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Related Concept Videos

Ordinal Level of Measurement00:55

Ordinal Level of Measurement

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Artificial intelligence-assisted grading for tear trough deformity.

Kevin Yu-Ting Chen1, Shin-Shi Tzeng2, Hung-Chang Chen3

  • 1Department of Plastic and Reconstructive Surgery, New Taipei Municipal Tucheng Hospital, New Taipei City, Taiwan; Department of Plastic and Reconstructive Surgery, Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Taoyuan, Taiwan.

Journal of Plastic, Reconstructive & Aesthetic Surgery : JPRAS
|August 16, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces an AI-powered smartphone app for grading tear trough deformity (TTD). The AI model assists surgeons, especially beginners, in accurate TTD classification and treatment decisions.

Keywords:
Artificial intelligenceDeep learningGrading systemSmartphoneTear trough deformity

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Area of Science:

  • Ophthalmology and Medical Imaging
  • Artificial Intelligence in Medicine

Background:

  • Tear trough deformity (TTD) classification systems can be complex for surgeons.
  • Artificial intelligence (AI) offers potential to improve accuracy and reduce errors in medical practice.

Purpose of the Study:

  • To develop a reliable digital image grading model for TTD using smartphone photography and AI deep learning.
  • To aid surgeons, particularly less experienced ones, in clinical evaluations and surgical decisions for TTD.

Main Methods:

  • Utilized Barton's grading system for TTD on 983 photos from 504 patients.
  • Employed a consistent smartphone for photography, processed with medical AI assistant (MAIA™) software.
  • Randomly divided photos into training and testing sets for model development.

Main Results:

  • The AI model achieved an AUROC of 0.85 for the training set and 0.83 for the testing set.
  • Sensitivity and specificity varied between training (56%, 87.3%) and testing (49.3%, 85%) groups.
  • F1 scores were 0.57 (training) and 0.49 (testing), with heatmaps generated for visualization.

Conclusions:

  • This is the first study to demonstrate AI-powered TTD categorization using smartphone images.
  • The AI approach can simplify TTD evaluation and reduce clinical errors for practitioners.
  • Smartphone-based AI grading offers a practical tool for improving TTD assessment and management.