Automated, multiparametric monitoring of respiratory biomarkers and vital signs in clinical and home settings for COVID-19 patients

Measuring Real-World Talk Time and Locations of People With Aphasia Using Wearable Technology

PURPOSE

Measuring real-world communication participation of individuals with aphasia is complicated. Historically, this has been estimated through subjective participant or proxy self-report. To address potential inaccuracies, objective measures such as "talk time" have been proposed. Although promising, technological barriers to collecting and quantifying everyday conversations have been documented (e.g., background noise interference, differentiating recorded speakers, and operating Bluetooth applications). This study explored the use of a novel laryngeal sensor and a Global Positioning System (GPS) tracker with the objective of measuring mean talk time per hour and participant locations across three 8-hr days.

METHOD

Sixteen participants utilized a wearable laryngeal sensor that captures physiological mechano-acoustic signals wirelessly, without recording speech content. The sensor differentiates speech from other laryngeal movements associated with swallowing and coughing. A GPS tracker was also issued to track daily locations. Semistructured interviews regarding feasibility and acceptability were conducted with participants at the end of the data collection period.

RESULTS

Across all participants, laryngeal sensor data were collected for a total of 38 days and GPS data for a total of 43 days, with a mean collection period of 8.21 hr (SD = 1.38) per day. Mean talk time per hour was 56.46 s (SD = 35.27). Participants were tracked at a mean of 2.09 locations daily (range: 1-6). Participants reported the devices were relatively comfortable to wear and easy to use.

CONCLUSIONS

Preliminary findings indicated that talk time of individuals with aphasia is limited, though variable. Higher fluency ratings were related to greater mean talk time per hour and locations tracked. Results suggest wearable technology is feasible to use and acceptable to people with aphasia. In the future, wearable devices may offer innovative ways to measure communication participation.

SUPPLEMENTAL MATERIAL

https://doi.org/10.23641/asha.26237531.

Modular Reconfigurable Approach Toward Noninvasive Wearable Body Net for Monitoring Sweat and Physiological Signals

In the realm of wearable technology, strategically placing sensors at various body locations enhances the detection of diverse physiological indicators crucial for remote medical care. However, current devices often focus on a single body part for specific physical parameters, which hinders the seamless integration of sensors across multiple body parts and necessitates redesign for new detection capabilities. Here, we propose a modular, reconfigurable circuit assembly method that can be adaptable for multiple body locations to construct the body net. By simply reassembling different child modules with the base module using flexible printed circuit board connectors, we can efficiently detect various parameters including sweat ion indicators, electrocardiogram signals, electromyography signals, motion data, heart rate, blood oxygen saturation, and skin temperature. These data can be transmitted to a mobile phone app via a Bluetooth Low Energy protocol for further evaluation. Comparative evaluations against established commercial devices substantiate the viability of our sensor technology. In addition, results from wearable body network detections using reconfigurable sensors across multiple body parts of volunteers also indicate promising application prospects, demonstrating the extensive potential for regular health monitoring and clinical applications.

Sensory artificial cilia for in situ monitoring of airway physiological properties

Continuously monitoring human airway conditions is crucial for timely interventions, especially when airway stents are implanted to alleviate central airway obstruction in lung cancer and other diseases. Mucus conditions, in particular, are important biomarkers for indicating inflammation and stent patency but remain challenging to monitor. Current methods, reliant on computational tomography imaging and bronchoscope inspection, pose risks due to radiation and lack the ability to provide continuous real-time feedback outside of hospitals. Inspired by the sensing ability of biological cilia, we report wireless sensing mechanisms in sensory artificial cilia for detecting mucus conditions, including viscosity and layer thickness, which are crucial biomarkers for disease severity. The sensing mechanism for mucus viscosity leverages external magnetic fields to actuate a magnetic artificial cilium and sense its shape using a flexible strain-gauge. Additionally, we report an artificial cilium with capacitance sensing for mucus layer thickness, offering unique self-calibration, adjustable sensitivity, and range, all enabled by external magnetic fields. To enable prolonged and wireless data access, we integrate Bluetooth Low Energy communication and onboard power, along with a wearable magnetic actuation system for sensor activation. We validate our method by deploying the sensor independently or in conjunction with an airway stent within a trachea phantom and sheep trachea ex vivo. The proposed sensing mechanisms and devices pave the way for real-time monitoring of mucus conditions, facilitating early disease detection and providing stent patency alerts, thereby allowing timely interventions and personalized care.

Development of a Miniaturized Mechanoacoustic Sensor for Continuous, Objective Cough Detection, Characterization and Physiologic Monitoring in Children With Cystic Fibrosis

Cough is an important symptom in children with acute and chronic respiratory disease. Daily cough is common in Cystic Fibrosis (CF) and increased cough is a symptom of pulmonary exacerbation. To date, cough assessment is primarily subjective in clinical practice and research. Attempts to develop objective, automatic cough counting tools have faced reliability issues in noisy environments and practical barriers limiting long-term use. This single-center pilot study evaluated usability, acceptability and performance of a mechanoacoustic sensor (MAS), previously used for cough classification in adults, in 36 children with CF over brief and multi-day periods in four cohorts. Children whose health was at baseline and who had symptoms of pulmonary exacerbation were included. We trained, validated, and deployed custom deep learning algorithms for accurate cough detection and classification from other vocalization or artifacts with an overall area under the receiver-operator characteristic curve (AUROC) of 0.96 and average precision (AP) of 0.93. Child and parent feedback led to a redesign of the MAS towards a smaller, more discreet device acceptable for daily use in children. Additional improvements optimized power efficiency and data management. The MAS's ability to objectively measure cough and other physiologic signals across clinic, hospital, and home settings is demonstrated, particularly aided by an AUROC of 0.97 and AP of 0.96 for motion artifact rejection. Examples of cough frequency and physiologic parameter correlations with participant-reported outcomes and clinical measurements for individual patients are presented. The MAS is a promising tool in objective longitudinal evaluation of cough in children with CF.

