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Predicting Refractive Surgery Outcome: Machine Learning Approach With Big Data.

Asaf Achiron, Zvi Gur, Uri Aviv

    Journal of Refractive Surgery (Thorofare, N.J. : 1995)
    |September 8, 2017
    PubMed
    Summary
    This summary is machine-generated.

    A new decision forest model accurately predicts laser refractive surgery outcomes, aiding clinical decisions and individual risk assessment for better patient results.

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    Area of Science:

    • Ophthalmology
    • Medical Machine Learning
    • Surgical Outcomes Analysis

    Background:

    • Laser refractive surgery, including LASIK and photorefractive keratectomy, aims to correct vision errors.
    • Predicting surgical outcomes is crucial for managing patient expectations and optimizing results.
    • Machine learning offers potential for analyzing complex datasets in refractive surgery.

    Purpose of the Study:

    • To develop and validate a decision forest model for predicting the outcome of laser refractive surgery.
    • To assess the model's ability to generalize to new patients using cross-validation.

    Main Methods:

    • A decision forest model was trained using data from 17,592 LASIK or photorefractive surgery cases over 12 years.
    • The model utilized 38 clinical parameters and feature vectors extracted from patient data.
    • A 10-fold cross-validation was employed to evaluate the predictive accuracy on unseen data.

    Main Results:

    • The model achieved high efficacy rates: 92.0% achieved 0.7 or greater, and 84.9% achieved 0.8 or greater.
    • Preoperative best spectacle-corrected visual acuity (CDVA) was the most influential factor in the model.
    • Factors like age, central corneal thickness, mean keratometry, and preoperative CDVA negatively correlated with efficacy, while pupil size showed a positive correlation.

    Conclusions:

    • The developed decision forest model can support clinical decision-making in refractive surgery.
    • The model offers potential for improved individual risk assessment, leading to better surgical planning.
    • Further exploration of machine learning in analyzing refractive surgery big data is recommended.