Management of Prediction and Classifying of Wound Healing Results in Plastic and Reconstructive Surgery Based on Machine Learning Models

The Identification of Risk Factors in the Development of Wound Healing Complications in Soft Tissue Sarcoma Patients

ABSTRACT Wound‐healing complications (WHCs) remain a significant challenge in soft tissue sarcoma (STS) surgery, contributing to increased morbidity, prolonged hospitalisation, and impaired functional outcomes. This study aimed to identify predictors of WHC development in STS patients to improve therapeutic strategies and patient recovery. We conducted a single‐institution, retrospective analysis of 318 STS patients treated between April 2019 and June 2021. Variables assessed included demographic data, tumour characteristics, treatment modalities, surgical interventions, and follow‐up outcomes. Statistical analyses used chi‐square tests, Kaplan–Meier estimates, and multivariate regression. WHCs occurred in 30.8% of patients. Significant negative predictors included high tumour grade (p = 0.018), presence of metastasis (p < 0.001), bone tissue resection (p = 0.005), number of revision procedures (p < 0.001), secondary wound closure (p < 0.001), and tumour relapse frequency (p = 0.012). WHCs were also associated with increased risk of death (p = 0.004) and longer initial hospitalisation (p < 0.001). Chemotherapy showed a protective effect when stratified by tumour grade. Patients with high‐grade tumours and multiple surgical interventions represent a high‐risk group for WHC development. These findings highlight the importance of ongoing treatment reevaluation and suggest that chemotherapy may offer beneficial outcomes in selected cases.

Predictive Analysis for Success and Complications in Dental Implant Therapy using Artificial Intelligence Models

Introduction: Dental implant therapy is a predictable treatment for tooth replacement, but its success is influenced by multiple patient, surgical, and systemic factors. Traditional statistical models, such as logistic regression, are limited in capturing nonlinear relationships among these variables. Artificial intelligence (AI) can integrate diverse predictors to enhance clinical risk assessment and improve treatment outcomes. Objective: To identify key predictors of dental implant success and develop AI-based models capable of accurately forecasting implant outcomes using clinical and surgical parameters. Methods: This retrospective cohort study analyzed data from 172 patients (219 implants) placed between 2020 and 2021. Patient demographics, systemic health (smoking, diabetes), surgical variables, and implant characteristics were evaluated. Univariate and multivariate logistic regression identified independent predictors. Machine-learning models Logistic Regression, Decision Tree, Support Vector Machine (SVM), and Random Forest were trained using stratified 10-fold cross-validation and SMOTE balancing. Performance was assessed via accuracy, precision, recall, F1-score, AUC, and Brier score; calibration and Decision-Curve Analysis (DCA) evaluated model reliability and clinical benefit. Results: Overall implant success was 91.3%, with smoking (AOR D 2.3; 95% CI 1.3–4.1; p D 0.001) and diabetes mellitus (AOR D 1.8; 95% CI 1.1–3.5; p D 0.03) emerging as independent predictors of failure. Flapless surgery demonstrated a protective effect (AOR D 0.7; 95% CI 0.5–0.9; p D 0.04). The Random Forest model achieved the highest predictive performance (Accuracy D 87.4%, AUC D 0.91) and showed good calibration (Brier D 0.07) with superior clinical net benefit on DCA. Conclusion: AI-based modeling offers a robust, data-driven approach for predicting dental implant success by integrating multifactorial clinical parameters. Incorporating such models into clinical workflows can enhance patient-specific risk assessment and improve long-term implant outcomes.

Evaluating risk factors affecting chronic wound healing: a comprehensive analysis using logistic regression and neural network models

Chronic wounds impose immense burdens and suffering on patients, with healing governed by a complex blend of physiological, social, and psychological components. This study aimed to comprehensively evaluate risk factors affecting chronic wound healing by integrating traditional statistical methods with machine learning techniques. We retrospectively collected demographic and clinical data from 232 chronic wound patients treated at our hospital. The Mann-Whitney U test was employed for univariate comparisons of continuous variables, while categorical variables were analyzed using the chi-square or Fisher's exact test, and ordinal variables were assessed with the Cochran-Armitage trend test. Statistically significant parameters were subsequently incorporated into binary logistic regression and multilayer perceptron neural network (MLP) models. Univariate analysis identified seven factors significantly associated with wound prognosis: number of concurrent wounds ( P = 0.020), wound size ( P = 0.043), pain score ( P = 0.005), intervention modalities ( P for trend = 0.005), NSAIDs use ( P = 0.010), hemoglobin ( P = 0.005), and albumin ( P = 0.001). In the multivariable logistic regression model (event = good prognosis), albumin (adjusted OR=1.101, 95% CI: 1.024 -1.183, P = 0.010) and intervention with two modalities (OR=4.775, 95% CI: 1.215 -18.762, P = 0.025) or three modalities (OR=6.360, 95% CI: 1.404 -28.797, P = 0.016) were independently associated with good prognosis. The MLP model's AUC was recorded at 0.771 (95% CI: 0.710 -0.832), with wound size (100%) and albumin (69.3%) showing the highest normalized importance. The DeLong test showed no significant difference between the logistic regression and MLP models ( P = 0.907). Albumin levels and multimodal interventions were independently associated with favorable chronic wound prognosis, while wound size and albumin emerged as the most influential predictors in the neural network model. These findings underscore the importance of nutritional status and comprehensive wound management, although causal inference is limited by the retrospective design.