Wearable network for multilevel physical fatigue prediction in manufacturing workers

Manufacturing workers face prolonged strenuous physical activities, impacting both financial aspects and their health due to work-related fatigue. Continuously monitoring physical fatigue and providing meaningful feedback is crucial to mitigating human and monetary losses in manufacturing workplaces. This study introduces a novel application of multimodal wearable sensors and machine learning techniques to quantify physical fatigue and tackle the challenges of real-time monitoring on the factory floor. Unlike past studies that view fatigue as a dichotomous variable, our central formulation revolves around the ability to predict multilevel fatigue, providing a more nuanced understanding of the subject's physical state. Our multimodal sensing framework is designed for continuous monitoring of vital signs, including heart rate, heart rate variability, skin temperature, and more, as well as locomotive signs by employing inertial motion units strategically placed at six locations on the upper body. This comprehensive sensor placement allows us to capture detailed data from both the torso and arms, surpassing the capabilities of single-point data collection methods. We developed an innovative asymmetric loss function for our machine learning model, which enhances prediction accuracy for numerical fatigue levels and supports real-time inference. We collected data on 43 subjects following an authentic manufacturing protocol and logged their self-reported fatigue. Based on the analysis, we provide insights into our multilevel fatigue monitoring system and discuss results from an in-the-wild evaluation of actual operators on the factory floor. This study demonstrates our system's practical applicability and contributes a valuable open-access database for future research.

AI-Assisted Detection of Biomarkers by Sensors and Biosensors for Early Diagnosis and Monitoring

The steady progress in consumer electronics, together with improvement in microflow techniques, nanotechnology, and data processing, has led to implementation of cost-effective, user-friendly portable devices, which play the role of not only gadgets but also diagnostic tools. Moreover, numerous smart devices monitor patients' health, and some of them are applied in point-of-care (PoC) tests as a reliable source of evaluation of a patient's condition. Current diagnostic practices are still based on laboratory tests, preceded by the collection of biological samples, which are then tested in clinical conditions by trained personnel with specialistic equipment. In practice, collecting passive/active physiological and behavioral data from patients in real time and feeding them to artificial intelligence (AI) models can significantly improve the decision process regarding diagnosis and treatment procedures via the omission of conventional sampling and diagnostic procedures while also excluding the role of pathologists. A combination of conventional and novel methods of digital and traditional biomarker detection with portable, autonomous, and miniaturized devices can revolutionize medical diagnostics in the coming years. This article focuses on a comparison of traditional clinical practices with modern diagnostic techniques based on AI and machine learning (ML). The presented technologies will bypass laboratories and start being commercialized, which should lead to improvement or substitution of current diagnostic tools. Their application in PoC settings or as a consumer technology accessible to every patient appears to be a real possibility. Research in this field is expected to intensify in the coming years. Technological advancements in sensors and biosensors are anticipated to enable the continuous real-time analysis of various omics fields, fostering early disease detection and intervention strategies. The integration of AI with digital health platforms would enable predictive analysis and personalized healthcare, emphasizing the importance of interdisciplinary collaboration in related scientific fields.

An implantable device for wireless monitoring of diverse physio-behavioral characteristics in freely behaving small animals and interacting groups

Comprehensive, continuous quantitative monitoring of intricately orchestrated physiological processes and behavioral states in living organisms can yield essential data for elucidating the function of neural circuits under healthy and diseased conditions, for defining the effects of potential drugs and treatments, and for tracking disease progression and recovery. Here, we report a wireless, battery-free implantable device and a set of associated algorithms that enable continuous, multiparametric physio-behavioral monitoring in freely behaving small animals and interacting groups. Through advanced analytics approaches applied to mechano-acoustic signals of diverse body processes, the device yields heart rate, respiratory rate, physical activity, temperature, and behavioral states. Demonstrations in pharmacological, locomotor, and acute and social stress tests and in optogenetic studies offer unique insights into the coordination of physio-behavioral characteristics associated with healthy and perturbed states. This technology has broad utility in neuroscience, physiology, behavior, and other areas that rely on studies of freely moving, small animal models.

Biomimetic Materials for Skin Tissue Regeneration and Electronic Skin

Biomimetic materials have become a promising alternative in the field of tissue engineering and regenerative medicine to address critical challenges in wound healing and skin regeneration. Skin-mimetic materials have enormous potential to improve wound healing outcomes and enable innovative diagnostic and sensor applications. Human skin, with its complex structure and diverse functions, serves as an excellent model for designing biomaterials. Creating effective wound coverings requires mimicking the unique extracellular matrix composition, mechanical properties, and biochemical cues. Additionally, integrating electronic functionality into these materials presents exciting possibilities for real-time monitoring, diagnostics, and personalized healthcare. This review examines biomimetic skin materials and their role in regenerative wound healing, as well as their integration with electronic skin technologies. It discusses recent advances, challenges, and future directions in this rapidly evolving field.

Use of artificial intelligence to develop predictive algorithms of cough and PCR-confirmed COVID-19 infections based on inputs from clinical-grade wearable sensors

There have been over 769 million cases of COVID-19, and up to 50% of infected individuals are asymptomatic. The purpose of this study aimed to assess the use of a clinical-grade physiological wearable monitoring system, ANNE One, to develop an artificial intelligence algorithm for (1) cough detection and (2) early detection of COVID-19, through the retrospective analysis of prospectively collected physiological data from longitudinal wear of ANNE sensors in a multicenter single arm study of subjects at high risk for COVID-19 due to occupational or home exposures. The study employed a two-fold approach: cough detection algorithm development and COVID-19 detection algorithm development. For cough detection, healthy individuals wore an ANNE One chest sensor during scripted activity. The final performance of the algorithm achieved an F-1 score of 83.3% in twenty-seven healthy subjects during biomarker validation. In the COVID-19 detection algorithm, individuals at high-risk for developing COVID-19 because of recent exposures received ANNE One sensors and completed daily symptom surveys. An algorithm analyzing vital parameters (heart rate, respiratory rate, cough count, etc.) for early COVID-19 detection was developed. The COVID-19 detection algorithm exhibited a sensitivity of 0.47 and specificity of 0.72 for detecting COVID-19 in 325 individuals with recent exposures. Participants demonstrated high adherence (≥ 4 days of wear per week). ANNE One shows promise for detection of COVID-19. Inclusion of respiratory biomarkers (e.g., cough count) enhanced the algorithm's predictive ability. These findings highlight the potential value of wearable devices in early disease detection and monitoring.

Joint modeling of longitudinal and competing risks for assessing blood oxygen saturation and its association with survival outcomes in COVID-19 patients

BACKGROUND

The objective of the present study is to evaluate the association between longitudinal and survival outcomes in the presence of competing risk events. To illustrate the application of joint modeling in clinical research, we assessed the blood oxygen saturation (SPO2) and its association with survival outcomes in coronavirus disease (COVID-19).

MATERIALS AND METHODS

In this prospective cohort study, we followed 300 COVID-19 patients, who were diagnosed with severe COVID-19 in the Rohani Hospital in Babol, the north of Iran from October 22, 2020 to March 5, 2021, where death was the event of interest, surviving was the competing risk event and SPO2 was the longitudinal outcome. Joint modeling analyses were compared to separate analyses for these data.

