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

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.

  • intraocular pressure

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