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Predicting visual acuity with machine learning in treated ocular trauma patients.

Zhi-Lu Zhou1,2, Yi-Fei Yan3,4, Jie-Min Chen2

  • 1Department of Forensic Medicine, Guizhou Medical University, Guiyang 550009, Guizhou Province, China.

International Journal of Ophthalmology
|July 19, 2023
PubMed
Summary

Machine learning accurately predicts best-corrected visual acuity (BCVA) in ocular trauma patients. This technology aids in identifying visual dysfunction after treatment, improving patient outcomes.

Keywords:
best-corrected visual acuitymachine learningocular traumapredicting visiual acuityvisual dysfunction

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Area of Science:

  • Ophthalmology
  • Medical Artificial Intelligence
  • Computer Vision

Background:

  • Ocular trauma presents a significant challenge in predicting visual outcomes.
  • Accurate prediction of best-corrected visual acuity (BCVA) is crucial for managing patients with ocular trauma.
  • Current methods may not fully capture the complexity of visual recovery post-trauma.

Purpose of the Study:

  • To develop and evaluate machine learning (ML) models for predicting BCVA in patients with ocular trauma.
  • To assess the accuracy of ML algorithms in classifying visual function after treatment.
  • To identify key clinical and ocular features influencing visual outcomes in trauma patients.

Main Methods:

  • Utilized a dataset of 850 patients (1589 eyes) with ocular trauma, with a separate test set of 60 patients (100 eyes).
  • Employed four ML algorithms (Extreme Gradient Boosting, support vector regression, Bayesian ridge, random forest regressor) for BCVA prediction and four for classification.
  • Integrated clinical data with ocular parameters from optical coherence tomography and fundus photographs.

Main Results:

  • ML models demonstrated significant correlation between predicted and actual BCVA values (Pearson correlation coefficient > 0.6).
  • Achieved low Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) in both traumatic and combined groups.
  • Test dataset showed high accuracy (0.90) and precision (0.92) for BCVA classification, with sensitivity (0.83) and specificity (0.95).

Conclusions:

  • Machine learning models provide accurate predictions of BCVA in patients with treated ocular trauma.
  • These models are valuable tools for identifying visual dysfunction and guiding patient management.
  • The integration of diverse data sources enhances predictive capabilities for visual outcomes.