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Automated algorithms combining structure and function outperform general ophthalmologists in diagnosing glaucoma.

Leonardo Seidi Shigueoka1, José Paulo Cabral de Vasconcellos1, Rui Barroso Schimiti1

  • 1Glaucoma Service, Department of Ophthalmology, University of Campinas, Campinas, São Paulo, Brazil.

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|December 6, 2018
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Summary
This summary is machine-generated.

Machine learning classifiers (MLCs) and the combined structure-function index (CSFI) show promise for improving glaucoma diagnosis. These methods, using optical coherence tomography (OCT) and standard automated perimetry (SAP), offer comparable diagnostic accuracy to glaucoma specialists.

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Area of Science:

  • Ophthalmology
  • Medical technology
  • Artificial intelligence

Background:

  • Glaucoma diagnosis relies on integrating structural (OCT) and functional (SAP) data.
  • Current diagnostic methods are limited by the availability of glaucoma specialists.

Purpose of the Study:

  • To evaluate machine learning classifiers (MLCs) using OCT and SAP parameters for glaucoma detection.
  • To compare MLCs' diagnostic performance against the combined structure-function index (CSFI), general ophthalmologists, and glaucoma specialists.

Main Methods:

  • A cross-sectional prospective study involving 58 glaucoma patients and 66 healthy individuals.
  • Ten MLCs were trained and tested using OCT and SAP data.
  • Diagnostic accuracy (AUC) of MLCs was compared to CSFI, general ophthalmologists, and glaucoma specialists.

Main Results:

  • MLCs achieved AUCs ranging from 0.805 to 0.931.
  • The CSFI demonstrated the highest AUC (0.948) with 79.3% sensitivity at 90% specificity.
  • Glaucoma specialists had an AUC of 0.921, comparable to the best MLC (RBF Network) and CSFI.

Conclusions:

  • MLCs and CSFI can significantly aid glaucoma diagnosis in primary eye care.
  • These AI-driven and index-based approaches enhance diagnostic capabilities, especially where specialists are unavailable.