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.

A handheld approach to three-dimensional (3D) photoacoustic imaging is essential in clinical applications. To this end, we develop a 3D handheld photoacoustic imager for dynamic (temporally and spatially) volumetric visualization. In this 3D imager, the optically transmitting part and the acoustically receiving part are integrated into a single handheld probe with a compact size about 160 mm×64 mm×40 mm. Besides, a dedicated imaging reconstruction algorithm for the heterogeneous medium is developed based on the phase-shift migration (PSM) method in frequency domain, which deals well with the stratified condition in the designed system. Dynamic 3D imaging supporting flexible handheld operation is demonstrated with needle biopsy and in-vitro temperature measurement for photothermal therapy. The development of such a 3D handheld photoacoustic system paves the way for compact and handheld-operating implementations, and its further clinical exploration is promising.

Conventional electromagnetic (EM) sensing techniques such as radar and LiDAR are widely used for remote sensing, vehicle applications, weather monitoring, and clinical monitoring. Acoustic techniques such as sonar and ultrasound sensors are also used for consumer applications, such as ranging and in vivo medical/healthcare applications. It has been of long-term interest to doctors and clinical practitioners to realize continuous healthcare monitoring in hospitals and/or homes. Physiological and biopotential signals in real-time serve as important health indicators to predict and prevent serious illness. Emerging electromagnetic-acoustic (EMA) sensing techniques synergistically combine the merits of EM sensing with acoustic imaging to achieve comprehensive detection of physiological and biopotential signals. Further, EMA enables complementary fusion sensing for challenging healthcare settings, such as real-world long-term monitoring of treatment effects at home or in remote environments. This article reviews various examples of EMA sensing instruments, including implementation, performance, and application from the perspectives of circuits to systems. The novel and significant applications to healthcare are discussed. Three types of EMA sensors are presented: (1) Chip-based radar sensors for health status monitoring, (2) Thermo-acoustic sensing instruments for biomedical applications, and (3) Photoacoustic (PA) sensing and imaging systems, including dedicated reconstruction algorithms were reviewed from time-domain, frequency-domain, time-reversal, and model-based solutions. The future of EMA techniques for continuous healthcare with enhanced accuracy supported by artificial intelligence (AI) is also presented.

Recently, many reconstruction methods have been developed to improve the lateral resolution of acoustic-resolution photoacoustic microscopy (ARPAM) in out-of-focus regions. Though these methods enhance image resolution to some extent, they require advanced computational hardware and large computational time, especially for three-dimensional cases. However, some methods do not consider the finite size of a transducer, while others employ numerical discretization to build a focused transducer model which is less efficient and accurate. To overcome these problems, we propose a three-dimensional ARPAM imaging reconstruction method with high precision, high efficiency, and low memory cost. It inherits the framework of model-based reconstructions and incorporates the forward acoustic model in hybrid-domain. This hybrid-domain acoustic model promotes an analytical solution to establish a focused transducer model. Furthermore, the non-uniform fast Fourier transform (NUFFT) and deconvolution methods are introduced to reduce the required computational time and memory volume for three-dimensional reconstructions. According to the experiment results reconstructed by the proposed method, the lateral resolution of an ARPAM image recorded by a 20MHz focused transducer (NA 0.393) can reach 88.39μm. This resolution exceeds the diffraction limitation of the focused transducer (137.8μm). When reconstructing a three-dimensional image with 200× 200× 150 pixels, the proposed method takes only 8.15 seconds using a laptop loaded with Intel(R) Core (TM) i7-8550U CPU @ 1.8GHz and 1.06GB memory.

Biosensing and bioimaging are essential in understanding biological and pathological processes in a living system, for example, in detecting and understanding certain diseases. Optical fiber has made remarkable contributions to the biosensing and bioimaging areas due to its unique advantages of compact size, immunity to electromagnetic interference, biocompatibility, fast response, etc. This review paper will present an overview of seven common types of optical fiber biosensors and optical fiber-based ultrasound detection in photoacoustic imaging (PAI) and the applications of these technologies in biosensing and bioimaging areas. Of course, there are many types of optical fiber biosensors. Still, this paper will review the most common ones: optical fiber grating, surface plasmon resonance, Sagnac interferometer, Mach–Zehnder interferometer, Michelson interferometer, Fabry–Perot Interferometer, lossy mode resonance, and surface-enhanced Raman scattering. Furthermore, different optical fiber techniques for detecting ultrasound in PAI are summarized. Finally, the main challenges and future development direction are briefly discussed.

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⁻¹) and low (0–350 mg dL⁻¹) concentration of glucose solution, compared to conventional amplitude-based prediction.