Major adverse cardiovascular events (MACE) are severe complications of peripheral arterial disease (PAD), associated with poor prognosis and disease burden. Therefore, the early identification of high-risk individuals is of paramount importance. This study aimed to develop and validate an interpretable machine learning-based prediction model for MACE risk in patients with PAD.

This retrospective study included patients with PAD enrolled between January 2022 and December 2023, with follow-up completed in December 2024. The primary outcome was MACE, defined as a composite of myocardial infarction, stroke, and cardiovascular mortality, and patients were followed up for 12-24 months using data sourced from non-overlapping datasets of four centers: three for model training and internal validation; and one for external validation. Feature selection was performed using univariate analysis, LASSO logistic regression, and Random Forest algorithm. Ten different machine learning (ML) algorithms were employed to construct the risk prediction model. Model performance was evaluated based on discrimination and calibration. The SHapley Additive exPlanations (SHAP) method was used to visualize model features and individual case predictions. The final risk prediction model was presented as a web-based calculator.

This multicenter study involved both model development dataset (n = 1110) and external validation dataset (n = 448). Among the 1558 enrolled patients with PAD, 469 (30.1%) experienced MACE. The incidence of MACE was higher in the training cohort (32.0%, 249/777) compared to the internal validation cohort (30.6%, 102/333) and external validation cohort (26.3%, 118/448). The mean follow-up duration was 19.0 ± 11.3 months. Participants' mean age was 73.1 ± 10.8 years, with males comprising 70.0% (1091/1558). We developed ML models incorporating eight clinically significant variables, with Gradient Boosting (GraBoost) demonstrating comparatively better performance by achieving AUC values of 0.864 (95% confidence interval [CI]: 0.822-0.905) in internal validation cohort and 0.777 (95% CI: 0.720-0.833) in external validation cohort. The key predictors included: polyvascular disease, cerebrovascular disease, hemoglobin A1c, C-reactive protein, albumin, peripheral arterial surgery, coronary heart disease, and neutrophils.

The GraBoost algorithm outperformed other models in predicting MACE risk in patients with PAD, with external validation confirming its clinical applicability. The SHAP framework and web-based calculator enhanced the model's interpretability, enabling clinicians to better understand the factors contributing to MACE. This tool potentially helps clinicians identify MACE risk of patients with PAD and implement preventive measures more effectively.

Sepsis is a severe complication in hospitalized patients with diabetic foot (DF), often associated with high morbidity and mortality. Despite its clinical significance, limited tools exist for early risk prediction.

To identify key risk factors and evaluate the predictive value of a nomogram model for sepsis in this population.

This retrospective study included 216 patients with DF admitted from January 2022 to June 2024. Patients were classified into sepsis (n = 31) and non-sepsis (n = 185) groups. Baseline characteristics, clinical parameters, and laboratory data were analyzed. Independent risk factors were identified through multivariable logistic regression, and a nomogram model was developed and validated. The model's performance was assessed by its discrimination (AUC), calibration (Hosmer-Lemeshow test, calibration plots), and clinical utility [decision curve analysis (DCA)].

The multivariable analysis identified six independent predictors of sepsis: Diabetes duration, DF Texas grade, white blood cell count, glycated hemoglobin, C-reactive protein, and albumin. A nomogram integrating these factors achieved excellent diagnostic performance, with an AUC of 0.908 (95%CI: 0.865-0.956) and robust internal validation (AUC: 0.906). Calibration results showed strong agreement between predicted and observed probabilities (Hosmer-Lemeshow P = 0.926). DCA demonstrated superior net benefit compared to extreme intervention scenarios, highlighting its clinical utility.

The nomogram prediction model, based on six key risk factors, demonstrates strong predictive value, calibration, and clinical utility for sepsis in patients with DF. This tool offers a practical approach for early risk stratification, enabling timely interventions and improved clinical management in this high-risk population.

Objective: The primary objective of this study is to enhance the detection and staging of pressure injuries using machine learning capabilities for precise image analysis. This study explores the application of the You Only Look Once version 8 (YOLOv8) deep learning model for pressure injury staging. Approach: We prepared a high-quality, publicly available dataset to evaluate different variants of YOLOv8 (YOLOv8n, YOLOv8s, YOLOv8m, YOLOv8l, and YOLOv8x) and five optimizers (Adam, AdamW, NAdam, RAdam, and stochastic gradient descent) to determine the most effective configuration. We followed a simulation-based research approach, which is an extension of the Consolidated Standards of Reporting Trials (CONSORT) and Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines for dataset preparation and algorithm evaluation. Results: YOLOv8s, with the AdamW optimizer and hyperparameter tuning, achieved the best performance metrics, including a mean average precision at intersection over union ≥0.5 of 84.16% and a recall of 82.31%, surpassing previous YOLO-based models in accuracy. The ensemble model incorporating all YOLOv8 variants showed strong performance when applied to unseen images. Innovation: Notably, the YOLOv8s model significantly improved detection for challenging stages such as Stage 2 and achieved accuracy rates of 0.90 for deep tissue injury, 0.91 for Unstageable, and 0.74, 0.76, 0.70, and 0.77 for Stages 1, 2, 3, and 4, respectively. Conclusion: These results demonstrate the effectiveness of YOLOv8s and ensemble models in improving the accuracy and robustness of pressure injury staging, offering a reliable tool for clinical decision-making.

