Use of Machine Learning on Contact Lens Sensor-Derived Parameters for the Diagnosis of Primary Open-angle Glaucoma.

Journal: American journal of ophthalmology
PMID:

Abstract

PURPOSE: To test the hypothesis that contact lens sensor (CLS)-based 24-hour profiles of ocular volume changes contain information complementary to intraocular pressure (IOP) to discriminate between primary open-angle glaucoma (POAG) and healthy (H) eyes.

Authors

  • Keith R Martin
    Department of Ophthalmology, Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom. Electronic address: krgm2@cam.ac.uk.
  • Kaweh Mansouri
    Shiley Eye Institute, Hamilton Glaucoma Center and Department of Ophthalmology, University of California San Diego, La Jolla, California, USA.
  • Robert N Weinreb
    Hamilton Glaucoma Center, Viterbi Family Department of Ophthalmology and Shiley Eye Institute, University of California, San Diego, La Jolla, California.
  • Robert Wasilewicz
    Przemienienia Pańskiego Hospital, Department of Ophthalmology, Division of Ophthalmology, Poznan University of Medical Sciences Karol Marcinkowski, Poznań, Poland.
  • Christophe Gisler
    University of Applied Sciences Western Switzerland (HES-SO) Fribourg, Fribourg, Switzerland.
  • Jean Hennebert
    DIVA research group, Department of Informatics, University of Fribourg, Bd de Pérolles 90, 1700 Fribourg, Switzerland. Electronic address: jean.hennebert@unifr.ch.
  • Dominique Genoud
    University of Applied Sciences Western Switzerland (HES-SO) Valais, Institute of Information Systems, Sierre, Switzerland.