An Ultralow-Power Real-Time Machine Learning Based fNIRS Motion Artifacts Detection.

Due to iterative matrix multiplications or gradient computations, machine learning modules often require a large amount of processing power and memory. As a result, they are often not feasible for use in wearable devices, which have limited processing power and memory. In this study, we propose an ultralow-power and real-time machine learning-based motion artifact detection module for functional near-infrared spectroscopy (fNIRS) systems. We achieved a high classification accuracy of 97.42%, low field-programmable gate array (FPGA) resource utilization of 38354 lookup tables and 6024 flip-flops, as well as low power consumption of 0.021 W in dynamic power. These results outperform conventional CPU support vector machine (SVM) methods and other state-of-the-art SVM implementations. This study has demonstrated that an FPGA-based fNIRS motion artifact classifier can be exploited while meeting low power and resource constraints, which are crucial in embedded hardware systems while keeping high classification accuracy.

Investigation of acousto-optic interaction with momentum mismatching considering acoustic anisotropy in birefringent crystal.

In this work, the momentum mismatching based on which the acousto-optic (AO) transfer function and diffraction efficiency was acquired, was calculated considering the properties of AO crystals in AO interactions in acousto-optic tunable filter (AOTF). Transfer functions were obtained using a 4f optical system combined with AOTF and compared with theoretical calculations. It demonstrated the influence of acoustic energy shift on the AO interaction which should be considered in the design of AOTF.

Mediation role of anxiety on social support and depression among diabetic patients in elderly caring social organizations in China during COVID-19 pandemic: a cross-sectional study.

BACKGROUND: Diabetes has become a prominent global public health problem, which is an important cause of death, disease burden, and medical and health economic burden. Previous studies have reported that majority of persons diagnosed with diabetes later presented with psychological and mental health diseases. The study aimed to explore the mediation role of anxiety on social support and depression among diabetic patents in elderly caring social organizations (SOs). METHODS: A multi-stage stratified cluster random sampling method was used in this cross-sectional study, and a questionnaire consisting of demographic questionnaire, MSPSS, GAD-7, and CES-D-10 was utilized to gather data. SPSS 22.0 and MPLUS 7.4 were used for statistical analysis. Spearman correlation analysis was employed to investigate correlations of key variables. A generalized linear model was used to exam factors associated with depression. Finally, the mediation effect among study variables was investigated by structural equation modeling (SEM). RESULTS: The average scores of social support, anxiety, and depression were 58.41 ± 14.67, 2.95 ± 3.95, and 7.24 ± 5.53, respectively. The factors of gender, social support, and anxiety were identified as significantly influential factors related to depression among diabetic patients in elderly caring SOs. The effect of social support on depression was significantly mediated by anxiety (ß = -0.467, 95%CI: -0.813 to -0.251). Furthermore, anxiety partially mediated the relationship between family support and depression (ß = -0.112, 95%CI: -0.229 to -0.012), and anxiety functioned as a complete mediator in the effect of significant others' support and depression (ß = -0.135, 95%CI: -0.282 to -0.024). CONCLUSIONS: The indirect effect of social support on depression through anxiety among diabetic patients in elderly caring SOs was elucidated. Social support played a key role in maintaining and regulating their mental health, particularly from family and significant others. Social support provided by both family and significant others exerted an important influence on maintaining and regulating their mental health. In light of this pathway, the elderly caring SOs should enhance the magnitude of social support from these two sources, thereby diminishing the likelihood of experiencing anxiety and depression.

Learning based motion artifacts processing in fNIRS: a mini review.

This paper provides a concise review of learning-based motion artifacts (MA) processing methods in functional near-infrared spectroscopy (fNIRS), highlighting the challenges of maintaining optimal contact during subject movement, which can lead to MA and compromise data integrity. Traditional strategies often result in reduced reliability of the hemodynamic response and statistical power. Recognizing the limited number of studies focusing on learning-based MA removal, we examine 315 studies, identifying seven pertinent to our focus area. We discuss the current landscape of learning-based MA correction methods and highlight research gaps. Noting the absence of standard evaluation metrics for quality assessment of MA correction, we suggest a novel framework, integrating signal and model quality considerations and employing metrics like ΔSignal-to-Noise Ratio (ΔSNR), confusion matrix, and Mean Squared Error. This work aims to facilitate the application of learning-based methodologies to fNIRS and improve the accuracy and reliability of neurovascular studies.

