Diabetes Detection and Sensor-Based Continuous Glucose Monitoring – A Deep Learning Approach
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Continuous glucose monitors such as Dexcom G6 and Abbott FreeStyle Libre continuously measure interstitial glucose levels and use AI algorithms to predict blood sugar trends, allowing users to take preventative action to avoid hypo- or hyperglycemia.
“For example, CGMs such as the Dexcom G6 and Abbott FreeStyle Libre continuously measured interstitial glucose levels and use AI algorithms to predict blood sugar trends, allowing users to take preventative action to avoid hypo- or hyperglycemia (39,40).”
From Generative artificial intelligence in predictive analysis of diabetes and its complications: a narrative review (2025) · cites this paper as [39]
Cites
Cappon, G., Vettoretti, M., Sparacino, G., & Facchinetti, A. (2019) · Diabetes & Metabolism Journal · doi:10.4093/dmj.2019.0121 · Cited by 477 · see citing works
Continuous glucose monitoring: Current use in diabetes management and possible future applications
Vettoretti, M., Cappon, G., Acciaroli, G., Facchinetti, A., & Sparacino, G. (2018) · Journal of Diabetes Science and Technology · doi:10.1177/1932296818774078 · Cited by 96 · see citing works
Real-World Data from the First U.S. Commercial Users of an Implantable Continuous Glucose Sensor
Sanchez, P., Ghosh-Dastidar, S., Tweden, K. S., & Kaufman, F. R. (2019) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2019.0234 · Cited by 40 · see citing works
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Osonuga, A., Olawade, D. B., Gore, M., Rotifa, O. B., Egbon, E., Chinwah, V., & Boussios, S. (2025) · Annals of Translational Medicine · doi:10.21037/atm-25-62 · Cited by 4 · see citing works
- Authors
- Swapna, G., & Soman, K. P.
- Affiliations
- Amrita Vishwa Vidyapeetham (IN)
- Venue
- Internet of Things
- Publisher
- Springer International Publishing
- Published
- 2021
- Pages
- 245-268
- ISSN
- 2199-1073 (ISSN-L)
- 2199-1081
- OpenAlex
- W3196851310
- Cited by
- 9
- References
- 3
- Citations by year
- 2026: 1 · 2025: 2 · 2024: 2 · 2023: 2 · 2022: 2
- FWCI
- 8.95
- Type
- book chapter
- Language
- en
- Open access
- closed
- Locations
- Internet of things (publishedVersion)
- Topics
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › ECG Monitoring and Analysis
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
- Keywords
- diabetes detection, continuous glucose monitoring, heart rate variability, diabetic neuropathy, ECG morphological features, hybrid deep learning model, computer-aided diagnosis, blood glucose monitoring, glucose sensor, hyperglycemia, cardiac autonomic dysfunction, non-invasive diagnosis, diabetes complications, cascaded neural networks
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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