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Visualization of Breast Reconstruction Outcomes Using an Artificial Intelligence-Enabled Simulator.

Jonlin Chen1,2, Jenny Chen1, Ariel Gabay1

  • 1Plastic and Reconstructive Surgery Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY.

Plastic and Reconstructive Surgery
|February 27, 2026
PubMed
Summary
This summary is machine-generated.

An AI-based surgical simulator accurately predicts breast reconstruction outcomes using patient photographs. This technology aids shared decision-making and manages expectations for personalized breast reconstruction results.

Keywords:
artificial intelligencebreast reconstructionmachine learningpatient educationshared decision-makingsurgical simulation

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Area of Science:

  • Medical AI
  • Surgical Simulation
  • Plastic Surgery

Background:

  • Personalized visual aids can improve shared decision-making and expectation management in breast reconstruction.
  • Current methods lack precise prediction of postoperative outcomes.

Purpose of the Study:

  • To develop and validate an Artificial Intelligence (AI)-based surgical simulator.
  • To predict breast reconstruction outcomes using real patient photographs.

Main Methods:

  • A generative adversarial network (GAN) was used to develop the AI simulator.
  • The model was trained on 1,405 patient photographs from Memorial Sloan Kettering Cancer Center.
  • AI predictions were evaluated against real outcomes using SSIM and FID metrics.

Main Results:

  • The AI simulator demonstrated accurate predictions for both implant and autologous breast reconstruction.
  • Quantitative metrics (SSIM, FID) supported the model's image fidelity.
  • AI predictions showed visual concordance with actual postoperative results in shape, volume, symmetry, and nipple reconstruction.

Conclusions:

  • AI models trained on clinical photographs can feasibly simulate breast reconstruction outcomes.
  • Further research into AI-based personalized surgical simulation is warranted.
  • Integration of AI simulators into clinical practice for breast reconstruction is supported.