Fast optical coherence tomography angiography image acquisition and reconstruction pipeline for skin application.

Traditional high-quality OCTA images require multi-repeated scans (e.g., 4-8 repeats) in the same position, which may cause the patient to be uncomfortable. We propose a deep-learning-based pipeline that can extract high-quality OCTA images from only two-repeat OCT scans. The performance of the proposed image reconstruction U-Net (IRU-Net) outperforms the state-of-the-art UNet vision transformer and UNet in OCTA image reconstruction from a two-repeat OCT signal. The results demonstrated a mean peak-signal-to-noise ratio increased from 15.7 to 24.2; the mean structural similarity index measure improved from 0.28 to 0.59, while the OCT data acquisition time was reduced from 21 seconds to 3.5 seconds (reduced by 83%).

Adaptive polarization photoacoustic computed tomography for biological anisotropic tissue imaging.

Most photoacoustic computed tomography (PACT) systems usually ignore the anisotropy of the tissue absorption coefficient, which will lead to the lack of information in reconstructed images. In this work, the effect is addressed of the possible optical absorption anisotropy of tissue on PACT images. The functional relationship is derived between the photoacoustic response and the polarization angle of the excitation light. An adaptive polarized light photoacoustic imaging (AP-PACT) approach is proposed and shown to make up for the lack of imaging information and achieve optimal image contrast when imaging samples with anisotropic optical absorption, by utilizing the standard deviation of photoacoustic response as the feedback signal in an adaptive data acquisition process. The method is implemented both on phantom and in vitro experiments, which show that AP-PACT can recover anisotropic absorption-related information from reconstructed images and thus significantly improve their quality.

A review of a strategic roadmapping exercise to advance clinical translation of photoacoustic imaging: From current barriers to future adoption.

Photoacoustic imaging (PAI), also referred to as optoacoustic imaging, has shown promise in early-stage clinical trials in a range of applications from inflammatory diseases to cancer. While the first PAI systems have recently received regulatory approvals, successful adoption of PAI technology into healthcare systems for clinical decision making must still overcome a range of barriers, from education and training to data acquisition and interpretation. The International Photoacoustic Standardisation Consortium (IPASC) undertook an community exercise in 2022 to identify and understand these barriers, then develop a roadmap of strategic plans to address them. Here, we outline the nature and scope of the barriers that were identified, along with short-, medium- and long-term community efforts required to overcome them, both within and beyond the IPASC group.

Review of recent advances in frequency-domain near-infrared spectroscopy technologies [Invited].

Over the past several decades, near-infrared spectroscopy (NIRS) has become a popular research and clinical tool for non-invasively measuring the oxygenation of biological tissues, with particular emphasis on applications to the human brain. In most cases, NIRS studies are performed using continuous-wave NIRS (CW-NIRS), which can only provide information on relative changes in chromophore concentrations, such as oxygenated and deoxygenated hemoglobin, as well as estimates of tissue oxygen saturation. Another type of NIRS known as frequency-domain NIRS (FD-NIRS) has significant advantages: it can directly measure optical pathlength and thus quantify the scattering and absorption coefficients of sampled tissues and provide direct measurements of absolute chromophore concentrations. This review describes the current status of FD-NIRS technologies, their performance, their advantages, and their limitations as compared to other NIRS methods. Significant landmarks of technological progress include the development of both benchtop and portable/wearable FD-NIRS technologies, sensitive front-end photonic components, and high-frequency phase measurements. Clinical applications of FD-NIRS technologies are discussed to provide context on current applications and needed areas of improvement. The review concludes by providing a roadmap toward the next generation of fully wearable, low-cost FD-NIRS systems.

