Diabetic patients need long-term and frequent glucose monitoring to assist in insulin intake. The current finger-prick devices are painful and costly, which places noninvasive glucose sensors in high demand. In this review paper, we list several advanced electromagnetic (EM)-wave-based technologies for noninvasive glucose measurement, including infrared (IR) spectroscopy, photoacoustic (PA) spectroscopy, Raman spectroscopy, fluorescence, optical coherence tomography (OCT), Terahertz (THz) spectroscopy, and microwave sensing. The development of each method is discussed regarding the fundamental principle, system setup, and experimental results. Despite the promising achievements that have been previously reported, no established product has obtained FDA approval or survived a marketing test. The limitations of, and prospects for, these techniques are presented at the end of this review.

Optical focusing and imaging through or inside scattering media, such multimode fiber and biological tissues, has significant impact in biomedicine yet considered challenging due to strong scattering nature of light. In the past decade, promising progress has been made in the field, largely benefiting from the invention of iterative optical wavefront shaping, with which deep-tissue high-resolution optical focusing and hence imaging becomes possible. Most of reported iterative algorithms can overcome small perturbations on the noise level but fail to effectively adapt beyond the noise level, e.g. sudden strong perturbations. Re-optimizations are usually needed for significant decorrelation to the medium since these algorithms heavily rely on the optimization performance in the previous iterations. Such ineffectiveness is probably due to the absence of a metric that can gauge the deviation of the instant wavefront from the optimum compensation based on the concurrently measured optical focusing. In this study, a square rule of binary-amplitude modulation, directly relating the measured focusing performance with the error in the optimized wavefront, is theoretically proved and experimentally validated. With this simple rule, it is feasible to quantify how many pixels on the spatial light modulator incorrectly modulate the wavefront for the instant status of the medium or the whole system. As an example of application, we propose a novel algorithm, dynamic mutation algorithm (DMA), which has high adaptability against perturbations by probing how far the optimization has gone toward the theoretically optimal performance. The diminished focus of scattered light can be effectively recovered when perturbations to the medium cause significant drop of the focusing performance, which no existing algorithms can achieve due to their inherent strong dependence on previous optimizations. With further improvement, the square rule and the new algorithm may boost or inspire many applications, such as high-resolution optical imaging and stimulation, in instable or dynamic scattering environments.

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We proposed a novel noninvasive method for glucose detection using a photoacoustic (PA) technique utilizing both the peak-to-peak value and peak arrival time delay information. Without increasing apparatus and system complexity, we demonstrated the accuracy enhancement of 29.5% and 33.63% for high (0-7 g dL) and low (0-350 mg dL) concentration of glucose solution, compared to conventional amplitude-based prediction.

Coherent optical control within or through scattering media via wavefront shaping has seen broad applications since its invention around 2007. Wavefront shaping is aimed at overcoming the strong scattering, featured by random interference, namely speckle patterns. This randomness occurs due to the refractive index inhomogeneity in complex media like biological tissue or the modal dispersion in multimode fiber, yet this randomness is actually deterministic and potentially can be time reversal or precompensated. Various wavefront shaping approaches, such as optical phase conjugation, iterative optimization, and transmission matrix measurement, have been developed to generate tight and intense optical delivery or high-resolution image of an optical object behind or within a scattering medium. The performance of these modulations, however, is far from satisfaction. Most recently, artificial intelligence has brought new inspirations to this field, providing exciting hopes to tackle the challenges by mapping the input and output optical patterns and building a neuron network that inherently links them. In this paper, we survey the developments to date on this topic and briefly discuss our views on how to harness machine learning (deep learning in particular) for further advancements in the field.