Use of Machine Learning on Contact Lens Sensor-Derived Parameters for the Diagnosis of Primary Open-angle Glaucoma.
Weinreb, Robert N
Am J Ophthalmol
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Martin, K., Mansouri, K., Weinreb, R. N., Wasilewicz, R., Gisler, C., Hennebert, J., Genoud, D., & et al. (2018). Use of Machine Learning on Contact Lens Sensor-Derived Parameters for the Diagnosis of Primary Open-angle Glaucoma.. Am J Ophthalmol, 194 46-53. https://doi.org/10.1016/j.ajo.2018.07.005
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.
Research Consortium, Humans, Glaucoma, Open-Angle, Tonometry, Ocular, Monitoring, Ambulatory, Telemetry, Area Under Curve, Prospective Studies, ROC Curve, Contact Lenses, Intraocular Pressure, Adult, Aged, Middle Aged, Female, Male, Machine Learning
External DOI: https://doi.org/10.1016/j.ajo.2018.07.005
This record's URL: https://www.repository.cam.ac.uk/handle/1810/285117