Computer-aided screening of aspiration risks in dysphagia with wearable technology: a Systematic Review and meta-analysis on test accuracy.

Aspiration caused by dysphagia is a prevalent problem that causes serious health consequences and even death. Traditional diagnostic instruments could induce pain, discomfort, nausea, and radiation exposure. The emergence of wearable technology with computer-aided screening might facilitate continuous or frequent assessments to prompt early and effective management. The objectives of this review are to summarize these systems to identify aspiration risks in dysphagic individuals and inquire about their accuracy. Two authors independently searched electronic databases, including CINAHL, Embase, IEEE Xplore® Digital Library, PubMed, Scopus, and Web of Science (PROSPERO reference number: CRD42023408960). The risk of bias and applicability were assessed using QUADAS-2. Nine (n = 9) articles applied accelerometers and/or acoustic devices to identify aspiration risks in patients with neurodegenerative problems (e.g., dementia, Alzheimer's disease), neurogenic problems (e.g., stroke, brain injury), in addition to some children with congenital abnormalities, using videofluoroscopic swallowing study (VFSS) or fiberoptic endoscopic evaluation of swallowing (FEES) as the reference standard. All studies employed a traditional machine learning approach with a feature extraction process. Support vector machine (SVM) was the most famous machine learning model used. A meta-analysis was conducted to evaluate the classification accuracy and identify risky swallows. Nevertheless, we decided not to conclude the meta-analysis findings (pooled diagnostic odds ratio: 21.5, 95% CI, 2.7-173.6) because studies had unique methodological characteristics and major differences in the set of parameters/thresholds, in addition to the substantial heterogeneity and variations, with sensitivity levels ranging from 21.7% to 90.0% between studies. Small sample sizes could be a critical problem in existing studies (median = 34.5, range 18-449), especially for machine learning models. Only two out of the nine studies had an optimized model with sensitivity over 90%. There is a need to enlarge the sample size for better generalizability and optimize signal processing, segmentation, feature extraction, classifiers, and their combinations to improve the assessment performance. Systematic Review Registration: (https://www.crd.york.ac.uk/prospero/), identifier (CRD42023408960).

Vision Transformers (ViT) for Blanket-Penetrating Sleep Posture Recognition Using a Triple Ultra-Wideband (UWB) Radar System.

Sleep posture has a crucial impact on the incidence and severity of obstructive sleep apnea (OSA). Therefore, the surveillance and recognition of sleep postures could facilitate the assessment of OSA. The existing contact-based systems might interfere with sleeping, while camera-based systems introduce privacy concerns. Radar-based systems might overcome these challenges, especially when individuals are covered with blankets. The aim of this research is to develop a nonobstructive multiple ultra-wideband radar sleep posture recognition system based on machine learning models. We evaluated three single-radar configurations (top, side, and head), three dual-radar configurations (top + side, top + head, and side + head), and one tri-radar configuration (top + side + head), in addition to machine learning models, including CNN-based networks (ResNet50, DenseNet121, and EfficientNetV2) and vision transformer-based networks (traditional vision transformer and Swin Transformer V2). Thirty participants (n = 30) were invited to perform four recumbent postures (supine, left side-lying, right side-lying, and prone). Data from eighteen participants were randomly chosen for model training, another six participants' data (n = 6) for model validation, and the remaining six participants' data (n = 6) for model testing. The Swin Transformer with side and head radar configuration achieved the highest prediction accuracy (0.808). Future research may consider the application of the synthetic aperture radar technique.

A Comprehensive Assessment Protocol for Swallowing (CAPS): Paving the Way towards Computer-Aided Dysphagia Screening.

