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Artificial intelligence virtual assistants in primary eye care practice.

Leandro Stuermer1,2, Sabrina Braga1,2, Raul Martin2,3

  • 1Department of Optometry, University of Contestado, Canoinhas, Brazil.

Ophthalmic & Physiological Optics : the Journal of the British College of Ophthalmic Opticians (Optometrists)
|December 26, 2024
PubMed
Summary

A new artificial intelligence (AI) virtual assistant accurately predicts eye examination classifications, ocular disorders, and binocular vision dysfunctions. This AI tool aids decision-making in primary eye care and optometry education.

Keywords:
artificial intelligenceclinical decision supportmachine learningoptometryvirtual assistant

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Area of Science:

  • Ophthalmic diagnostics
  • Artificial intelligence in healthcare
  • Clinical decision support systems

Background:

  • Primary eye care relies on accurate classification of refractive errors, binocular vision dysfunctions, and ocular disorders.
  • Optometry education requires effective tools for training and skill development in diagnostic interpretation.
  • Existing diagnostic methods can be time-consuming and may benefit from AI-driven support.

Purpose of the Study:

  • To develop and evaluate a novel artificial intelligence (AI)-based virtual assistant.
  • To provide decision-making support for primary eye care practitioners and optometry students.
  • To train AI models on tabular clinical data for predicting eye examination outcomes.

Main Methods:

  • Utilized anonymized clinical data from 1125 optometric examinations (2250 eyes).
  • Trained machine learning algorithms to classify refractive status, binocular vision, and ocular disorders.
  • Employed data preprocessing techniques including one-hot encoding and SMOTE, with a train/test split of 80%/20%.

Main Results:

  • The random forest algorithm achieved high performance: >95.2% for eye examination classification, >98.1% for ocular disorder subclassification, and >99.7% for binocular vision dysfunction differentiation.
  • AI models were integrated into a multilingual, responsive web application for intuitive clinical use.
  • Performance was rigorously evaluated using accuracy, precision, sensitivity, F1 score, and specificity.

Conclusions:

  • Developed an effective AI virtual assistant for predicting patient classification, eye disorders, and binocular vision dysfunction.
  • The AI assistant demonstrates significant potential for enhancing primary eye care practice.
  • The tool is also poised to be a valuable asset in optometry education programmes.