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Automated iridocorneal angle classification using a multimodal large language model.

Giovanni Rubegni1, Dario Romano2, Benedetta Colizzi2

  • 1Ophthalmology Unit, Department of Medicine, Surgery and Neurosciences, University of Siena, Siena, Italy. giovannirubegni@gmail.com.

Graefe'S Archive for Clinical and Experimental Ophthalmology = Albrecht Von Graefes Archiv Fur Klinische Und Experimentelle Ophthalmologie
|June 27, 2026
PubMed
Summary

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Functional assessment of the XEN<sup>®</sup> Gel Stent relative to the implant position using non-invasive stress testing and automated gonioscopy.

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This summary is machine-generated.

A general-purpose AI model, GPT-4o, showed high accuracy in classifying anterior chamber angle configurations from gonioscopic images but had moderate sensitivity for detecting closed angles. While useful as a supportive tool, it was outperformed by human experts.

Area of Science:

  • Ophthalmology
  • Artificial Intelligence
  • Medical Imaging

Background:

  • Gonioscopy is crucial for diagnosing glaucoma by assessing anterior chamber angle (ACA) configuration.
  • Accurate ACA grading is essential for timely treatment and management of glaucoma.

Purpose of the Study:

  • To evaluate the diagnostic performance of GPT-4o, a vision-language model, in interpreting gonioscopic images.
  • To assess GPT-4o's ability in classifying ACA configuration and Shaffer grading.

Main Methods:

  • 80 gonioscopic images were analyzed by glaucoma specialists, ophthalmologists, and GPT-4o.
  • GPT-4o received standardized instructions and one example per Shaffer grade.
  • Performance was measured using accuracy, sensitivity, specificity, and Kappa statistics.
Keywords:
Artificial intelligenceGlaucomaGonioscopyLarge language model

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Main Results:

  • GPT-4o achieved high accuracy (90-91.3%) in angle configuration classification but moderate sensitivity (56.3%) for closed angles.
  • Shaffer grading showed moderate agreement (QWK 0.471-0.573) with experts, with predominant misclassification between adjacent grades.
  • Comprehensive ophthalmologists demonstrated superior performance compared to GPT-4o.

Conclusions:

  • GPT-4o can generate clinically relevant assessments of gonioscopic images without specific training.
  • While not matching expert performance, GPT-4o shows potential as a supportive tool in resource-limited settings.
  • Further validation is required to enhance its reliability for grading tasks.