A machine learning-based predictive model for stem cell therapy outcomes in plastic surgery

Area of Science:

• Plastic Surgery
• Regenerative Medicine
• Biotechnology

Background:

• Stem cell therapy shows promise in plastic surgery but has variable outcomes.
• Lack of reliable tools to predict treatment efficacy hinders personalized approaches.

Purpose of the Study:

• To develop and validate a predictive model for stem cell therapy efficacy in plastic surgery.
• To identify key factors influencing treatment outcomes using clinical data and machine learning.

Main Methods:

• Retrospective analysis of 620 patients undergoing stem cell therapy.
• Development of Random Forest (RF), Support Vector Machine (SVM), and K-nearest neighbors (KNN) models.
• Validation using training (70%) and testing (30%) datasets.

Main Results:

• Age, disease duration, diabetes, and cell passage number were risk factors.
• Baseline skin score, stem cell dose, and injection frequency were protective factors.
• The RF model achieved the highest AUC (0.798 training, 0.787 validation).

Conclusions:

• A machine learning-based predictive model for stem cell therapy efficacy in plastic surgery was successfully constructed.
• The model demonstrates moderate predictive accuracy and can guide personalized clinical treatments.
• Further refinement may enhance its utility in clinical practice.