The satisfaction of elderly people with elderly caring social organizations and its relationship with social support and anxiety during the COVID-19 pandemic: a cross-sectional study.

BACKGROUND: With the deepening of China's aging population, higher demands have been placed on the supply of elderly care services. As one of the main sources of providing elderly care services, the quality of service provided by elderly caring social organizations (SOs) directly affects the quality of life of the elderly. In recent years, mental health issues among the elderly have become increasingly prominent, especially with the onset of the COVID-19 pandemic. Necessitating the need to pay much more attention to the social support and mental health of this population. This study, therefore, explores the mediating role of institutional satisfaction between the social support and anxiety levels of elderly people in Chongqing's elderly caring SOs. METHOD: This study employed a multi-stage stratified random sampling method to survey 1004 service recipients in elderly caring social organizations from July to August 2022. The self-made sociodemographic questionnaire, institutional satisfaction questionnaire, MSPSS, and GAD-7 were used to collect data on sociodemographic characteristics, institutional satisfaction, social support, and anxiety levels of older adults. Exploratory Factor Analysis and Cronbach's alpha were used to test construct validity and scale reliability, respectively. Data features were described with One-Way Analysis of Variance, while Multiple Linear Regression and Structural Equation Modeling were used to evaluate relationships between social support, institutional satisfaction, and anxiety levels. RESULTS: The average institutional satisfaction score for elderly people in elderly caring SOs was 48.14 ± 6.75. Specifically, the satisfaction score for environmental quality and the satisfaction score for service quality were 16.63 ± 2.56 and 31.52 ± 4.76, respectively. In terms of socio-demographic variables, the presence of visits from relatives, personal annual average income, and self-rated health status all have significant effects on anxiety. Elders who receive visits from relatives have lower levels of anxiety compared to those who do not. Personal annual average income and self-rated health status are negatively correlated with anxiety levels. Social support had significant positive effect on institutional satisfaction, while institutional satisfaction had significant negative effect on anxiety. Institutional satisfaction partially mediated the relationship between social support and anxiety. CONCLUSIONS: Our research demonstrates that improving the quality of organizational services in elderly caring SOs and increasing institutional satisfaction among the elders has significant potential for reducing anxiety levels among the elderly. Additionally, the social support by visits from family members cannot be overlooked. We encourage increasing the frequency of family visits through various means to enhance the support provided to elderly individuals.

A hand-held optical coherence tomography angiography scanner based on angiography reconstruction transformer networks.

Optical coherence tomography angiography (OCTA) has successfully demonstrated its viability for clinical applications in dermatology. Due to the high optical scattering property of skin, extracting high-quality OCTA images from skin tissues requires at least six-repeated scans. While the motion artifacts from the patient and the free hand-held probe can lead to a low-quality OCTA image. Our deep-learning-based scan pipeline enables fast and high-quality OCTA imaging with 0.3-s data acquisition. We utilize a fast scanning protocol with a 60 µm/pixel spatial interval rate and introduce angiography-reconstruction-transformer (ART) for 4× super-resolution of low transverse resolution OCTA images. The ART outperforms state-of-the-art networks in OCTA image super-resolution and provides a lighter network size. ART can restore microvessels while reducing the processing time by 85%, and maintaining improvements in structural similarity and peak-signal-to-noise ratio. This study represents that ART can achieve fast and flexible skin OCTA imaging while maintaining image quality.

The Human Activity Radar Challenge: benchmarking based on the 'Radar signatures of human activities' dataset from Glasgow University.

