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Related Concept Videos

Glaucoma: Overview01:25

Glaucoma: Overview

497
Glaucoma is an eye condition characterized by increased intraocular pressure that damages the retina and optic nerve, leading to irreversible blindness if left untreated. The human eye has various components, including the cornea, iris, pupil, lens, and optic nerve. Aqueous humor is secreted by the epithelium of the ciliary body in the posterior chamber and flows through the trabecular meshwork and canal of Schlemm, maintaining normal intraocular pressure. The trabecular meshwork and the canal...
497

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Diagnostic Decision-Making Variability Between Novice and Expert Optometrists for Glaucoma: Comparative Analysis to

Faisal Ghaffar1, Nadine M Furtado2, Imad Ali3

  • 1Department of Systems Design Engineering, Faculty of Engineering, University of Waterloo, Waterloo, ON, Canada.

JMIR Medical Informatics
|January 29, 2025
PubMed
Summary

Expert optometrists demonstrate more accurate glaucoma diagnosis through intuitive pattern recognition compared to novices who rely on data. This study informs AI development to support varying expertise levels in optometry.

Keywords:
AIartificial intelligenceclinical datacomparative analysisconsistencydecision-makingdiagnostic accuracyexperts versus novicesexperts versus novices differenceseye diseaseglaucomaglaucoma diagnosishuman factorshuman-centered AI designmethodologyoptometristoptometryprogression analysisrisk assessmentvisionvision impairment

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Area of Science:

  • Ophthalmology
  • Medical Artificial Intelligence
  • Clinical Decision-Making

Background:

  • Optometrist diagnostic approaches vary significantly with experience levels.
  • Novice optometrists often depend on data and external guidance, while experts utilize intuition and disease knowledge.
  • Understanding these differences is crucial for developing effective Artificial Intelligence (AI) support systems.

Purpose of the Study:

  • To identify and analyze variations in glaucoma diagnostic decision-making between novice and expert optometrists.
  • To provide guidelines for developing AI systems that cater to optometrists of all experience levels.
  • To enhance diagnostic accuracy and minimize decision inconsistencies in optometric practice through AI.

Main Methods:

  • In-depth interviews with 14 optometrists (novices and experts) on glaucoma diagnosis approaches.
  • Mixed-methods analysis combining qualitative and quantitative data.
  • Statistical tests (Mann-Whitney U, chi-square) to identify significant intergroup differences.

Main Results:

  • Experts showed higher diagnostic concordance rates (20%) than novices (10%) with limited data, increasing to 42% for both with complete patient history.
  • Significant differences observed in decision-making between initial and subsequent patient visits (p < .05).
  • Experts employed risk assessment and progression analysis; novices used structured methods and external references. Significant variations in follow-up times noted (p < .001).

Conclusions:

  • Experience significantly influences accuracy, approach, and management in optometrists' glaucoma diagnosis.
  • Tailored AI systems are essential to support optometrists across different expertise levels.
  • Findings offer insights for designing AI to improve diagnostic accuracy and consistency, enhancing patient outcomes.