June 2022
Volume 63, Issue 7
Open Access
ARVO Annual Meeting Abstract  |   June 2022
Assessing the external validity of machine learning-based detection of glaucoma
Author Affiliations & Notes
  • Jacqueline Chua
    Singapore Eye Research Institute, Singapore, Singapore
  • Chi Li
    Singapore Eye Research Institute, Singapore, Singapore
  • Alina Popa-Cherecheanu
    Universitatea de Medicina si Farmacie Carol Davila, Bucuresti, Bucuresti, Romania
  • Damon Wong
    Nanyang Technological University, Singapore, Singapore, Singapore
  • Leopold Schmetterer
    Singapore Eye Research Institute, Singapore, Singapore
  • Footnotes
    Commercial Relationships   Jacqueline Chua None; Chi Li None; Alina Popa-Cherecheanu None; Damon Wong None; Leopold Schmetterer None
  • Footnotes
    Support  None
Investigative Ophthalmology & Visual Science June 2022, Vol.63, 184 – F0031. doi:
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    • Get Citation

      Jacqueline Chua, Chi Li, Alina Popa-Cherecheanu, Damon Wong, Leopold Schmetterer; Assessing the external validity of machine learning-based detection of glaucoma. Invest. Ophthalmol. Vis. Sci. 2022;63(7):184 – F0031.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

Purpose : To validate a machine learning model from spectral-domain optical coherence tomography (Cirrus, Carl Zeiss Meditec, Dublin, CA) data along with patients’ background information for glaucoma detection and assess its external validity.

Methods : In this cross-sectional study, 514 Asians (257 glaucoma and 257 controls) were enrolled to construct a machine learning model for glaucoma detection, which was then tested on an independent dataset of 356 Asians (183 glaucoma and 173 controls) and an external dataset of 138 Caucasians (57 glaucoma and 81 controls). The machine learning model was also compared to the existing multivariate-adjusted retinal nerve fiber layer (RNFL) thickness compensation model and Cirrus-generated (measured) RNFL model. The area under the receiver operating characteristics curve (AUC) for glaucoma detection was calculated.

Results : The machine learning-based model (AUC=0.97) outperformed both the compensation model (AUC=0.93; P<0.001) and the measured RNFL model (AUC=0.93; P<0.001) for glaucoma detection in the Asian dataset. However, in the Caucasian dataset, the compensation model (AUC=0.90 to 0.93) outperformed both machine learning-based (AUC=0.85; P<0.001) and measured RNFL (AUC=0.82; P<0.001) models, and there was no significant difference in the AUC between the machine learning-based model and measured RNFL model (P=0.174) for glaucoma detection.

Conclusions : While the machine learning model detected glaucoma at a higher accuracy as compared to the compensation model or measured RNFL model in the internal dataset, this finding was not fully replicated in the external validation with the Caucasian dataset. Care must be taken when machine learning models are applied to patient cohorts of different ethnicities.

This abstract was presented at the 2022 ARVO Annual Meeting, held in Denver, CO, May 1-4, 2022, and virtually.

 

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