RESULT

The estimation of the association parameter in the joint modeling verified the association between longitudinal outcome SPO2 with survival outcome of death (Hazard Ratio (HR) = 0.33, P = 0.001) and the competing risk outcome of surviving (HR = 4.18, P < 0.001). Based on the joint modeling, longitudinal outcome (SPO2) decreased in hypertension patients (β = -0.28, P = 0.581) and increased in those with a high level of SPO2 on admission (β = 0.75, P = 0.03). Also, in the survival submodel in the joint model, the risk of death survival outcome increased in patients with diabetes comorbidity (HR = 4.38, P = 0.026).

CONCLUSION

The association between longitudinal measurements of SPO2 and survival outcomes of COVID-19 confirms that SPO2 is an important indicator in this disease. Thus, the application of this joint model can provide useful clinical evidence in the different areas of medical sciences.

Flexible Antibacterial Respiratory Monitoring Sensor Based on Controllable Au-Modified Surface of Highly {001} Preferred Anatase Titanium Dioxide Thin Film

Respiratory signals are critical clinical diagnostic criteria for respiratory diseases and health conditions, and respiratory sensors play a crucial role in achieving the desired respiratory monitoring effect. High sensitivity to a single factor can improve the reliability of respiratory monitoring, and maintaining the hygiene of the sensors is also important for daily health monitoring. Herein, we propose a flexible Au-modified anatase titanium dioxide resistive respiratory sensor, which can be mechanically compliantly attached to curved surfaces for respiratory monitoring in different modalities (i.e., respiratory intensity, frequency, and rate). The uniform and preferentially oriented anatase titanium dioxide films gained by the polymer-assisted deposition technique can be fabricated on flexible substrates through a liquid-assisted transferring process. The Au modification can enhance surface plasmon resonance to facilitate the photocatalytic activity of titanium dioxide, and the optimized distribution of Au on the surface of titanium dioxide film made the sensor have an excellent antibacterial effect. The uniquely designed encapsulation can effectively control the contact between the surface of titanium dioxide films and electrodes, allowing the flexible sensor to exhibit fast response time (0.71 s) and recovery time (1.06 s) to respiratory as well as insensitivity or low sensitivity to other factors (i.e., gas composition, humidity, temperature, stress, and strain). This work provided an effective strategy for flexible wearable respiratory sensors and has great potential in daily respiratory monitoring for health management and pandemic control.

Advances in Respiratory Monitoring: A Comprehensive Review of Wearable and Remote Technologies

This article explores the importance of wearable and remote technologies in healthcare. The focus highlights its potential in continuous monitoring, examines the specificity of the issue, and offers a view of proactive healthcare. Our research describes a wide range of device types and scientific methodologies, starting from traditional chest belts to their modern alternatives and cutting-edge bioamplifiers that distinguish breathing from chest impedance variations. We also investigated innovative technologies such as the monitoring of thorax micromovements based on the principles of seismocardiography, ballistocardiography, remote camera recordings, deployment of integrated optical fibers, or extraction of respiration from cardiovascular variables. Our review is extended to include acoustic methods and breath and blood gas analysis, providing a comprehensive overview of different approaches to respiratory monitoring. The topic of monitoring respiration with wearable and remote electronics is currently the center of attention of researchers, which is also reflected by the growing number of publications. In our manuscript, we offer an overview of the most interesting ones.

RespirAnalyzer: an R package for analyzing data from continuous monitoring of respiratory signals

Motivation

The analysis of data obtained from continuous monitoring of respiratory signals (CMRS) holds significant importance in improving patient care, optimizing sports performance, and advancing scientific understanding in the field of respiratory health.

Results

The R package RespirAnalyzer provides an analytic tool specifically for feature extraction, fractal and complexity analysis for CMRS data. The package covers a wide and comprehensive range of data analysis methods including obtaining inter-breath intervals (IBI) series, plotting time series, obtaining summary statistics of IBI series, conducting power spectral density, multifractal detrended fluctuation analysis (MFDFA) and multiscale sample entropy analysis, fitting the MFDFA results with the extended binomial multifractal model, displaying results using various plots, etc. This package has been developed from our work in directly analyzing CMRS data and is anticipated to assist fellow researchers in computing the related features of their CMRS data, enabling them to delve into the clinical significance inherent in these features.

Availability and implementation

The package for Windows is available from both Comprehensive R Archive Network (CRAN): https://cran.r-project.org/web/packages/RespirAnalyzer/index.html and GitHub: https://github.com/dongxinzheng/RespirAnalyzer.

A Novel Method for Tracking Neck Motions Using a Skin-Conformable Wireless Accelerometer: A Pilot Study

Introduction

Cervical spine disease is a leading cause of pain and disability. Degenerative conditions of the spine can result in neurologic compression of the cervical spinal cord or nerve roots and may be surgically treated with an anterior cervical discectomy and fusion (ACDF) in up to 137,000 people per year in the United States. A common sequelae of ACDF is reduced cervical range of motion (CROM) with patient-based complaints of stiffness and neck pain. Currently, tools for assessment of CROM are manual, subjective, and only intermittently utilized during doctor or physical therapy visits. We propose a skin-mountable acousto-mechanic sensor (ADvanced Acousto-Mechanic sensor; ADAM) as a tool for continuous neck motion monitoring in postoperative ACDF patients. We have developed and validated a machine learning neck motion classification algorithm to differentiate between eight neck motions (right/left rotation, right/left lateral bending, flexion, extension, retraction, protraction) in healthy normal subjects and patients.

Methods

Sensor data from 12 healthy normal subjects and 5 patients were used to develop and validate a Convolutional Neural Network (CNN).

Results

An average algorithm accuracy of 80.0 ± 3.8% was obtained for healthy normal subjects (94% for right rotation, 98% for left rotation, 65% for right lateral bending, 87% for left lateral bending, 89% for flexion, 77% for extension, 50% for retraction, 84% for protraction). An average accuracy of 67.5 ± 5.8% was obtained for patients.

Discussion

ADAM, with our algorithm, may serve as a rehabilitation tool for neck motion monitoring in postoperative ACDF patients. Sensor-captured vital signs and other events (extubation, vocalization, physical therapy, walking) are potential metrics to be incorporated into our algorithm to offer more holistic monitoring of patients after cervical spine surgery.

