiden University, Leiden, the Netherlands) and CiteSpace (College of Computing and Informatics, Drexel University, Philadelphia, United States) were employed to address high-impact countries and institutions, journals, references, research hotspots, and key research fields in DF research. Our analysis findings indicated that publications on DF have increased markedly since 2016 and were primarily published in the United States of America. The recent studies focus on the amniotic membrane, foot ulcers, osteomyelitis, and diabetic wound healing. The five keyword clusters, which included DF ulcer and wound healing therapies, management and guidelines, neuropathy and plantar pressure, amputation and ischemia, and DF infection and osteomyelitis, are helpful for enhancing prevention, standardising treatment, avoiding complications, and improving prognosis. These findings indicated a method for future therapies and research in DF.

Antibiotic-loaded bone substitutes therapy in the management of the moderate to severe diabetic foot infection: A meta-analysis

In recent years, local antibiotic-loaded bone substitutes (ALBS) have been used increasingly in the treatment of diabetic foot infection (DFI). The meta-analysis aimed to analyse the efficacy of ALBS on patients with moderate to severe DFI (with or without osteomyelitis). With an appropriate search strategy, 7 studies were selected for analysis (2 RCTs and 5 cohort studies). The result showed that the application of ALBS effectively reduced the length of hospital stay (WMD -5.55; 95% CI: -9.85 to -1.26; P = 0.01), the recurrence rates (RR 0.33; 95% CI: 0.15 to 0.69; P = 0.003) and the mortality rates (RR 0.22; 95% CI: 0.06 to 0.82; P = 0.02). Compared to the control groups, however, there was no difference in healing rates (RR 1.06; 95% CI: 0.96 to 1.18; P = 0.26), healing time (WMD -1.44; 95% CI: -3.37 to -0.49; P = 0.14), the number of debridement (WMD -1.98; 95% CI: -4.08 to 0.12; P = 0.06) and major amputation rates (RR 0.76; 95% CI: 0.35 to 1.61; P = 0.47). The ALBS appears to have some beneficial effects as an adjunct to standard surgery in the treatment of DFI with or without osteomyelitis, as it reduces recurrence rates, mortality rates, and length of hospital stay, but there was no statistically significant difference in enhancing wound healing.

Status and influencing factors of lower limb amputation in patients with diabetic foot ulcer

To investigate the influencing factors of lower limb amputation in patients with diabetic foot ulcers. Patients with diabetic foot ulcers who were hospitalised in a tertiary general hospital in Guizhou Province from January 2019 to March 2022 were retrospectively collected. Sociological information of the general population, comorbidities, laboratory-related indicators, and information on the specialty situation, using univariate analysis and multifactor analysis, compared the influencing factors of amputation and non-amputee patients. A total of 205 patients with diabetic foot and 69 ampute patients (33.7%) were enrolled. The univariate analysis found that the decrease in HDL cholesterol levels was associated with the occurrence of lower extremity amputation, and logistic stepwise regression analysis showed that HDL-C was inversely correlated with the amputation rate of patients with diabetic foot ulcers, and the risk of amputation at low levels of HDL-C was 2.452 times higher than that of high-level HDL-C (95% CI: 1.105-5.846). Decreased HDL cholesterol levels are an independent predictor of amputation in patients with diabetic foot ulcers.

Artificial Intelligence Methodologies Applied to Technologies for Screening, Diagnosis and Care of the Diabetic Foot: A Narrative Review

Diabetic foot syndrome is a multifactorial pathology with at least three main etiological factors, i.e., peripheral neuropathy, peripheral arterial disease, and infection. In addition to complexity, another distinctive trait of diabetic foot syndrome is its insidiousness, due to a frequent lack of early symptoms. In recent years, it has become clear that the prevalence of diabetic foot syndrome is increasing, and it is among the diabetes complications with a stronger impact on patient's quality of life. Considering the complex nature of this syndrome, artificial intelligence (AI) methodologies appear adequate to address aspects such as timely screening for the identification of the risk for foot ulcers (or, even worse, for amputation), based on appropriate sensor technologies. In this review, we summarize the main findings of the pertinent studies in the field, paying attention to both the AI-based methodological aspects and the main physiological/clinical study outcomes. The analyzed studies show that AI application to data derived by different technologies provides promising results, but in our opinion future studies may benefit from inclusion of quantitative measures based on simple sensors, which are still scarcely exploited.

Machine learning in vascular surgery: a systematic review and critical appraisal

Machine learning (ML) is a rapidly advancing field with increasing utility in health care. We conducted a systematic review and critical appraisal of ML applications in vascular surgery. MEDLINE, Embase, and Cochrane CENTRAL were searched from inception to March 1, 2021. Study screening, data extraction, and quality assessment were performed by two independent reviewers, with a third author resolving discrepancies. All original studies reporting ML applications in vascular surgery were included. Publication trends, disease conditions, methodologies, and outcomes were summarized. Critical appraisal was conducted using the PROBAST risk-of-bias and TRIPOD reporting adherence tools. We included 212 studies from a pool of 2235 unique articles. ML techniques were used for diagnosis, prognosis, and image segmentation in carotid stenosis, aortic aneurysm/dissection, peripheral artery disease, diabetic foot ulcer, venous disease, and renal artery stenosis. The number of publications on ML in vascular surgery increased from 1 (1991-1996) to 118 (2016-2021). Most studies were retrospective and single center, with no randomized controlled trials. The median area under the receiver operating characteristic curve (AUROC) was 0.88 (range 0.61-1.00), with 79.5% [62/78] studies reporting AUROC ≥ 0.80. Out of 22 studies comparing ML techniques to existing prediction tools, clinicians, or traditional regression models, 20 performed better and 2 performed similarly. Overall, 94.8% (201/212) studies had high risk-of-bias and adherence to reporting standards was poor with a rate of 41.4%. Despite improvements over time, study quality and reporting remain inadequate. Future studies should consider standardized tools such as PROBAST and TRIPOD to improve study quality and clinical applicability.