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CC BY 4.0 · Indian Journal of Neurotrauma
DOI: 10.1055/s-0045-1806943
Original Article

Machine Learning–Based Calibration of Commercial Continuous Glucose Monitoring Sensor in Nonserum Solutions: An In Vitro Validation Study

Megha Gautam
1   Department of Neurosurgery, All India Institute of Medical Sciences, New Delhi, India
,
Aditya Choudhary
1   Department of Neurosurgery, All India Institute of Medical Sciences, New Delhi, India
,
Deepak Agrawal 
1   Department of Neurosurgery, All India Institute of Medical Sciences, New Delhi, India
› Institutsangaben Funding This study was funded by IITI DRISHTI CPS Foundation, IIT Indore.
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Abstract

Background

Continuous glucose monitoring (CGM) systems, such as the FreeStyle Libre Pro (Abbott Diabetes Care), offer noninvasive glucose measurement. However, their accuracy in cerebrospinal fluid (CSF) glucose monitoring remains unvalidated. This study evaluates the performance of the FreeStyle Libre sensor against a standard laboratory analyzer and proposes a regression-based calibration model to enhance measurement accuracy in neurotrauma ICU.

Materials and Methods

A FreeStyle Libre sensor was integrated into an experimental setup using an adapter. Sensor readings were recorded with glucose concentrations ranging from 50 to 275 mg/dL. A standard laboratory analyzer was used as the reference. A linear regression model was trained to correct sensor deviations, with interpolation (SciPy's interp1d) used for refined predictions. Real-time data acquisition was facilitated via Universal asynchronous receiver / transmitter (UART)-based serial communication, and adaptive learning enabled model retraining upon accumulating 10 sensor laboratory value pairs.

Results

Initial sensor readings exhibited significant deviations from laboratory values, particularly at lower glucose concentrations (mean absolute relative difference [MARD]: 30.45%). Postcalibration, the MARD was reduced to 8.92%, demonstrating improved accuracy. Interpolation further minimized deviations, correcting values such as 40 mg/dL (20% deviation) to 49.1 mg/dL (1.8% deviation) and 72 mg/dL (42.4% deviation) to 123.5 mg/dL (1.2% deviation). Adaptive learning progressively reduced the root mean square error (RMSE) from 23.7 to 9.8 mg/dL after 30 updates.

Conclusion

The calibration model makes the FreeStyle Libre sensor more accurate for CSF glucose measurements. This method might be promising for monitoring CSF glucose continuously of patients with external ventricular drainage, improving patient care in the neurotrauma ICU.

Keywords

biosensor - calibration model - glucose monitoring - external ventricular drainage - neurotrauma


Publikationsverlauf

Artikel online veröffentlicht:
09. April 2025

© 2025. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution License, permitting unrestricted use, distribution, and reproduction so long as the original work is properly cited. (https://creativecommons.org/licenses/by/4.0/)

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