Recent advances in point-of-care testing of COVID-19

Advances in microfluidic device miniaturization and system integration contribute to the development of portable, handheld, and smartphone-compatible devices. These advancements in diagnostics have the potential to revolutionize the approach to detect and respond to future pandemics. Accordingly, herein, recent advances in point-of-care testing (POCT) of coronavirus disease 2019 (COVID-19) using various microdevices, including lateral flow assay strips, vertical flow assay strips, microfluidic channels, and paper-based microfluidic devices, are reviewed. However, visual determination of the diagnostic results using only microdevices leads to many false-negative results due to the limited detection sensitivities of these devices. Several POCT systems comprising microdevices integrated with portable optical readers have been developed to address this issue. Since the outbreak of COVID-19, effective POCT strategies for COVID-19 based on optical detection methods have been established. They can be categorized into fluorescence, surface-enhanced Raman scattering, surface plasmon resonance spectroscopy, and wearable sensing. We introduced next-generation pandemic sensing methods incorporating artificial intelligence that can be used to meet global health needs in the future. Additionally, we have discussed appropriate responses of various testing devices to emerging infectious diseases and prospective preventive measures for the post-pandemic era. We believe that this review will be helpful for preparing for future infectious disease outbreaks.

Artificial Intelligence-Powered Electronic Skin

Skin-interfaced electronics is gradually changing medical practices by enabling continuous and noninvasive tracking of physiological and biochemical information. With the rise of big data and digital medicine, next-generation electronic skin (e-skin) will be able to use artificial intelligence (AI) to optimize its design as well as uncover user-personalized health profiles. Recent multimodal e-skin platforms have already employed machine learning (ML) algorithms for autonomous data analytics. Unfortunately, there is a lack of appropriate AI protocols and guidelines for e-skin devices, resulting in overly complex models and non-reproducible conclusions for simple applications. This review aims to present AI technologies in e-skin hardware and assess their potential for new inspired integrated platform solutions. We outline recent breakthroughs in AI strategies and their applications in engineering e-skins as well as understanding health information collected by e-skins, highlighting the transformative deployment of AI in robotics, prosthetics, virtual reality, and personalized healthcare. We also discuss the challenges and prospects of AI-powered e-skins as well as predictions for the future trajectory of smart e-skins.

A mixed electronic-ionic conductor-based bifunctional sensing layer beyond ionophores for sweat electrolyte monitoring

Noninvasive and continuous monitoring of electrolytes in biofluids based on wearable biotechnology provides extensive health-related physiological information. The state-of-the-art wearable bioelectronic ion sensors depend on the organic ionophore-based solid-contact structure of potentiometric ion-selective electrodes. This structure contains two functional sensing layers, i.e., a solid contact (ion-to-electron signal transduction) and an ionophore-containing ion-selective membrane (ISM, ion recognition). However, the potential drift, biotoxicity, and expensive organic ionophores complicate practical wearable applications. These challenges intrinsically originate from the ISM. Herein, an ISM-free wearable ion sensor based on mixed electronic-ionic conductors of tungsten bronzes is reported. These materials can serve as a bifunctional sensing layer for simultaneous ion-to-electron transduction through the redox reaction of W6+/5+ and ion recognition through crystal ion exchange. The K- and Na-adjusted WO3 disclosed Nernstian responses toward NH4+ and H+, respectively. The selectivity is comparable to or even better than organic ionophores, such as ammonia ionophore of nonactin. Further, the on-body monitoring of sweat ammonia and pH was realized using an integrated ISM-free flexible sensor. Therefore, this work offers an ISM-free concept and emphasizes the importance of developing next-generation ISM-free wearable bioelectronic ion sensors.

Recent advances in wearable sensors and data analytics for continuous monitoring and analysis of biomarkers and symptoms related to COVID-19

The COVID-19 pandemic has changed the lives of many people around the world. Based on the available data and published reports, most people diagnosed with COVID-19 exhibit no or mild symptoms and could be discharged home for self-isolation. Considering that a substantial portion of them will progress to a severe disease requiring hospitalization and medical management, including respiratory and circulatory support in the form of supplemental oxygen therapy, mechanical ventilation, vasopressors, etc. The continuous monitoring of patient conditions at home for patients with COVID-19 will allow early determination of disease severity and medical intervention to reduce morbidity and mortality. In addition, this will allow early and safe hospital discharge and free hospital beds for patients who are in need of admission. In this review, we focus on the recent developments in next-generation wearable sensors capable of continuous monitoring of disease symptoms, particularly those associated with COVID-19. These include wearable non/minimally invasive biophysical (temperature, respiratory rate, oxygen saturation, heart rate, and heart rate variability) and biochemical (cytokines, cortisol, and electrolytes) sensors, sensor data analytics, and machine learning-enabled early detection and medical intervention techniques. Together, we aim to inspire the future development of wearable sensors integrated with data analytics, which serve as a foundation for disease diagnostics, health monitoring and predictions, and medical interventions.

Skin-interfaced electronics: A promising and intelligent paradigm for personalized healthcare

Skin-interfaced electronics (skintronics) have received considerable attention due to their thinness, skin-like mechanical softness, excellent conformability, and multifunctional integration. Current advancements in skintronics have enabled health monitoring and digital medicine. Particularly, skintronics offer a personalized platform for early-stage disease diagnosis and treatment. In this comprehensive review, we discuss (1) the state-of-the-art skintronic devices, (2) material selections and platform considerations of future skintronics toward intelligent healthcare, (3) device fabrication and system integrations of skintronics, (4) an overview of the skintronic platform for personalized healthcare applications, including biosensing as well as wound healing, sleep monitoring, the assessment of SARS-CoV-2, and the augmented reality-/virtual reality-enhanced human-machine interfaces, and (5) current challenges and future opportunities of skintronics and their potentials in clinical translation and commercialization. The field of skintronics will not only minimize physical and physiological mismatches with the skin but also shift the paradigm in intelligent and personalized healthcare and offer unprecedented promise to revolutionize conventional medical practices.

Explainable discovery of disease biomarkers: The case of ovarian cancer to illustrate the best practice in machine learning and Shapley analysis

OBJECTIVE

Ovarian cancer is a significant health issue with lasting impacts on the community. Despite recent advances in surgical, chemotherapeutic and radiotherapeutic interventions, they have had only marginal impacts due to an inability to identify biomarkers at an early stage. Biomarker discovery is challenging, yet essential for improving drug discovery and clinical care. Machine learning (ML) techniques are invaluable for recognising complex patterns in biomarkers compared to conventional methods, yet they can lack physical insights into diagnosis. eXplainable Artificial Intelligence (XAI) is capable of providing deeper insights into the decision-making of complex ML algorithms increasing their applicability. We aim to introduce best practice for combining ML and XAI techniques for biomarker validation tasks.

METHODS

We focused on classification tasks and a game theoretic approach based on Shapley values to build and evaluate models and visualise results. We described the workflow and apply the pipeline in a case study using the CDAS PLCO Ovarian Biomarkers dataset to demonstrate the potential for accuracy and utility.

