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

Lingang Xu1, Ying Lian1, Zhen Song1

  • 1Department of Plastic and Cosmetic Surgery, Henan Provincial People's Hospital, People's Hospital of Zhengzhou University, Zhengzhou, China.

Frontiers in Medicine
|January 2, 2026
PubMed
Summary
This summary is machine-generated.

This study developed a machine learning model to predict stem cell therapy effectiveness in plastic surgery. The random forest model showed the best performance, aiding personalized treatment decisions.

Keywords:
machine learningplastic surgerypredictive modelstem cell therapytreatment outcome

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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.