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Supervised Automated Kinetic Perimetry (SAKP) Using Simulated Visual Field Data - Presentation of a New Examination

Ulrich Schiefer1,2, Michael Wörner3, Ditta Zobor4

  • 1Zentrum für Optische Technologien (ZOT), Hochschule Aalen, Deutschland.

Klinische Monatsblatter Fur Augenheilkunde
|December 6, 2024
PubMed
Summary
This summary is machine-generated.

The developed Supervised Automated Kinetic Perimetry (SAKP) algorithm demonstrates high accuracy and robustness in simulated visual field testing. This advanced algorithm achieves reliable results within practical examination times, paving the way for clinical use.

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Area of Science:

  • Ophthalmology
  • Medical Imaging
  • Computational Science

Background:

  • Automated perimetry is crucial for diagnosing visual field defects.
  • Kinetic perimetry traditionally requires skilled operators and can be time-consuming.
  • Developing automated methods can improve efficiency and consistency in visual field testing.

Purpose of the Study:

  • To develop, optimize, train, and evaluate a Supervised Automated Kinetic Perimetry (SAKP) algorithm.
  • To assess the algorithm's accuracy and examination duration using simulated digital perimetric data.
  • To establish the algorithm's robustness against varying error rates and magnitudes.

Main Methods:

  • The SAKP algorithm was trained iteratively on simulated 3D visual field data representing various defect classes.
  • It utilizes isopter segment analysis and probing vectors to map visual field boundaries.
  • The algorithm was evaluated on its accuracy (A) and examination time (T) against simulated visual field results.

Main Results:

  • The SAKP algorithm achieved a median accuracy (A) of 0.93 and a median examination time (T) of 7.0 minutes for simulated visual fields.
  • Highest accuracy was observed for altitudinal and hemianopic defects (0.98), while examination time was shortest for blind spot displacement (3.9 minutes).
  • Varying error rates and magnitudes up to 30% had a minimal impact on accuracy and time.

Conclusions:

  • The SAKP algorithm demonstrates high accuracy and robustness for simulated visual field data.
  • Achieves reliable diagnostic performance within acceptable examination durations.
  • The algorithm is being prepared for real-world clinical application in patient examinations.