RESULTS

The case study results demonstrate the efficacy of the ML pipeline, its consistency, and advantages compared to conventional statistical approaches.

CONCLUSION

The resulting guidelines provide a general framework for practical application of XAI in medical research that can inform clinicians and validate and explain cancer biomarkers.

Technology Roadmap for Flexible Sensors

Humans rely increasingly on sensors to address grand challenges and to improve quality of life in the era of digitalization and big data. For ubiquitous sensing, flexible sensors are developed to overcome the limitations of conventional rigid counterparts. Despite rapid advancement in bench-side research over the last decade, the market adoption of flexible sensors remains limited. To ease and to expedite their deployment, here, we identify bottlenecks hindering the maturation of flexible sensors and propose promising solutions. We first analyze challenges in achieving satisfactory sensing performance for real-world applications and then summarize issues in compatible sensor-biology interfaces, followed by brief discussions on powering and connecting sensor networks. Issues en route to commercialization and for sustainable growth of the sector are also analyzed, highlighting environmental concerns and emphasizing nontechnical issues such as business, regulatory, and ethical considerations. Additionally, we look at future intelligent flexible sensors. In proposing a comprehensive roadmap, we hope to steer research efforts towards common goals and to guide coordinated development strategies from disparate communities. Through such collaborative efforts, scientific breakthroughs can be made sooner and capitalized for the betterment of humanity.

Soft Electronics for Health Monitoring Assisted by Machine Learning

Due to the development of the novel materials, the past two decades have witnessed the rapid advances of soft electronics. The soft electronics have huge potential in the physical sign monitoring and health care. One of the important advantages of soft electronics is forming good interface with skin, which can increase the user scale and improve the signal quality. Therefore, it is easy to build the specific dataset, which is important to improve the performance of machine learning algorithm. At the same time, with the assistance of machine learning algorithm, the soft electronics have become more and more intelligent to realize real-time analysis and diagnosis. The soft electronics and machining learning algorithms complement each other very well. It is indubitable that the soft electronics will bring us to a healthier and more intelligent world in the near future. Therefore, in this review, we will give a careful introduction about the new soft material, physiological signal detected by soft devices, and the soft devices assisted by machine learning algorithm. Some soft materials will be discussed such as two-dimensional material, carbon nanotube, nanowire, nanomesh, and hydrogel. Then, soft sensors will be discussed according to the physiological signal types (pulse, respiration, human motion, intraocular pressure, phonation, etc.). After that, the soft electronics assisted by various algorithms will be reviewed, including some classical algorithms and powerful neural network algorithms. Especially, the soft device assisted by neural network will be introduced carefully. Finally, the outlook, challenge, and conclusion of soft system powered by machine learning algorithm will be discussed.

Responsive materials and mechanisms as thermal safety systems for skin-interfaced electronic devices

Soft, wireless physiological sensors that gently adhere to the skin are capable of continuous clinical-grade health monitoring in hospital and/or home settings, of particular value to critically ill infants and other vulnerable patients, but they present risks for injury upon thermal failure. This paper introduces an active materials approach that automatically minimizes such risks, to complement traditional schemes that rely on integrated sensors and electronic control circuits. The strategy exploits thin, flexible bladders that contain small volumes of liquid with boiling points a few degrees above body temperature. When the heat exceeds the safe range, vaporization rapidly forms highly effective, thermally insulating structures and delaminates the device from the skin, thereby eliminating any danger to the skin. Experimental and computational thermomechanical studies and demonstrations in a skin-interfaced mechano-acoustic sensor illustrate the effectiveness of this simple thermal safety system and suggest its applicability to nearly any class of skin-integrated device technology.

Detecting Patient Deterioration Early Using Continuous Heart rate and Respiratory rate Measurements in Hospitalized COVID-19 Patients

BACKGROUND

Presenting symptoms of COVID-19 patients are unusual compared with many other illnesses. Blood pressure, heart rate, and respiratory rate may stay within acceptable ranges as the disease progresses. Consequently, intermittent monitoring does not detect deterioration as it is happening. We investigated whether continuously monitoring heart rate and respiratory rate enables earlier detection of deterioration compared with intermittent monitoring, or introduces any risks.

METHODS

When available, patients admitted to a COVID-19 ward received a wireless wearable sensor which continuously measured heart rate and respiratory rate. Two intensive care unit (ICU) physicians independently assessed sensor data, indicating when an intervention might be necessary (alarms). A third ICU physician independently extracted clinical events from the electronic medical record (EMR events). The primary outcome was the number of true alarms. Secondary outcomes included the time difference between true alarms and EMR events, interrater agreement for the alarms, and severity of EMR events that were not detected.

RESULTS

In clinical practice, 48 (EMR) events occurred. None of the 4 ICU admissions were detected with the sensor. Of the 62 sensor events, 13 were true alarms (also EMR events). Of these, two were related to rapid response team calls. The true alarms were detected 39 min (SD = 113) before EMR events, on average. Interrater agreement was 10%. Severity of the 38 non-detected events was similar to the severity of 10 detected events.

CONCLUSION

Continuously monitoring heart rate and respiratory rate does not reliably detect deterioration in COVID-19 patients when assessed by ICU physicians.

Multi-Modal Point-of-Care Diagnostics for COVID-19 Based on Acoustics and Symptoms

BACKGROUND

The COVID-19 pandemic has highlighted the need to invent alternative respiratory health diagnosis methodologies which provide improvement with respect to time, cost, physical distancing and detection performance. In this context, identifying acoustic bio-markers of respiratory diseases has received renewed interest.

OBJECTIVE

In this paper, we aim to design COVID-19 diagnostics based on analyzing the acoustics and symptoms data. Towards this, the data is composed of cough, breathing, and speech signals, and health symptoms record, collected using a web-application over a period of twenty months.

METHODS

We investigate the use of time-frequency features for acoustic signals and binary features for encoding different health symptoms. We experiment with use of classifiers like logistic regression, support vector machines and long-short term memory (LSTM) network models on the acoustic data, while decision tree models are proposed for the symptoms data.

RESULTS

We show that a multi-modal integration of inference from different acoustic signal categories and symptoms achieves an area-under-curve (AUC) of 96.3%, a statistically significant improvement when compared against any individual modality ([Formula: see text]). Experimentation with different feature representations suggests that the mel-spectrogram acoustic features performs relatively better across the three kinds of acoustic signals. Further, a score analysis with data recorded from newer SARS-CoV-2 variants highlights the generalization ability of the proposed diagnostic approach for COVID-19 detection.

CONCLUSION

The proposed method shows a promising direction for COVID-19 detection using a multi-modal dataset, while generalizing to new COVID variants.

