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Area of Science:

  • Plastic Surgery
  • Artificial Intelligence
  • Computer Vision

Background:

  • Deep learning (DL) has revolutionized various fields, including healthcare.
  • The study introduces a novel DL application for plastic surgery.
  • Focuses on predicting rhinoplasty status using a mobile-deployed neural network.

Purpose of the Study:

  • To develop and demonstrate a deep neural network for predicting rhinoplasty status.
  • To assess the accuracy of this DL model compared to human surgeons.
  • To explore future applications of DL in plastic surgery.

Main Methods:

  • A deep convolutional neural network, RhinoNet, was created.
  • Trained on 22,686 before/after rhinoplasty images from public sources.
  • Network performance was evaluated against plastic surgery experts on 2,269 test images.

Main Results:

  • RhinoNet achieved 85% accuracy in predicting rhinoplasty status.
  • Model sensitivity and specificity were comparable to expert consensus.
  • Deep learning and human experts demonstrated equivalent accuracy in this classification task.

Conclusions:

  • DL applications can identify superficial surgical procedures from images alone.
  • DL excels with unstructured visual data, suitable for mobile deployment.
  • DL is expected to significantly impact various plastic surgery domains.