Non-invasive blood glucose sensing by machine learning of optic fiber-based speckle pattern variation.

SIGNIFICANCE: The ability to perform frequent non-invasive monitoring of glucose in the bloodstream is very applicable for diabetic patients. AIM: We experimentally verified a non-invasive multimode fiber-based technique for sensing glucose concentration in the bloodstream by extracting and analyzing the collected speckle patterns. APPROACH: The proposed sensor consists of a laser source, digital camera, computer, multimode fiber, and alternating current (AC) generated magnetic field source. The experiments were performed using a covered (with cladding and jacket) and uncovered (without cladding and jacket) multimode fiber touching the skin under a magnetic field and without it. The subject's finger was placed on a fiber to detect the glucose concentration. The method tracks variations in the speckle patterns due to light interaction with the bloodstream affected by blood glucose. RESULTS: The uncovered fiber placed above the finger under the AC magnetic field (150 G) at 140 Hz was found to have a lock-in amplification role, improving the glucose detection precision. The application of the machine learning algorithms in preprocessed speckle pattern data increase glucose measurement accuracy. Classification of the speckle patterns for uncovered fiber under the AC magnetic field allowed for detection of the blood glucose with high accuracy for all tested subjects compared with other tested configurations. CONCLUSIONS: The proposed technique was theoretically analyzed and experimentally validated in this work. The results were verified by the traditional finger-prick method, which was also used for classification as a conventional reference marker of blood glucose levels. The main goal of the proposed technique was to develop a non-invasive, low-cost blood glucose sensor for easy use by humans.

Corneal thickness measurement by secondary speckle tracking and image processing using machine-learning algorithms.

Corneal thickness (CoT) is an important tool in the evaluation process for several disorders and in the assessment of intraocular pressure. We present a method enabling high-precision measurement of CoT based on secondary speckle tracking and processing of the information by machine-learning (ML) algorithms. The proposed configuration includes capturing by fast camera the laser beam speckle patterns backscattered from the corneal-scleral border, followed by ML processing of the image. The technique was tested on a series of phantoms having different thicknesses as well as in clinical trials on human eyes. The results show high accuracy in determination of eye CoT, and implementation is speedy in comparison with other known measurement methods.