Continuous Glucose Monitoring using a Wearable Phosphorescence Lifetime Imager and Machine Learning
We present a misalignment-resilient continuous glucose monitoring system leveraging an insertable sensor and a wearable phosphorescence lifetime imager. In vitro testing revealed 88.8% accuracy in classifying glucose levels as normal, low, and high concentration ranges.
Authors
- Aydogan Özcan (ORCID: https://orcid.org/0000-0002-0717-683X)
- Zoltán Göröcs (ORCID: https://orcid.org/0000-0002-7200-7055)
- Brian Ko (ORCID: https://orcid.org/0000-0003-4460-3850)
- Jessica C. Ramella‐Roman (ORCID: https://orcid.org/0000-0002-5710-6004)
- Michael J. McShane (ORCID: https://orcid.org/0000-0001-6838-3982)
- Tianle Zheng (ORCID: https://orcid.org/0000-0002-4646-8253)
- Marcell Veszpremi
- Ajmal Ajmal
- Xilin Yang (ORCID: https://orcid.org/0000-0002-6954-9662)
- Maxime Pindrys
- Oliver Wang (ORCID: https://orcid.org/0009-0001-8019-5236)
- Andrés Rodrı́guez
- Artem Goncharov
- Ridhi Pradhan
- David Baum
Publication Details
- Published
- 2025-01-01
- DOI
- https://doi.org/10.1364/cleo_at.2025.aa111_2
- Primary Topic
- Spectroscopy Techniques in Biomedical and Chemical Research
- Type
- article
- Field-Weighted Citation Impact
- 0.00