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

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

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.

Publication types

  • Comparative Study
  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't
  • Validation Study

MeSH terms

  • Adult
  • Aged
  • Female
  • Glaucoma, Angle-Closure / diagnosis*
  • Glaucoma, Angle-Closure / physiopathology
  • Glaucoma, Open-Angle / diagnosis*
  • Glaucoma, Open-Angle / physiopathology
  • Humans
  • Intraocular Pressure / physiology*
  • Low Tension Glaucoma / diagnosis*
  • Low Tension Glaucoma / physiopathology
  • Machine Learning*
  • Male
  • Middle Aged
  • Ocular Hypertension / diagnosis
  • Pilot Projects
  • Reproducibility of Results
  • Smartphone / instrumentation*
  • Tonometry, Ocular / instrumentation*