Radar is an extremely valuable sensing technology for detecting moving targets and measuring their range, velocity, and angular positions. When people are monitored at home, radar is more likely to be accepted by end-users, as they already use WiFi, is perceived as privacy-preserving compared to cameras, and does not require user compliance as wearable sensors do. Furthermore, it is not affected by lighting condi-tions nor requires artificial lights that could cause discomfort in the home environment. So, radar-based human activities classification in the context of assisted living can empower an aging society to live at home independently longer. However, challenges remain as to the formulation of the most effective algorithms for radar-based human activities classification and their validation. To promote the exploration and cross-evaluation of different algorithms, our dataset released in 2019 was used to benchmark various classification approaches. The challenge was open from February 2020 to December 2020. A total of 23 organizations worldwide, forming 12 teams from academia and industry, participated in the inaugural Radar Challenge, and submitted 188 valid entries to the challenge. This paper presents an overview and evaluation of the approaches used for all primary contributions in this inaugural challenge. The proposed algorithms are summarized, and the main parameters affecting their performances are analyzed.

Radar-based human activity recognition with adaptive thresholding towards resource constrained platforms.

Radar systems are increasingly being employed in healthcare applications for human activity recognition due to their advantages in terms of privacy, contactless sensing, and insensitivity to lighting conditions. The proposed classification algorithms are however often complex, focusing on a single domain of radar, and requiring significant computational resources that prevent their deployment in embedded platforms which often have limited memory and computational resources. To address this issue, we present an adaptive magnitude thresholding approach for highlighting the region of interest in the multi-domain micro-Doppler signatures. The region of interest is beneficial to extract salient features, meanwhile it ensures the simplicity of calculations with less computational cost. The results for the proposed approach show an accuracy of up to 93.1% for six activities, outperforming state-of-the-art deep learning methods on the same dataset with an over tenfold reduction in both training time and memory footprint, and a twofold reduction in inference time compared to a series of deep learning implementations. These results can help bridge the gap toward embedded platform deployment.

Research progress on intestinal tissue-resident memory T cells in inflammatory bowel disease.

Tissue-resident memory T (TRM) cells are a recently discovered subpopulation of memory T cells that reside in non-lymphoid tissues such as the intestine and skin and do not enter the bloodstream. The intestine encounters numerous pathogens daily. Intestinal mucosal immunity requires a balance between immune responses to pathogens and tolerance to food antigens and symbiotic microbiota. Therefore, intestinal TRM cells exhibit unique characteristics. In healthy intestines, TRM cells induce necessary inflammation to strengthen the intestinal barrier and inhibit bacterial translocation. During intestinal infections, TRM cells rapidly eliminate pathogens by proliferating, releasing cytokines, and recruiting other immune cells. Moreover, certain TRM cell subsets may have regulatory functions. The involvement of TRM cells in inflammatory bowel disease (IBD) is increasingly recognized as a critical factor. In IBD, the number of pro-inflammatory TRM cells increases, whereas the number of regulatory subgroups decreases. Additionally, the classic markers, CD69 and CD103, are not ideal for intestinal TRM cells. Here, we review the phenotype, development, maintenance, and function of intestinal TRM cells, as well as the latest findings in the context of IBD. Further understanding of the function of intestinal TRM cells and distinguishing their subgroups is crucial for developing therapeutic strategies to target these cells.

Co-optimization method to improve lateral resolution in photoacoustic computed tomography.

In biomedical imaging, photoacoustic computed tomography (PACT) has recently gained increased interest as this imaging technique has good optical contrast and depth of acoustic penetration. However, a spinning blur will be introduced during the image reconstruction process due to the limited size of the ultrasonic transducers (UT) and a discontinuous measurement process. In this study, a damping UT and adaptive back-projection co-optimization (CODA) method is developed to improve the lateral spatial resolution of PACT. In our PACT system, a damping aperture UT controls the size of the receiving area, which suppresses image blur at the signal acquisition stage. Then, an innovative adaptive back-projection algorithm is developed, which corrects the undesirable artifacts. The proposed method was evaluated using agar phantom and ex-vivo experiments. The results show that the CODA method can effectively compensate for the spinning blur and eliminate unwanted artifacts in PACT. The proposed method can significantly improve the lateral spatial resolution and image quality of reconstructed images, making it more appealing for wider clinical applications of PACT as a novel, cost-effective modality.

Semisupervised representative learning for measuring epidermal thickness in human subjects in optical coherence tomography by leveraging datasets from rodent models.

