[Telemetric self-monitoring of intraocular pressure: from vision to reality? : Insights from the EyeMate-SC sensor].

Authors

Peter Szurman, H Burkhard Dick, Kaweh Mansouri, Esther M Hoffmann, Marc J Mackert, Colya N Englisch

Published in

Die Ophthalmologie. Jul 22, 2026. Epub Jul 22, 2026.

Abstract

The EyeMate-SC (G-Metrics GmbH, Hanover, Germany) is a permanently implantable microsensor positioned in the suprachoroidal space for telemetric, high-frequency self-measurement of intraocular pressure (IOP), with measurements transmitted to the treating ophthalmologist via an external reading device.
The aim of this review is to summarize the evidence regarding the safety of the implant, agreement of measurements with Goldmann applanation tonometry (GAT), patient adherence and acceptance, and the potential for capturing IOP fluctuations.
This review includes 24 patients with open-angle glaucoma who received an EyeMate-SC sensor as part of a nonpenetrating glaucoma surgery and were followed for 3 years within the ARGOS-SC01 study and its follow-up study, ARGOS-SC01_FU.
Implantation was successfully performed in all cases without complications. Over the observation period 15 moderate adverse events occurred beyond the early postoperative phase, of which one case was possibly related to the sensor. The device demonstrated long-term positional stability without dislocation and remained astigmatically neutral despite its superficial location. Agreement with GAT remained consistently good. Patients reported high acceptance and adherence and stated they would recommend the system to other glaucoma patients. In addition, the sensor enabled the assessment of both short-term and long-term IOP fluctuations.
The EyeMate-SC enables, for the first time, high-frequency telemetric self-monitoring of IOP independent of lens status and supports therapeutic decisions based on comprehensive pressure profiles rather than single measurements. Newer system generations further extend this concept by enabling automated, continuous data acquisition with higher temporal resolution. In combination with artificial intelligence, this could enable early detection of progression-associated patterns; however, the impact on disease progression, quality of life and social participation requires further investigation.

PMID:
42484855
Bibliographic data and abstract were imported from PubMed on 22 Jul 2026.

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