Journal article
Use of Machine Learning on Contact Lens Sensor–Derived Parameters for the Diagnosis of Primary Open-angle Glaucoma
KR Martin, K Mansouri, RN Weinreb, R Wasilewicz, C Gisler, J Hennebert, D Genoud, T Shaarawy, C Erb, N Pfeiffer, GE Trope, FA Medeiros, Y Barkana, JHK Liu, R Ritch, A Mermoud, D Jinapriya, C Birt, II Ahmed, C Kranemann Show all
American Journal of Ophthalmology | ELSEVIER SCIENCE INC | Published : 2018
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. Design: Development and evaluation of a diagnostic test with machine learning. Methods: SUBJECTS: From 435 subjects (193 healthy and 242 POAG), 136 POAG and 136 age-matched healthy subjects were selected. Subjects with contraindications for CLS wear were excluded. PROCEDURE: This is a pooled analysis of data from 24 prospective clinical studies and a registry. All subjects underwent 24-hour CLS recording on 1 eye. Statistical and physiologi..
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Awarded by Comprehensive Transplant Institute, University of Alabama at Birmingham
Funding Acknowledgements
SENSIMED AG, LAUSANNE, SWITZERLAND, PROVIDED FINANCIAL SUPPORT FOR THIS RESEARCH AND participated in the design of the research, data management, data analysis, interpretation of the data, and preparation, review and approval of the manuscript. The University of Applied Sciences Western Switzerland (HES-SO) participated in the data analysis and data interpretation, as well as preparation, review, and approval of the manuscript in the framework of this research project, partially funded by the Swiss Commission for Technology and Innovation (CTI) under grant 17325.1 PFLS-LS.