Development and validation of a SHAP-interpretable GBM model for predicting postoperative recurrence of anal Fistula

This study aimed to develop and interpret a machine learning model for predicting postoperative recurrence of anal fistula using routine laboratory indicators and inflammation-related indices. A total of 2,214 patients who underwent fistulectomy were included. Patients from wards 5, 11, 12, 13 and 14 ( n  = 1,772) were divided by stratified random sampling according to recurrence status into training ( n  = 1,242) and testing ( n  = 530) cohorts. Patients from wards 15 and 16 ( n  = 442), which were managed by separate clinical teams, were reserved as a ward-based internal validation cohort. Univariate and multiple fistula tracts. analyses were performed to identify recurrence-associated factors, and LASSO regression was used for feature selection. Multiple machine learning models were developed and compared, including logistic regression, support vector machine, GBM, neural network, XGBoost, AdaBoost, LightGBM, and CatBoost. Model performance was assessed using ROC curves, calibration curves, decision curve analysis, and classification metrics. SHAP analysis was applied for model interpretation. Multivariate logistic regression analysis showed that WBC, RBC, hs-CRP, and NCR were independent predictors of recurrence. LASSO regression selected 11 variables for model development. Among the candidate models, GBM demonstrated the most balanced predictive performance and was therefore selected as the final model. The AUCs of GBM in the training, testing, and validation sets were 0.777, 0.784, and 0.712, respectively. Calibration curves showed acceptable agreement between predicted and observed risks, while decision curve analysis indicated potential clinical benefit within low-to-moderate threshold probability ranges. SHAP analysis identified age, WBC, RBC, NCR, and hs-CRP as the main contributors to model prediction. Restricted cubic spline analysis revealed a significant nonlinear association between NCR and recurrence risk. WBC, RBC, hs-CRP, and NCR were independently associated with postoperative recurrence of anal fistula. The LASSO-based GBM model demonstrated stable predictive performance and acceptable clinical utility. Routine hematological parameters and inflammation-related indices, particularly NCR, may support individualized recurrence risk stratification and postoperative follow-up.

Machine learning-based prediction of perioperative complications in spine surgery: a large-scale model development and validation study.

Early prediction of anastomotic leakage within 24 h after minimally invasive colorectal cancer surgery using postoperative inflammatory markers: development and temporal validation of a machine learning model.

PURPOSE Early identification of anastomotic leakage (AL) is critical for safe discharge within enhanced recovery pathways. This study developed and prospectively validated a machine learning (ML) model to predict AL using 24-h postoperative inflammatory biomarkers. METHODS We analyzed 1,961 patients undergoing elective minimally invasive colorectal resection (2012-2025). Five ML architectures were developed using a 70/15/15 split. The Regularized Logistic Regression (RLB) model was selected and locked with a pre-specified threshold (0.1258). Global variable importance and directionality were assessed via SHAP analysis. Prospective temporal validation was performed on 250 consecutive patients (February 2024 - December 2025). RESULTS AL incidence was 9.8% in the development cohort. The RLB model achieved high discrimination (AUCPR 0.859; AUC-ROC 0.819). Postoperative C-reactive protein (CRP) and the Systemic Inflammation Response Index (SIRI) at 24 h were the strongest predictors. During temporal validation, despite a 70% relative reduction in AL incidence (2.8%), the model maintained a robust negative predictive value (NPV) of 97.9% (95% CI 95.1-99.1% and an AUC of 0.73 (95% CI 0.54-0.92). Calibration was near-optimal (slope 0.987, intercept 0.505). Decision curve analysis demonstrated superior net clinical benefit across risk thresholds of 5-20%. CONCLUSIONS ML-based integration of early inflammatory biomarkers provides a reliable "safety filter" for postoperative surveillance. The high NPV supports objective decision-making for early discharge, even in changing clinical environments with decreasing complication rates.