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Predicting 60–4 visual field tests using 3D facial reconstruction
  1. Sepideh Jamali Dogahe1,
  2. Armin Garmany2,
  3. Seyedmostafa Sadegh Mousavi1,
  4. Cheryl L Khanna1
  1. 1Department of Ophthalmology, Mayo Clinic, Rochester, Minnesota, USA
  2. 2Graduate School of Biomedical Sciences, Alix School of Medicine, Medical Scientist Training Program, Mayo Clinic, Rochester, Minnesota, USA
  1. Correspondence to Dr Cheryl L Khanna, Department of Ophthalmology, Mayo Clinic, Rochester, USA; Khanna.cheryl{at}mayo.edu

Abstract

Background Despite, the potential clinical utility of 60–4 visual fields, they are not frequently used in clinical practice partly, due to the purported impact of facial contour on field defects. The purpose of this study was to design and test an artificial intelligence-driven platform to predict facial structure-dependent visual field defects on 60–4 visual field tests.

Methods Subjects with no ocular pathology were included. Participants were subject to optical coherence tomography, 60–4 Swedish interactive thresholding algorithm visual field tests and photography. The predicted visual field was compared with observed 60–4 visual field results in subjects. Average and point-specific sensitivity, specificity, precision, negative predictive value, accuracy, and F1-scores were primary outcome measures.

Results 30 healthy were enrolled. Three-dimensional facial reconstruction using a convolution neural network (CNN) was able to predict facial contour-dependent 60–4 visual field defects in 30 subjects without ocular pathology. Overall model accuracy was 97%±3% and 96%±3% and the F1-score, dependent on precision and sensitivity, was 58%±19% and 55%±15% for the right eye and left eye, respectively. Spatial-dependent model performance was observed with increased sensitivity and precision within the far inferior nasal field reflected by an average F1-score of 76%±20% and 70%±29% for the right eye and left eye, respectively.

Conclusions This pilot study reports the development of a CNN-enhanced platform capable of predicting 60–4 visual field defects in healthy controls based on facial contour. Further study with this platform may enhance understanding of the influence of facial contour on 60–4 visual field testing.

  • field of vision
  • glaucoma
  • diagnostic tests/investigation

Data availability statement

Data are available on reasonable request.

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Data availability statement

Data are available on reasonable request.

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Footnotes

  • Twitter @ArminGarmany

  • Contributors Study conception and design: CLK; data collection: SJD, SSM; analysis and interpretation of results: AG, SJD, SSM, CLK; draft manuscript preparation: AG; Manuscript editing and review: CLK, SJD, SSM, AG. All authors reviewed the results and approved the final version of the manuscript. Guarantor: CLK.

  • Funding AG was supported by the National Institute of General Medical Sciences (T32 GM 65841).

  • Competing interests None declared.

  • Provenance and peer review Not commissioned; externally peer reviewed.