Predicting treatment outcome in sclerotherapy of reticular veins and telangiectasia using machine learning: A comprehensive analysis and performance evaluation

Background Accurately predicting treatment responses in varicose vein sclerotherapy is crucial for improving patient quality of life and optimizing overall healthcare costs. Purpose Our study aims to accurately predict treatment responses in telangiectasia and reticular vein treatment in lower extremity sclerotherapy, by taking advantage of machine learning’s (ML) ability to navigate complex data sets and provide personalized predictions. Materials and Methods ML algorithms were used to predict outcomes in 99 patients with varicose veins. The data set, which included patient characteristics such as age, gender, dosage, and photographs, was analyzed using six ML methods. Response to treatment was divided into three groups as “poor,” “moderate,” and “good” as a result of clinical visual evaluation. Results Individuals with no prior treatment exhibited a notably higher rate of “Good” responses than those who had received prior treatment. (p < .001) The group receiving a 2% polidocanol dosage showed a higher rate of “Good” responses than the group receiving a 1% polidocanol dosage. (p = .008) XGBoost outperformed other ML algorithms, particularly excelling in predicting “Poor” responses. Discussion ML-based predictive models for assessing sclerotherapy outcomes in varicose veins, uncovering significant efficacy determinants such as dosage and prior treatment history. While pioneering ML in sclerotherapy prediction, our study acknowledges limitations and proposes future research directions, including additional variable incorporation and real-time predictive tool development.