Wearable technology in the sports medicine clinic to guide the return-to-play and performance protocols of athletes following a COVID-19 diagnosis

The coronavirus disease 2019 (COVID-19) pandemic has enabled the adoption of digital health platforms for self-monitoring and diagnosis. Notably, the pandemic has had profound effects on athletes and their ability to train and compete. Sporting organizations worldwide have reported a significant increase in injuries manifesting from changes in training regimens and match schedules resulting from extended quarantines. While current literature focuses on the use of wearable technology to monitor athlete workloads to guide training, there is a lack of literature suggesting how such technology can mediate the return to sport processes of athletes infected with COVID-19. This paper bridges this gap by providing recommendations to guide team physicians and athletic trainers on the utility of wearable technology for improving the well-being of athletes who may be asymptomatic, symptomatic, or tested negative but have had to quarantine due to a close exposure. We start by describing the physiologic changes that occur in athletes infected with COVID-19 with extended deconditioning from a musculoskeletal, psychological, cardiopulmonary, and thermoregulatory standpoint and review the evidence on how these athletes may safely return to play. We highlight opportunities for wearable technology to aid in the return-to-play process by offering a list of key parameters pertinent to the athlete affected by COVID-19. This paper provides the athletic community with a greater understanding of how wearable technology can be implemented in the rehabilitation process of these athletes and spurs opportunities for further innovations in wearables, digital health, and sports medicine to reduce injury burden in athletes of all ages.

A survey of COVID-19 detection and prediction approaches using mobile devices, AI, and telemedicine

Since 2019, the COVID-19 pandemic has had an extremely high impact on all facets of the society and will potentially have an everlasting impact for years to come. In response to this, over the past years, there have been a significant number of research efforts on exploring approaches to combat COVID-19. In this paper, we present a survey of the current research efforts on using mobile Internet of Thing (IoT) devices, Artificial Intelligence (AI), and telemedicine for COVID-19 detection and prediction. We first present the background and then present current research in this field. Specifically, we present the research on COVID-19 monitoring and detection, contact tracing, machine learning based approaches, telemedicine, and security. We finally discuss the challenges and the future work that lay ahead in this field before concluding this paper.

Pregnancy outcomes in infertility patients diagnosed with sleep disordered breathing with wireless wearable sensors

OBJECTIVE

To study the feasibility of home-based assessment of sleep disordered breathing (SDB) on early pregnancy success after in vitro fertilization with novel wearable sensors.

DESIGN

Prospective observational study.

SETTING

Patients 18 to 45 years old undergoing autologous IVF at an academic infertility center.

PATIENTS

30 women (24-44 years old) INTERVENTION: Participants provided medical history, completed sleep surveys, and a single night of home sleep monitoring prior to IVF with a novel, FDA-cleared wireless sensor system (ANNE® Sleep, Sibel Health), to collect continuous measurements of heart rate, respiratory rate, pulse oxygenation, respiratory effort/snoring, peripheral arterial tonometry, pulse arrival time, and pulse transit time, an accepted surrogate of continuous blood pressure generated by pulse arrival time and pulse transit time. Sleep nights were reviewed to derive the apnea hypopnea index (AHI), defined as the average number of apnea or hypopnea events per hour. An AHI of greater than or equal to 5 events/hour was considered abnormal.

MAIN OUTCOME MEASURE

Rate of clinical pregnancy (defined as intrauterine gestational sac with a yolk sac) after IVF. Logistic regression models were used to estimate the unadjusted and adjusted odds ratio.

RESULTS

The overall rate of sleep disordered breathing of any severity was 57%. Participants with SDB had a mean AHI of 13.4 compared to 2.7 events/hr (p<0.01), were younger, and more likely to have polycystic ovary syndrome. Of the 29 patients undergoing an embryo transfer, clinical pregnancy and livebirth occurred in 35% of women with SDB compared to 58% without SDB (p = 0.22). After adjusting for age, SDB reduced pregnancy rates but was not statistically significant (aOR 0.23, 95% CI: 0.04-1.5, p = 0.12). Though polycystic ovary syndrome was associated with higher rates of SDB it was not independently associated with lower pregnancy rates.

CONCLUSION

Screening for sleep disordered breathing using home-based wireless, wearable sensors was well accepted and easily performed by infertile patients in this cohort. Sleep disordered breathing of any severity was associated with an 77% (95% CI: 0.08-1.8) lower likelihood of clinical pregnancy and live birth independent of underlying diagnosis. Future larger studies will be needed to understand the role of sleep disordered breathing and IVF outcomes.

Discovery and Analytical Validation of a Vocal Biomarker to Monitor Anosmia and Ageusia in Patients With COVID-19: Cross-sectional Study

Background

The COVID-19 disease has multiple symptoms, with anosmia and ageusia being the most prevalent, varying from 75% to 95% and from 50% to 80% of infected patients, respectively. An automatic assessment tool for these symptoms will help monitor the disease in a fast and noninvasive manner.

Objective

We hypothesized that people with COVID-19 experiencing anosmia and ageusia had different voice features than those without such symptoms. Our objective was to develop an artificial intelligence pipeline to identify and internally validate a vocal biomarker of these symptoms for remotely monitoring them.

Methods

This study used population-based data. Participants were assessed daily through a web-based questionnaire and asked to register 2 different types of voice recordings. They were adults (aged >18 years) who were confirmed by a polymerase chain reaction test to be positive for COVID-19 in Luxembourg and met the inclusion criteria. Statistical methods such as recursive feature elimination for dimensionality reduction, multiple statistical learning methods, and hypothesis tests were used throughout this study. The TRIPOD (Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis) Prediction Model Development checklist was used to structure the research.

Results

This study included 259 participants. Younger (aged <35 years) and female participants showed higher rates of ageusia and anosmia. Participants were aged 41 (SD 13) years on average, and the data set was balanced for sex (female: 134/259, 51.7%; male: 125/259, 48.3%). The analyzed symptom was present in 94 (36.3%) out of 259 participants and in 450 (27.5%) out of 1636 audio recordings. In all, 2 machine learning models were built, one for Android and one for iOS devices, and both had high accuracy—88% for Android and 85% for iOS. The final biomarker was then calculated using these models and internally validated.

Conclusions

This study demonstrates that people with COVID-19 who have anosmia and ageusia have different voice features from those without these symptoms. Upon further validation, these vocal biomarkers could be nested in digital devices to improve symptom assessment in clinical practice and enhance the telemonitoring of COVID-19–related symptoms.

