Prediction model of laparoendoscopic single-site surgery in gynecology using machine learning algorithm.

Abstract

Introduction Minimally invasive surgery has been widely used in gynecology. The laparoendoscopic single-site surgery (LESS) risk prediction model can provide evidence-based references for preoperative surgical procedure selection. Aim To determine whether the patients are suitable for LESS and to provide guidance for the clinical operation plan, we aimed to compare the clinical outcomes of LESS and conventional laparoscopic surgery (CLS) in gynecology. We constructed a LESS risk prediction model and predicted surgical conditions for the preoperative evaluation system. Material and methods A retrospective analysis was carried out among patients undergoing LESS (n = 1019) and CLS (n = 1055). Various clinical indicators were compared. Multiple machine model algorithms were evaluated. The optimal results were chosen as the model to form the risk prediction model. Results The LESS group showed advantages in the postoperative 12/24 h visual analog scale and Vancouver scar score compared with the CLS group (p < 0.05). The comparisons in other clinical indicators between the two groups showed that each group had advantages and the difference was statistically significant (p < 0.05), including operative time, estimated blood loss, and hospital stay. We evaluated the predictive value for various models using AUC values of 0.77, 0.77, 0.76, and 0.67 for XGBoost, random forest, GBDT, and logistic regression, respectively. The decision tree model was shown to be the optimal model. Conclusions LESS can reduce postoperative pain, shorten hospital stay and make scars acceptable. The risk prediction model based on a machine learning algorithm has manifested a high degree of accuracy and can satisfy the doctors’ demand for individualized preoperative evaluation and surgical safety in LESS.

Construction of a novel lower-extremity peripheral artery disease subtype prediction model using unsupervised machine learning and neutrophil-related biomarkers

“…With further validation, the implementation of such AI models can assist clinicians in applying personalized therapies and surveillance strategies. Jun Ma et al used UMLAs to develop a prediction model with high accuracy that could meet doctors’ needs for individualized preoperative and surgical safety evaluations in laparoendoscopic single-site surgery [ 44 ].…”