Use of machine learning on contact lens sensor : derived parameters for the diagnosis of primary open-angle glaucoma

Martin, Keith R. (Department of Ophthalmology, Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom) ; Mansouri, Kaweh (Shiley Eye Institute, Hamilton Glaucoma Center and Department of Ophthalmology, University of California San Diego, La Jolla, California, USA) ; Weinreb, Robert N. (Shiley Eye Institute, Hamilton Glaucoma Center and Department of Ophthalmology, University of California San Diego, La Jolla, California, USA) ; Wasilewicz, Robert (Przemienienia Pańskiego Hospital, Department of Ophthalmology, Division of Ophthalmology, Poznan University of Medical Sciences Karol Marcinkowski, Poznań, Poland) ; Gisler, Christophe (School of Engineering and Architecture (HEIA-FR), HES-SO // University of Applied Sciences Western Switzerland) ; Hennebert, Jean (School of Engineering, HES-SO Valais-Wallis, HEI, HES-SO // University of Applied Sciences Western Switzerland) ; Genoud, Dominique (School of Engineering, HES-SO Valais-Wallis, HEI, HES-SO // University of Applied Sciences Western Switzerland)

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 physiological CLS parameters were derived from the signal recorded. CLS parameters frequently associated with the presence of POAG were identified using a random forest modeling approach. Main Outcome Measures: Area under the receiver operating characteristic curve (ROC AUC) for feature sets including CLS parameters and Start IOP, as well as a feature set with CLS parameters and Start IOP combined. Results : The CLS parameters feature set discriminated POAG from H eyes with mean ROC AUCs of 0.611, confidence interval (CI) 0.493–0.722. Larger values of a given CLS parameter were in general associated with a diagnosis of POAG. The Start IOP feature set discriminated between POAG and H eyes with a mean ROC AUC of 0.681, CI 0.603–0.765. The combined feature set was the best indicator of POAG with an ROC AUC of 0.759, CI 0.654–0.855. This ROC AUC was statistically higher than for CLS parameters or Start IOP feature sets alone (both P < .0001). Conclusions : CLS recordings contain information complementary to IOP that enable discrimination between H and POAG. The feature set combining CLS parameters and Start IOP provide a better indication of the presence of POAG than each of the feature sets separately. As such, the CLS may be a new biomarker for POAG.


Article Type:
scientifique
Faculty:
Ingénierie et Architecture
School:
HEIA-FR
HEI-VS
Institute:
iCoSys - Institut des systèmes complexes
Institut Systèmes industriels
Subject(s):
Ingénierie
Date:
2018-10
Pagination:
8 p.
Published in:
American Journal of Ophthalmology
Numeration (vol. no.):
2018, vol. 194, pp. 46-53
DOI:
ISSN:
0002-9394
Appears in Collection:

Note: The status of this file is: restricted


 Record created 2019-02-26, last modified 2019-03-05

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