Trial Registration

Clinicaltrials.gov NCT04380987; https://clinicaltrials.gov/ct2/show/NCT04380987

A wearable electrochemical biosensor for the monitoring of metabolites and nutrients

Wearable non-invasive biosensors for the continuous monitoring of metabolites in sweat can detect a few analytes at sufficiently high concentrations, typically during vigorous exercise so as to generate sufficient quantity of the biofluid. Here we report the design and performance of a wearable electrochemical biosensor for the continuous analysis, in sweat during physical exercise and at rest, of trace levels of multiple metabolites and nutrients, including all essential amino acids and vitamins. The biosensor consists of graphene electrodes that can be repeatedly regenerated in situ, functionalized with metabolite-specific antibody-like molecularly imprinted polymers and redox-active reporter nanoparticles, and integrated with modules for iontophoresis-based sweat induction, microfluidic sweat sampling, signal processing and calibration, and wireless communication. In volunteers, the biosensor enabled the real-time monitoring of the intake of amino acids and their levels during physical exercise, as well as the assessment of the risk of metabolic syndrome (by correlating amino acid levels in serum and sweat). The monitoring of metabolites for the early identification of abnormal health conditions could facilitate applications in precision nutrition.

Wearable respiratory sensors for COVID-19 monitoring

Since its outbreak in 2019, COVID-19 becomes a pandemic, severely burdening the public healthcare systems and causing an economic burden. Thus, societies around the world are prioritizing a return to normal. However, fighting the recession could rekindle the pandemic owing to the lightning-fast transmission rate of SARS-CoV-2. Furthermore, many of those who are infected remain asymptomatic for several days, leading to the increased possibility of unintended transmission of the virus. Thus, developing rigorous and universal testing technologies to continuously detect COVID-19 for entire populations remains a critical challenge that needs to be overcome. Wearable respiratory sensors can monitor biomechanical signals such as the abnormities in respiratory rate and cough frequency caused by COVID-19, as well as biochemical signals such as viral biomarkers from exhaled breaths. The point-of-care system enabled by advanced respiratory sensors is expected to promote better control of the pandemic by providing an accessible, continuous, widespread, noninvasive, and reliable solution for COVID-19 diagnosis, monitoring, and management.

Sensing Devices for Detecting and Processing Acoustic Signals in Healthcare

Acoustic signals are important markers to monitor physiological and pathological conditions, e.g., heart and respiratory sounds. The employment of traditional devices, such as stethoscopes, has been progressively superseded by new miniaturized devices, usually identified as microelectromechanical systems (MEMS). These tools are able to better detect the vibrational content of acoustic signals in order to provide a more reliable description of their features (e.g., amplitude, frequency bandwidth). Starting from the description of the structure and working principles of MEMS, we provide a review of their emerging applications in the healthcare field, discussing the advantages and limitations of each framework. Finally, we deliver a discussion on the lessons learned from the literature, and the open questions and challenges in the field that the scientific community must address in the near future.

Soft skin-interfaced mechano-acoustic sensors for real-time monitoring and patient feedback on respiratory and swallowing biomechanics

Swallowing is a complex neuromuscular activity regulated by the autonomic nervous system. Millions of adults suffer from dysphagia (impaired or difficulty swallowing), including patients with neurological disorders, head and neck cancer, gastrointestinal diseases, and respiratory disorders. Therapeutic treatments for dysphagia include interventions by speech-language pathologists designed to improve the physiology of the swallowing mechanism by training patients to initiate swallows with sufficient frequency and during the expiratory phase of the breathing cycle. These therapeutic treatments require bulky, expensive equipment to synchronously record swallows and respirations, confined to use in clinical settings. This paper introduces a wireless, wearable technology that enables continuous, mechanoacoustic tracking of respiratory activities and swallows through movements and vibratory processes monitored at the skin surface. Validation studies in healthy adults (n = 67) and patients with dysphagia (n = 4) establish measurement equivalency to existing clinical standard equipment. Additional studies using a differential mode of operation reveal similar performance even during routine daily activities and vigorous exercise. A graphical user interface with real-time data analytics and a separate, optional wireless module support both visual and haptic forms of feedback to facilitate the treatment of patients with dysphagia.

Measuring Health-Related Quality of Life With Multimodal Data: Viewpoint

The ability to objectively measure aspects of performance and behavior is a fundamental pillar of digital health, enabling digital wellness products, decentralized trial concepts, evidence generation, digital therapeutics, and more. Emerging multimodal technologies capable of measuring several modalities simultaneously and efforts to integrate inputs across several sources are further expanding the limits of what digital measures can assess. Experts from the field of digital health were convened as part of a multi-stakeholder workshop to examine the progress of multimodal digital measures in two key areas: detection of disease and the measurement of meaningful aspects of health relevant to the quality of life. Here we present a meeting report, summarizing key discussion points, relevant literature, and finally a vision for the immediate future, including how multimodal measures can provide value to stakeholders across drug development and care delivery, as well as three key areas where headway will need to be made if we are to continue to build on the encouraging progress so far: collaboration and data sharing, removal of barriers to data integration, and alignment around robust modular evaluation of new measurement capabilities.

The performance of wearable sensors in the detection of SARS-CoV-2 infection: a systematic review

Containing the COVID-19 pandemic requires rapidly identifying infected individuals. Subtle changes in physiological parameters (such as heart rate, respiratory rate, and skin temperature), discernible by wearable devices, could act as early digital biomarkers of infections. Our primary objective was to assess the performance of statistical and algorithmic models using data from wearable devices to detect deviations compatible with a SARS-CoV-2 infection. We searched MEDLINE, Embase, Web of Science, the Cochrane Central Register of Controlled Trials (known as CENTRAL), International Clinical Trials Registry Platform, and ClinicalTrials.gov on July 27, 2021 for publications, preprints, and study protocols describing the use of wearable devices to identify a SARS-CoV-2 infection. Of 3196 records identified and screened, 12 articles and 12 study protocols were analysed. Most included articles had a moderate risk of bias, as per the National Institute of Health Quality Assessment Tool for Observational and Cross-Sectional Studies. The accuracy of algorithmic models to detect SARS-CoV-2 infection varied greatly (area under the curve 0·52-0·92). An algorithm's ability to detect presymptomatic infection varied greatly (from 20% to 88% of cases), from 14 days to 1 day before symptom onset. Increased heart rate was most frequently associated with SARS-CoV-2 infection, along with increased skin temperature and respiratory rate. All 12 protocols described prospective studies that had yet to be completed or to publish their results, including two randomised controlled trials. The evidence surrounding wearable devices in the early detection of SARS-CoV-2 infection is still in an early stage, with a limited overall number of studies identified. However, these studies show promise for the early detection of SARS-CoV-2 infection. Large prospective, and preferably controlled, studies recruiting and retaining larger and more diverse populations are needed to provide further evidence.