SIGNIFICANCE: Morphological changes in the epidermis layer are critical for the diagnosis and assessment of various skin diseases. Due to its noninvasiveness, optical coherence tomography (OCT) is a good candidate for observing microstructural changes in skin. Convolutional neural network (CNN) has been successfully used for automated segmentation of the skin layers of OCT images to provide an objective evaluation of skin disorders. Such method is reliable, provided that a large amount of labeled data is available, which is very time-consuming and tedious. The scarcity of patient data also puts another layer of difficulty to make the model more generalizable. AIM: We developed a semisupervised representation learning method to provide data augmentations. APPROACH: We used rodent models to train neural networks for accurate segmentation of clinical data. RESULT: The learning quality is maintained with only one OCT labeled image per volume that is acquired from patients. Data augmentation introduces a semantically meaningful variance, allowing for better generalization. Our experiments demonstrate the proposed method can achieve accurate segmentation and thickness measurement of the epidermis. CONCLUSION: This is the first report of semisupervised representative learning applied to OCT images from clinical data by making full use of the data acquired from rodent models. The proposed method promises to aid in the clinical assessment and treatment planning of skin diseases.

Thermography for disease detection in livestock: A scoping review.

Infra-red thermography (IRT) offers potential opportunities as a tool for disease detection in livestock. Despite considerable research in this area, there are no common standards or protocols for managing IRT parameters in animal disease detection research. In this review, we investigate parameters that are essential to the progression of this tool and make recommendations for their use based on the literature found and the veterinary thermography guidelines from the American Academy of Thermology. We analyzed a defined set of 109 articles concerned with the use of IRT in livestock related to disease and from these articles, parameters for accurate IRT were identified and sorted into the fields of camera-, animal- or environment-related categories to assess the practices of each article in reporting parameters. This review demonstrates the inconsistencies in practice across peer-reviewed articles and reveals that some important parameters are completely unreported while others are incorrectly captured and/or under-represented in the literature. Further to this, our review highlights the lack of measured emissivity values for live animals in multiple species. We present guidelines for the standards of parameters that should be used and reported in future experiments and discuss potential opportunities and challenges associated with using IRT for disease detection in livestock.

Two-Photon Absorption: An Open Door to the NIR-II Biological Window?

The way in which photons travel through biological tissues and subsequently become scattered or absorbed is a key limitation for traditional optical medical imaging techniques using visible light. In contrast, near-infrared wavelengths, in particular those above 1000 nm, penetrate deeper in tissues and undergo less scattering and cause less photo-damage, which describes the so-called "second biological transparency window". Unfortunately, current dyes and imaging probes have severely limited absorption profiles at such long wavelengths, and molecular engineering of novel NIR-II dyes can be a tedious and unpredictable process, which limits access to this optical window and impedes further developments. Two-photon (2P) absorption not only provides convenient access to this window by doubling the absorption wavelength of dyes, but also increases the possible resolution. This review aims to provide an update on the available 2P instrumentation and 2P luminescent materials available for optical imaging in the NIR-II window.

Machine learning-enabled quantitative ultrasound techniques for tissue differentiation.

PURPOSE: Quantitative ultrasound (QUS) infers properties about tissue microstructure from backscattered radio-frequency ultrasound data. This paper describes how to implement the most practical QUS parameters using an ultrasound research system for tissue differentiation. METHODS: This study first validated chicken liver and gizzard muscle as suitable acoustic phantoms for human brain and brain tumour tissues via measurement of the speed of sound and acoustic attenuation. A total of thirteen QUS parameters were estimated from twelve samples, each using data obtained with a transducer with a frequency of 5-11 MHz. Spectral parameters, i.e., effective scatterer diameter and acoustic concentration, were calculated from the backscattered power spectrum of the tissue, and echo envelope statistics were estimated by modelling the scattering inside the tissue as a homodyned K-distribution, yielding the scatterer clustering parameter α and the structure parameter κ. Standard deviation and higher-order moments were calculated from the echogenicity value assigned in conventional B-mode images. RESULTS: The k-nearest neighbours algorithm was used to combine those parameters, which achieved 94.5% accuracy and 0.933 F1-score. CONCLUSION: We were able to generate classification parametric images in near-real-time speed as a potential diagnostic tool in the operating room for the possible use for human brain tissue characterisation.

