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Artificial intelligence-based nomogram for small-incision lenticule extraction.

Seungbin Park1, Hannah Kim1,2, Laehyun Kim1

  • 1Center for Bionics, Korea Institute of Science and Technology, Seoul, Korea.

Biomedical Engineering Online
|April 24, 2021
PubMed
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This summary is machine-generated.

Machine learning, specifically AdaBoost, accurately predicts nomograms for small-incision lenticule extraction (SMILE) surgery. This AI-driven approach enhances surgical outcomes by preventing misdiagnosis and improving refractive results.

Area of Science:

  • Ophthalmology and Artificial Intelligence
  • Refractive Surgery and Machine Learning

Background:

  • Small-incision lenticule extraction (SMILE) is a safe and effective refractive surgery for myopia and astigmatism.
  • Over- and under-correction can occur post-SMILE, highlighting the need for accurate nomograms.
  • Current nomogram development relies on surgeon experience and preoperative data analysis.

Purpose of the Study:

  • To accurately predict nomograms for sphere, cylinder, and astigmatism axis in SMILE surgery using machine learning.
  • To evaluate the performance of various machine learning algorithms in nomogram prediction.

Main Methods:

  • Retrospective analysis of 3,034 eyes with 4 categorical and 28 numerical features.
  • Development of nomogram models using multiple linear regression, decision tree, AdaBoost, XGBoost, and multi-layer perceptron.
Keywords:
Artificial intelligenceMachine learningNomogramSMILE

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  • Evaluation of models based on root-mean-square error (RMSE) and accuracy; feature importance analysis.

    • Main Results:

    • AdaBoost demonstrated the highest performance with the lowest RMSE for sphere (0.1378), cylinder (0.1166), and astigmatism axis (5.17).
    • High accuracies were achieved: 0.969 for sphere, 0.976 for cylinder, and 0.994 for astigmatism axis (within specified error margins).
    • Preoperative manifest refraction was the most important feature; surgeon identity was also highly important for sphere and cylinder nomograms.

    Conclusions:

    • AdaBoost is the optimal machine learning algorithm for predicting SMILE nomograms.
    • The study confirms the feasibility of applying artificial intelligence (AI) to SMILE nomograms.
    • AI-assisted nomograms can enhance SMILE surgical quality by preventing misdiagnosis and improving outcomes.