Development of nano- and microdevices for the next generation of biotechnology, wearables and miniaturized instrumentation

This is a short communication based on recent high-impact publications related to how various chemical materials and substrate modifications could be tuned for nano- and microdevices, where their application for high point-of-care bioanalysis and further applications in life science is discussed. Hence, they have allowed different high-impact research topics in a variety of fields, from the control of nanoscale to functional microarchitectures embedded in various support materials to obtain a device for a given application or use. Thus, their incorporation in standard instrumentation is shown, as well as in new optical setups to record different classical and non-classical light, signaling, and energy modes at a variety of wavelengths and energy levels. Moreover, the development of miniaturized instrumentation was also contemplated. In order to develop these different levels of technology, the chemistry, physics and engineering of materials were discussed. In this manner, a number of subjects that allowed the design and manufacture of devices could be found. The following could be mentioned by way of example: (i) nanophotonics; (ii) design, synthesis and tuning of advanced nanomaterials; (iii) classical and non-classical light generation within the near field; (iv) microfluidics and nanofluidics; (v) signal waveguiding; (vi) quantum-, nano- and microcircuits; (vii) materials for nano- and microplatforms, and support substrates and their respective modifications for targeted functionalities. Moreover, nano-optics in in-flow devices and chips for biosensing were discussed, and perspectives on biosensing and single molecule detection (SMD) applications. In this perspective, new insights about precision nanomedicine based on genomics and drug delivery systems were obtained, incorporating new advanced diagnosis methods based on lab-on-particles, labs-on-a-chip, gene therapies, implantable devices, portable miniaturized instrumentation, single molecule detection for biophotonics, and neurophotonics. In this manner, this communication intends to highlight recent reports and developments of nano- and microdevices and further approaches towards the incorporation of developments in nanophotonics and biophotonics in the design of new materials based on different strategies and enhanced techniques and methods. Recent proofs of concept are discussed that could allow new substrates for device manufacturing. Thus, physical phenomena and materials chemistry with accurate control within the nanoscale were introduced into the discussion. In this manner, new potential sources of ideas and strategies for the next generation of technology in many research and development fields are showcased.

State-of-the-art Deep Learning Methods on Electrocardiogram Data: A Systematic Review (Preprint)

Background

Electrocardiogram (ECG) is one of the most common noninvasive diagnostic tools that can provide useful information regarding a patient’s health status. Deep learning (DL) is an area of intense exploration that leads the way in most attempts to create powerful diagnostic models based on physiological signals.

Objective

This study aimed to provide a systematic review of DL methods applied to ECG data for various clinical applications.

Methods

The PubMed search engine was systematically searched by combining “deep learning” and keywords such as “ecg,” “ekg,” “electrocardiogram,” “electrocardiography,” and “electrocardiology.” Irrelevant articles were excluded from the study after screening titles and abstracts, and the remaining articles were further reviewed. The reasons for article exclusion were manuscripts written in any language other than English, absence of ECG data or DL methods involved in the study, and absence of a quantitative evaluation of the proposed approaches.

Results

We identified 230 relevant articles published between January 2020 and December 2021 and grouped them into 6 distinct medical applications, namely, blood pressure estimation, cardiovascular disease diagnosis, ECG analysis, biometric recognition, sleep analysis, and other clinical analyses. We provide a complete account of the state-of-the-art DL strategies per the field of application, as well as major ECG data sources. We also present open research problems, such as the lack of attempts to address the issue of blood pressure variability in training data sets, and point out potential gaps in the design and implementation of DL models.

Conclusions

We expect that this review will provide insights into state-of-the-art DL methods applied to ECG data and point to future directions for research on DL to create robust models that can assist medical experts in clinical decision-making.

Artificial Intelligence in Endoscopy

Background

Owing to their rapid development, artificial intelligence (AI) technologies offer a great promise for gastroenterology practice and research. At present, AI-guided image interpretation has already been used with success for endoscopic detection of early malignant lesions. Nonetheless, there are complex challenges and possible shortcomings that must be considered before full implementation can be realized.

Summary

In this review, the current status of AI in endoscopy is summarized. Future perspectives and open questions for further studies are stressed.

Key Messages

The usage of AI algorithms for polyp detection in screening colonoscopy results in a significant increase in the adenoma detection rate, mainly attributed to the identification of diminutive polyps. Computer-aided characterization of colorectal polyps accompanies the detection, but further studies are needed to evaluate the clinical benefit. In contrast to colonoscopy, usage of AI in gastroscopy is currently rather limited. Regarding other fields of endoscopic imaging, capsule endoscopy is the ideal imaging platform for AI, due to the potential of saving time in the video analysis.

Wireless Networking-Driven Healthcare Approaches in Combating COVID-19

Since its outbreak, the coronavirus (COVID-19) pandemic has caused havoc on people's lives. All activities were paused due to the virus's spread across the continents. Researchers have been working hard to find new medication treatments for the COVID-19 pandemic. The World Health Organization (WHO) recommends that safety and self-measures play a major role in preventing the virus from spreading from one person to another. Wireless technology is playing a critical role in avoiding viral propagation. This technology mainly comprises of portable devices that assist self-isolated patients in adhering to safe precautionary measures. Government officials are currently using wireless technologies to identify infected people at large gatherings. In this research, we gave an overview of wireless technologies that assisted the general public and healthcare professionals in maintaining effective healthcare services during COVID-19. We also discussed the possible challenges faced by them for effective implementation in day-to-day life. In conclusion, wireless technologies are one of the best techniques in today's age to effectively combat the pandemic.

Bitter Flavored, Soft Composites for Wearables Designed to Reduce Risks of Choking in Infants

Wireless, skin-integrated devices for continuous, clinical-quality monitoring of vital signs have the potential to greatly improve the care of patients in neonatal and pediatric intensive-care units. These same technologies can also be used in the home, across a broad spectrum of ages, from beginning to end of life. Although miniaturized forms of such devices minimize patient burden and improve compliance, they represent life-threatening choking hazards for infants. A materials strategy is presented here to address this concern. Specifically, composite materials are introduced as soft encapsulating layers and gentle adhesives that release chemical compounds designed to elicit an intense bitter taste when placed in the mouth. Reflexive reactions to this sensation strongly reduce the potential for ingestion, as a safety feature. The materials systems described involve a non-toxic bitterant (denatonium benzoate) as a dopant in an elastomeric (poly(dimethylsiloxane)) or hydrogel matrix. Experimental and computational studies of these composite materials and the kinetics of release of the bitterant define the key properties. Incorporation into various wireless skin-integrated sensors demonstrates their utility in functional systems. This simple strategy offers valuable protective capabilities, with broad practical relevance to the welfare of children monitored with wearable devices.