Relationship between histological mixed-type early gastric cancer and lymph node metastasis: A systematic review and meta-analysis.

The clinicopathological features of early gastric cancer (EGC) with mixed-type histology (differentiated and undifferentiated) are incompletely understood, and the capacity of endoscopic submucosal dissection (ESD) to treat mixed-type cancer remains controversial. This systematic review analyzed the rate of lymph node metastasis (LNM) in mixed-type EGC. We gathered articles published up to February 21, 2021, that analyzed the relationship between LNM and mixed-type EGC from Embase, PubMed, and Web of Science. The primary outcome was the LNM rate associated with different histological types of EGC, and the secondary outcomes were the odds ratios (ORs) for LNM risk factors among EGC patients. From the 24 studies included in this meta-analysis, the overall rate of LNM in predominantly differentiated mixed-type (MD) EGC was 12%, whereas the LNM rate in predominantly undifferentiated mixed-type (MU) EGC was 22%. We further divided these studies into 2 groups according to the depth of invasion. In mixed-type mucosal EGC, the pooled LNM rate was 15%; in submucosal EGC, the rate was 33% for MU, which was higher than the rates for pure types (pure differentiated type, 13%; pure undifferentiated type, 21%; p<0.05). The LNM rate of MD was 20%, it was higher than those of the pure differentiated type and nearly the same as pure undifferentiated type. Other pooled statistics showed that submucosal invasion, pure undifferentiated EGC, and mixed-type EGC were independent risk factors for LNM. This meta-analysis showed that MD submucosal EGC has a high rate of LNM and is highly correlated with LNM; thus, the management of MD EGC as purely differentiated EGC according to the indications for ESD is inappropriate, and the mixed type should be added as a parameter in these indications.

Deep-learning approach for automated thickness measurement of epithelial tissue and scab using optical coherence tomography.

SIGNIFICANCE: In order to elucidate therapeutic treatment to accelerate wound healing, it is crucial to understand the process underlying skin wound healing, especially re-epithelialization. Epidermis and scab detection is of importance in the wound healing process as their thickness is a vital indicator to judge whether the re-epithelialization process is normal or not. Since optical coherence tomography (OCT) is a real-time and non-invasive imaging technique that can perform a cross-sectional evaluation of tissue microstructure, it is an ideal imaging modality to monitor the thickness change of epidermal and scab tissues during wound healing processes in micron-level resolution. Traditional segmentation on epidermal and scab regions was performed manually, which is time-consuming and impractical in real time. AIM: We aim to develop a deep-learning-based skin layer segmentation method for automated quantitative assessment of the thickness of in vivo epidermis and scab tissues during a time course of healing within a rodent model. APPROACH: Five convolution neural networks were trained using manually labeled epidermis and scab regions segmentation from 1000 OCT B-scan images (assisted by its corresponding angiographic information). The segmentation performance of five segmentation architectures was compared qualitatively and quantitatively for validation set. RESULTS: Our results show higher accuracy and higher speed of the calculated thickness compared with human experts. The U-Net architecture represents a better performance than other deep neural network architectures with 0.894 at F1-score, 0.875 at mean intersection over union, 0.933 at Dice similarity coefficient, and 18.28 µm at an average symmetric surface distance. Furthermore, our algorithm is able to provide abundant quantitative parameters of the wound based on its corresponding thickness maps in different healing phases. Among them, normalized epidermal thickness is recommended as an essential hallmark to describe the re-epithelialization process of the rodent model. CONCLUSIONS: The automatic segmentation and thickness measurements within different phases of wound healing data demonstrates that our pipeline provides a robust, quantitative, and accurate method for serving as a standard model for further research into effect of external pharmacological and physical factors.