Predicting 60–4 visual field tests using 3D facial reconstruction

• Published in: British journal of ophthalmology Vol. 108; no. 1; pp. 112 - 116
• Main Authors: Jamali Dogahe, Sepideh, Garmany, Armin, Sadegh Mousavi, Seyedmostafa, Khanna, Cheryl L
• Format: Journal Article
• Language: English
• Published: BMA House, Tavistock Square, London, WC1H 9JR BMJ Publishing Group Ltd 01.01.2024; BMJ Publishing Group LTD
• Subjects: 3-D graphics; Artificial Intelligence; Defects; diagnostic tests/investigation; Eyes & eyesight; Face; field of vision; Field study; Glaucoma; Humans; Intraocular Pressure; Medical diagnosis; Neural networks; Ophthalmology; Pilot Projects; Predictive Value of Tests; Retina; Sensitivity and Specificity; Three dimensional imaging; Tomography, Optical Coherence; Toxicity; Vision Disorders - diagnosis; Visual Field Tests - methods; Visual Fields; United States > US; diagnostic tests/investigation; glaucoma; field of vision
• ISSN: 0007-1161; 1468-2079; 1468-2079
• DOI: 10.1136/bjo-2022-321651

BackgroundDespite, 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.MethodsSubjects 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.Results30 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.ConclusionsThis 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.