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Comparative Study
. 2020 Oct;104(10):1394-1398.
doi: 10.1136/bjophthalmol-2019-315446. Epub 2019 Dec 23.

Development and validation of a machine learning, smartphone-based tonometer

Yue Wu  1 , Ian Luttrell  1 , Shu Feng  2 , Philip P Chen  2 , Ted Spaide  2 , Aaron Y Lee #  2 , Joanne C Wen #  3
Affiliations
Comparative Study

Development and validation of a machine learning, smartphone-based tonometer

Yue Wu et al. Br J Ophthalmol. 2020 Oct.

Abstract

Background/aims: To compare intraocular pressure (IOP) measurements using a prototype smartphone tonometer with other tonometers used in clinical practice.

Methods: Patients from an academic glaucoma practice were recruited. The smartphone tonometer uses fixed force applanation and in conjunction with a machine-learning computer algorithm is able to calculate the IOP. IOP was also measured using Goldmann applanation tonometry (GAT) in all subjects. A subset of patients were also measured using ICare, pneumotonometry (upright and supine positions) and Tono-Pen (upright and supine positions) and the results were compared.

Results: 92 eyes of 81 subjects were successfully measured. The mean difference (in mm Hg) for IOP measurements of the smartphone tonometer versus other devices was +0.24 mm Hg for GAT, -1.39 mm Hg for ICare, -3.71 mm Hg for pneumotonometry and -1.30 mm Hg for Tono-Pen. The 95% limits of agreement for the smartphone tonometer versus other devices was -4.35 to 4.83 mm Hg for GAT, -6.48 to 3.70 mm Hg for ICare, -7.66 to -0.15 mm Hg for pneumotonometry and -5.72 to 3.12 mm Hg for Tono-Pen. Overall, the smartphone tonometer results correlated best with GAT (R2=0.67, p<0.001). Of the 92 videos, 90 (97.8%) were within ±5 mm Hg of GAT and 58 (63.0%) were within ±2 mm Hg of GAT.

Conclusions: Preliminary IOP measurements using a prototype smartphone-based tonometer was grossly equivalent to the reference standard.

Keywords: intraocular pressure.

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Conflict of interest statement

Competing interests: AYL has received grant support from Novartis and Carl Zeiss Meditec. AYL has received honoraria from Topcon and Verana Health. JCW has a patent pending US20170215728A1. None of the other authors have any competing interests related to this study.

Figures

Figure 1.
Figure 1.
Prototype smartphone tonometer and machine learning image processing. (A) Prototype schematic showing the clear applanator aligned with the smartphone camera (B) The prototype smartphone tonometer used in this study. Metal siding in conjunction with a blue filter redirects the smartphone flash to illuminate the applanator tip with blue light. (C) Example frame from a video showing a typical appearance for the applanation tip and applanation mire. (D) Machine-Learning segmentation of the applanation tip circle and the applanation mire circle.
Figure 2.
Figure 2.
Comparison of smartphone tonometer with Goldmann applanation tonometer. (A) Linear correlation between smartphone tonometer intraocular pressure (IOP) and Goldmann applanation tonometer (GAT) (r=0.82, R2 = 0.67, p-value < 0.001) (B) Bland-Altman Plot comparing IOP measurements
Figure 3.
Figure 3.
Smartphone tonometer intraocular pressure (IOP) measurements compared to other tonometers. The smartphone tonometer IOP is shown as a boxplot with the corresponding Goldmann (blue star), iCare (orange circle), Pneumatonometer (green cross) and Tonopen (red triangle) measurements.
Figure 4.
Figure 4.
Example scatterplot of fully processed video demonstrating ocular pulsations. Shaded gray areas denote when the tonometer tip was lifted off the ocular surface.
See this image and copyright information in PMC

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