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Using Deep Learning to Automate Goldmann Applanation Tonometry Readings.

Ted Spaide1, Yue Wu1, Ryan T Yanagihara1

  • 1Department of Ophthalmology, University of Washington, Seattle, Washington.

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This study developed an automated method for measuring intraocular pressure using deep learning and Goldmann applanation tonometry (GAT). The automated GAT showed comparable results to standard GAT, with potential to reduce bias and improve repeatability.

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Area of Science:

  • Ophthalmology
  • Medical Technology
  • Artificial Intelligence

Background:

  • Intraocular pressure (IOP) measurement is crucial for diagnosing and managing glaucoma.
  • Current Goldmann applanation tonometry (GAT) methods rely on manual technique, introducing potential variability.
  • Objective and automated IOP measurement methods are needed to enhance diagnostic accuracy and consistency.

Purpose of the Study:

  • To develop an objective and automated method for measuring intraocular pressure (IOP).
  • To utilize deep learning algorithms combined with fixed-force Goldmann applanation tonometry (GAT).
  • To assess the performance of the automated GAT system against standard GAT measurements.

Main Methods:

  • A prospective cross-sectional study involving patients from an academic glaucoma practice.
  • Video analysis of standard slit-lamp microscopy and fixed-force GAT to capture tonometer and mire outlines.
  • Training a deep learning model for localization and segmentation of tonometer and mires to calculate IOP via the Imbert-Fick formula.

Main Results:

  • The automated GAT demonstrated a mean difference of -0.9 mmHg compared to standard GAT.
  • Interobserver variability was comparable between automated GAT (-0.3 mmHg) and standard GAT (0.09 mmHg).
  • Automated GAT showed improved bias reduction and comparable coefficients of repeatability (3.8 mmHg) versus standard GAT (3.9 mmHg).

Conclusions:

  • Deep learning-based automated GAT provides preliminary results comparable to standard GAT.
  • Automated GAT holds significant potential to enhance current IOP measurement standards by reducing bias and improving repeatability.
  • The technique may also allow for the observation of ocular pulse amplitudes.