Noninvasive blood glucose sensing by secondary speckle pattern artificial intelligence analyses.
Deep PalAmitesh KumarNave AvrahamYoram EisenbachYevgeny BeidermanSergey AgdarovYafim BeidermanZeev ZalevskyPublished in: Journal of biomedical optics (2023)
The proposed noncontact blood glucose sensing mechanism utilizing AI data processing and a magnetic field can potentially improve glucose detection accuracy, making it more convenient and less painful for patients. The system also allows for inexpensive blood glucose sensing mechanisms and fast blood glucose screening. The results suggest that noninvasive methods can improve blood glucose detection accuracy, which can have significant implications for diabetes management. Investigations involving representative sampling data, including subjects of different ages, gender, race, and health status, could allow for further improvement.
Keyphrases
- blood glucose
- artificial intelligence
- glycemic control
- big data
- type diabetes
- machine learning
- blood pressure
- deep learning
- electronic health record
- end stage renal disease
- cardiovascular disease
- newly diagnosed
- ejection fraction
- mental health
- label free
- prognostic factors
- real time pcr
- cross sectional
- skeletal muscle
- data analysis
- quantum dots
- patient reported