Dysphagia is one of the most common problems among older adults, which might lead to aspiration pneumonia and eventual death. It calls for a feasible, reliable, and standardized screening or assessment method to prompt rehabilitation measures and mitigate the risks of dysphagia complications. Computer-aided screening using wearable technology could be the solution to the problem but is not clinically applicable because of the heterogeneity of assessment protocols. The aim of this paper is to formulate and unify a swallowing assessment protocol, named the Comprehensive Assessment Protocol for Swallowing (CAPS), by integrating existing protocols and standards. The protocol consists of two phases: the pre-test phase and the assessment phase. The pre-testing phase involves applying different texture or thickness levels of food/liquid and determining the required bolus volume for the subsequent assessment. The assessment phase involves dry (saliva) swallowing, wet swallowing of different food/liquid consistencies, and non-swallowing (e.g., yawning, coughing, speaking, etc.). The protocol is designed to train the swallowing/non-swallowing event classification that facilitates future long-term continuous monitoring and paves the way towards continuous dysphagia screening.

Endocrine Tumor Classification via Machine-Learning-Based Elastography: A Systematic Scoping Review.

Elastography complements traditional medical imaging modalities by mapping tissue stiffness to identify tumors in the endocrine system, and machine learning models can further improve diagnostic accuracy and reliability. Our objective in this review was to summarize the applications and performance of machine-learning-based elastography on the classification of endocrine tumors. Two authors independently searched electronic databases, including PubMed, Scopus, Web of Science, IEEEXpress, CINAHL, and EMBASE. Eleven (n = 11) articles were eligible for the review, of which eight (n = 8) focused on thyroid tumors and three (n = 3) considered pancreatic tumors. In all thyroid studies, the researchers used shear-wave ultrasound elastography, whereas the pancreas researchers applied strain elastography with endoscopy. Traditional machine learning approaches or the deep feature extractors were used to extract the predetermined features, followed by classifiers. The applied deep learning approaches included the convolutional neural network (CNN) and multilayer perceptron (MLP). Some researchers considered the mixed or sequential training of B-mode and elastographic ultrasound data or fusing data from different image segmentation techniques in machine learning models. All reviewed methods achieved an accuracy of ≥80%, but only three were ≥90% accurate. The most accurate thyroid classification (94.70%) was achieved by applying sequential training CNN; the most accurate pancreas classification (98.26%) was achieved using a CNN-long short-term memory (LSTM) model integrating elastography with B-mode and Doppler images.

Depth-Camera-Based Under-Blanket Sleep Posture Classification Using Anatomical Landmark-Guided Deep Learning Model.

Emerging sleep health technologies will have an impact on monitoring patients with sleep disorders. This study proposes a new deep learning model architecture that improves the under-blanket sleep posture classification accuracy by leveraging the anatomical landmark feature through an attention strategy. The system used an integrated visible light and depth camera. Deep learning models (ResNet-34, EfficientNet B4, and ECA-Net50) were trained using depth images. We compared the models with and without an anatomical landmark coordinate input generated with an open-source pose estimation model using visible image data. We recruited 120 participants to perform seven major sleep postures, namely, the supine posture, prone postures with the head turned left and right, left- and right-sided log postures, and left- and right-sided fetal postures under four blanket conditions, including no blanket, thin, medium, and thick. A data augmentation technique was applied to the blanket conditions. The data were sliced at an 8:2 training-to-testing ratio. The results showed that ECA-Net50 produced the best classification results. Incorporating the anatomical landmark features increased the F1 score of ECA-Net50 from 87.4% to 92.2%. Our findings also suggested that the classification performances of deep learning models guided with features of anatomical landmarks were less affected by the interference of blanket conditions.

Breast Tumour Classification Using Ultrasound Elastography with Machine Learning: A Systematic Scoping Review.

