AI GLUCOSE SCAN
POWERED BY BGEM® TECHNOLOGY
The World’s First Non-Invasive Glucose Monitoring Technology
There’s Nothing Else Like It
AI Glucose Scan estimates your glucose status using data from Actxa wearables. Instead of requiring blood samples, it analyses physiological signals, transforming them into biophotonic and hemodynamic biomarkers before using AI to determine whether your glucose is Normal or Elevated.
Results in 60 Seconds
The scan completes in about one minute and provides a clear ‘Normal’ or ‘Elevated’ result. No need for calibration, test strips, or manual input.
Many People Living With Diabetes Don’t Know They Have It
Most people assume they’re healthy. But metabolic disorders often develops quietly, without obvious symptoms
of adults with diabetes
worldwide are undiagnosed
43%
million adults are undiagnosed (2024)
242
million adults age 20-79 are
living with diabetes (2024)
589
million adults expected
to have diabetes by 2030
853
- -DF Diabetes Atlas 11th Edition ¹
"The vast majority of prediabetes and type 2 diabetes can be prevented through diet and lifestyle changes, and this has been proven by 20 years of medical research."
- Harvard Medical School ²
This breakthrough BGEM® Technology turns complex biometric signals into clear, insights, so you can act earlier and with more confidence
Powered by BGEM® Technology
AI Glucose Scan is powered by Actxa’s Blood Glucose Evaluation & Monitoring (BGEM®) Technology. It uses signals from photoplethysmography (PPG) sensors, found in smart rings and watches. These sensors already track things like heart rate and blood flow.
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Jointly Developed by Medical Doctors, Clinical Researchers,
Biostatisticians, Data Scientists and Engineers Since 2019
Clinical Trials
2
Geographic regions
2
Subjects
1,300
Data samples
36,000
Artificial Intelligent
& Signal Processing
Using proprietary signal processing, extracted 800 over biophotomic and hemodynamics biomarkers, and finetuned
to account for various cohort biases.
Accuracy of AI Glucose Scan
Clinical results shows AI Glucose scan has achieved an accuracy of ~80% over a cohort of balanced healthy and diabetic adult subjects.
A First of Its Kind. Made for Every Day
Designed to make glucose awareness something anyone can do, anywhere, anywhere.
Non Invasive - No needles, no pain
Easy and convenient - Fits into everyday routines
Frequent monitoring - Supports ongoing awareness
Cost-effective & Scalable - no subscriptions, no consumables
Spoil Yourself; Not Your Health
You shouldn’t have to choose between enjoying life and taking care of your health. AI Glucose Scan helps you understand how your body responds to everyday habits so you can make better choices without giving up the things you love.
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References
1. Diabetes Atlas. (2025, October 10). IDF Diabetes Atlas 2025 | Global Diabetes Data & Insights. https://diabetesatlas.org/resources/idf-diabetes-atlas-2025/
2. Tello, M. (2018, September 5). Healthy lifestyle can prevent diabetes (and even reverse it). Harvard Health Blog. https://www.health.harvard.edu/blog/healthy-lifestyle-can-prevent-diabetes-and-even-reverse-it-2018…
3. Shi, B., Dhaliwal, S. S., Soo, M., Chan, C., Wong, J., Lam, N. W. C., Zhou, E., Paitimusa, V., Loke, K. Y., Chin, J., Chua, M. T., Liaw, K. C. S., Lim, A. W. H., Insyirah, F. F., Yen, S., Tay, A., & Ang, S. B. (n.d.). Assessing Elevated Blood Glucose Levels Through Blood Glucose Evaluation and Monitoring Using Machine Learning and Wearable Photoplethysmography Sensors: Algorithm Development and Validation. JMIR AI, 2, e48340. https://doi.org/10.2196/48340
4. Ang, S. B., Chua, M. T., Shi, B., Chan, S. H. C., Liaw, C. S. K., & Dhaliwal, S. S. (n.d.). Utility of photoplethysmography in detecting elevated blood glucose among non-diabetics. Singapore Medical Journal. https://doi.org/10.4103/singaporemedj.smj-2023-156
5. Suradji, E. W., Dhaliwal, S. S., Li-Feng, Z., Zhou, E., Lim, A., Wong, H. H., Du, Y., Fredicia, F., Dianky, A., Pasaribu, P., Takashi, E. G. M., Tan, J. Y. A., Santoso, M., & Ang, S. B. (2025). Assessment of blood glucose measurement using new noninvasive Technology: Protocol and Methodology. JMIR Research Protocols, 15, e76558. https://doi.org/10.2196/76558