Chronic wounds, which take over four weeks to heal, are a major global health issue linked to conditions such as diabetes, venous insufficiency, arterial diseases, and pressure ulcers. These wounds cause pain, reduce quality of life, and impose significant economic burdens. This systematic review explores the impact of technological advancements on the diagnosis of chronic wounds, focusing on how computational methods in wound image and data analysis improve diagnostic precision and patient outcomes. A literature search was conducted in databases including ACM, IEEE, PubMed, Scopus, and Web of Science, covering studies from 2013 to 2023. The focus was on articles applying complex computational techniques to analyze chronic wound images and clinical data. Exclusion criteria were non-image samples, review articles, and non-English or non-Spanish texts. From 2,791 articles identified, 93 full-text studies were selected for final analysis. The review identified significant advancements in tissue classification, wound measurement, segmentation, prediction of wound aetiology, risk indicators, and healing potential. The use of image-based and data-driven methods has proven to enhance diagnostic accuracy and treatment efficiency in chronic wound care. The integration of technology into chronic wound diagnosis has shown a transformative effect, improving diagnostic capabilities, patient care, and reducing healthcare costs. Continued research and innovation in computational techniques are essential to unlock their full potential in managing chronic wounds effectively.

Globally, diabetes mellitus poses a significant health challenge as well as the associated complications of diabetes, such as diabetic foot ulcers (DFUs). The early detection of DFUs is important in the healing process and machine learning may be able to help inform clinical staff during the treatment process.

A PRISMA-informed search of the literature was completed via the Cochrane Library and MEDLINE (OVID), EMBASE, CINAHL Plus and Scopus databases for reports published in English and in the last ten years. The primary outcome of interest was the impact of machine learning on the prediction of DFUs. The secondary outcome was the statistical performance measures reported. Data were extracted using a predesigned data extraction tool. Quality appraisal was undertaken using the evidence-based librarianship critical appraisal tool.

A total of 18 reports met the inclusion criteria. Nine reports proposed models to identify two classes, either healthy skin or a DFU. Nine reports proposed models to predict the progress of DFUs, for example, classing infection versus non-infection, or using wound characteristics to predict healing. A variety of machine learning techniques were proposed. Where reported, sensitivity = 74.53-98 %, accuracy = 64.6-99.32 %, precision = 62.9-99 %, and the F-measure = 52.05-99.0 %.

A variety of machine learning models were suggested to successfully classify DFUs from healthy skin, or to inform the prediction of DFUs. The proposed machine learning models may have the potential to inform the clinical practice of managing DFUs and may help to improve outcomes for individuals with DFUs. Future research may benefit from the development of a standard device and algorithm that detects, diagnoses and predicts the progress of DFUs.

Foot care self-management is underutilized in older adults and diabetic foot ulcers are more common in older adults. It is important to identify predictors of foot care self-management in older adults with diabetes in order to identify and support vulnerable groups. This study aimed to identify predictors of foot care self-management in older adults with diabetes using a machine learning approach.

This cross-sectional study was conducted between November 2023 and February 2024. The data were collected in the endocrinology and metabolic diseases departments of three hospitals in Turkey. Patient identification form and the Foot Care Scale for Older Diabetics (FCS-OD) were used for data collection. Gradient boosting algorithms were used to predict the variable importance. Three machine learning algorithms were used in the study: XGBoost, LightGBM and Random Forest. The algorithms were used to predict patients with a score below or above the mean FCS-OD score.

XGBoost had the best performance (AUC: 0.7469). The common predictors of the models were age (0.0534), gender (0.0038), perceived health status (0.0218), and treatment regimen (0.0027). The XGBoost model, which had the highest AUC value, also identified income level (0.0055) and A1c (0.0020) as predictors of the FCS-OD score.

The study identified age, gender, perceived health status, treatment regimen, income level and A1c as predictors of foot care self-management in older adults with diabetes. Attention should be given to improving foot care self-management among this vulnerable group.

The escalating impact of diabetes and its complications, including diabetic foot ulcers (DFUs), presents global challenges in quality of life, economics, and resources, affecting around half a billion people. DFU healing is hindered by hyperglycemia-related issues and diverse diabetes-related physiological changes, necessitating ongoing personalized care. Artificial intelligence and clinical research strive to address these challenges by facilitating early detection and efficient treatments despite resource constraints. This study establishes a standardized framework for DFU data collection, introducing a dedicated case report form, a comprehensive dataset named Zivot with patient population clinical feature breakdowns and a baseline for DFU detection using this dataset and a UNet architecture.

Following this protocol, we created the Zivot dataset consisting of 269 patients with active DFUs, and about 3700 RGB images and corresponding thermal and depth maps for the DFUs. The effectiveness of collecting a consistent and clean dataset was demonstrated using a bounding box prediction deep learning network that was constructed with EfficientNet as the feature extractor and UNet architecture. The network was trained on the Zivot dataset, and the evaluation metrics showed promising values of 0.79 and 0.86 for F1-score and mAP segmentation metrics.

This work and the Zivot database offer a foundation for further exploration of holistic and multimodal approaches to DFU research.