Ultrasound elastography can quantify stiffness distribution of tissue lesions and complements conventional B-mode ultrasound for breast cancer screening. Recently, the development of computer-aided diagnosis has improved the reliability of the system, whilst the inception of machine learning, such as deep learning, has further extended its power by facilitating automated segmentation and tumour classification. The objective of this review was to summarize application of the machine learning model to ultrasound elastography systems for breast tumour classification. Review databases included PubMed, Web of Science, CINAHL, and EMBASE. Thirteen (n = 13) articles were eligible for review. Shear-wave elastography was investigated in six articles, whereas seven studies focused on strain elastography (5 freehand and 2 Acoustic Radiation Force). Traditional computer vision workflow was common in strain elastography with separated image segmentation, feature extraction, and classifier functions using different algorithm-based methods, neural networks or support vector machines (SVM). Shear-wave elastography often adopts the deep learning model, convolutional neural network (CNN), that integrates functional tasks. All of the reviewed articles achieved sensitivity ³ 80%, while only half of them attained acceptable specificity ³ 95%. Deep learning models did not necessarily perform better than traditional computer vision workflow. Nevertheless, there were inconsistencies and insufficiencies in reporting and calculation, such as the testing dataset, cross-validation, and methods to avoid overfitting. Most of the studies did not report loss or hyperparameters. Future studies may consider using the deep network with an attention layer to locate the targeted object automatically and online training to facilitate efficient re-training for sequential data.

Swallow Detection with Acoustics and Accelerometric-Based Wearable Technology: A Scoping Review.

Swallowing disorders, especially dysphagia, might lead to malnutrition and dehydration and could potentially lead to fatal aspiration. Benchmark swallowing assessments, such as videofluoroscopy or endoscopy, are expensive and invasive. Wearable technologies using acoustics and accelerometric sensors could offer opportunities for accessible and home-based long-term assessment. Identifying valid swallow events is the first step before enabling the technology for clinical applications. The objective of this review is to summarize the evidence of using acoustics-based and accelerometric-based wearable technology for swallow detection, in addition to their configurations, modeling, and assessment protocols. Two authors independently searched electronic databases, including PubMed, Web of Science, and CINAHL. Eleven (n = 11) articles were eligible for review. In addition to swallowing events, non-swallowing events were also recognized by dry (saliva) swallowing, reading, yawning, etc., while some attempted to classify the types of swallowed foods. Only about half of the studies reported that the device attained an accuracy level of >90%, while a few studies reported poor performance with an accuracy of <60%. The reviewed articles were at high risk of bias because of the small sample size and imbalanced class size problem. There was high heterogeneity in assessment protocol that calls for standardization for swallowing, dry-swallowing and non-swallowing tasks. There is a need to improve the current wearable technology and the credibility of relevant research for accurate swallowing detection before translating into clinical screening for dysphagia and other swallowing disorders.

Two-Week Exclusive Supplementation of Modified Ketogenic Nutrition Drink Reserves Lean Body Mass and Improves Blood Lipid Profile in Obese Adults: A Randomized Clinical Trial.

The ketogenic diet has long been recommended in patients with neurological disorders, and its protective effects on the cardiovascular system are of growing research interest. This study aimed to investigate the effects of two-week of low-calorie ketogenic nutrition drinks in obese adults. Subjects were randomized to consume drinks either a ketone-to-non-ketone ratio of 4:1 (KD 4:1), a drink partially complemented with protein at 1.7:1 (KD 1.7:1), or a balanced nutrition drink (BD). Changes in body weight, body composition, blood lipid profile, and blood ketone bodies were investigated. Blood ketone bodies were induced and maintained in the group that consumed both 4:1 and 1.7:1 ketogenic drinks (p < 0.001). Body weight and body fat mass significantly declined in all groups between 0 and 1 week and between 1 and 2 weeks (p < 0.05), while skeletal muscle mass remained unchanged only in the KD 1.7:1 group (p > 0.05). The blood lipid profile improved, appetite was reduced, and fullness was maintained in the two ketogenic drink groups. This study indicates the possibility for the development of obesity treatments based on ketogenic nutrition drinks even with a moderate ketogenic ratio of 1.7:1, as well as adjuvant therapies based on ketosis induction and maintenance for the treatment of other diseases and health conditions.

Attenuation of RANKL-induced Osteoclast Formation via p38-mediated NFATc1 Signaling Pathways by Extract of Euphorbia Lathyris L.

BACKGROUND: Osteoclasts are the only cell type capable of breaking down bone matrix, and its excessive activation is responsible for the development of bone-destructive diseases. Euphorbia lathyris L. (ELL) is an herbal plant that belongs to the Euphorbiaceae family. This study investigated the effects of the methanol extract of the aerial part of ELL on receptor activator of nuclear factor-kappa B ligand (RANKL)-induced osteoclast formation and signaling pathways. METHODS: Osteoclasts were formed by co-culturing mouse bone marrow with osteoblasts or by culturing mouse bone marrow-derived macrophages (BMMs) with macrophage colony-stimulating factor (M-CSF) and RANKL. Bone resorption assays were performed using dentine slices. The expression level of mRNA was analyzed by real-time polymerase chain reaction (PCR) or reverse transcription (RT)-PCR. Western blotting assays were performed to detect the expression or activation level of proteins. RESULTS: ELL inhibited RANKL-induced osteoclast formation without cytotoxicity. Furthermore, the RANKL-stimulated bone resorption was diminished by ELL. Mechanistically, ELL blocked the RANKL-triggered p38 mitogen-activated protein kinase (MAPK) phosphorylation, which resulted in the suppression of the expression of c-Fos and nuclear factor of activated T cells (NFATc1). In osteoblasts, ELL had little effect on the mRNA expression of RANKL and osteoprotegerin (OPG). CONCLUSIONS: The present data suggest that ELL has an inhibitory effect on osteoclast differentiation and function via downregulation of the p38/c-Fos/NFATc1 signaling pathways. Thus, ELL could be useful for the treatment of bone diseases associated with excessive bone resorption.

Functionalization of indium tin oxide electrode with both of dendrimer-encapsulated Pt nanoparticles and chemically converted graphenes for enhanced electrochemiluminescence of luminol/H2O2.

Here, we report highly enhanced electrochemiluminescence (ECL) of luminol in the presence of H2O2 on indium tin oxides (ITOs) modified with both of dendrimer-encapsulated Pt nanoparticles (Pt DENs) and chemically converted graphenes (CCGs). The ITO electrodes were electrochemically modified with size-monodisperse Pt DENs via electrooxidative grafting of the terminal amines of the dendrimers encapsulating Pt nanoparticles. The Pt DEN-modified ITOs were then decorated with CCG sheets via electrostatic attachments of graphene oxides (GOs) and subsequent chemical reduction of the GOs to the CCGs. The resulting CCG-Pt DEN/ITO electrodes exhibited highly catalyzed electrochemical oxidation of luminol/H2O2, leading to significantly enhanced ECL of the luminol/H2O2 system, i.e., ∼15-fold enhancement, compared to ECL emission from bare ITOs even at lower applied potentials, which allowed sensitive ECL-based analysis of H2O2 using the CCG-Pt DEN/ITOs. Graphical abstract We report the highly enhanced electrochemiluminescence of the luminol/H2O2 system on the indium tin oxide electrodes modified with both of Pt nanoparticles and chemically converted graphenes using amine-terminated dendrimers.

Tailoring Catalytic Activity of Pt Nanoparticles Encapsulated Inside Dendrimers by Tuning Nanoparticle Sizes with Subnanometer Accuracy for Sensitive Chemiluminescence-Based Analyses.

Here, we report the size-dependent catalysis of Pt dendrimer-encapsulated nanoparticles (DENs) having well-defined sizes over the range of 1-3 nm with subnanometer accuracy for the highly enhanced chemiluminescence of the luminol/H2O2 system. This size-dependent catalysis is ascribed to the differences in the chemical states of the Pt DENs as well as in their surface areas depending on their sizes. Facile and versatile applications of the Pt DENs in diverse oxidase-based assays are demonstrated as efficient catalysts for sensitive chemiluminescence-based analyses.