IEEE Journal of Translational Engineering in Health and Medicine-Jtehm最新文献

{"title":"Applying Machine Learning and Point-Set Registration to Automatically Measure the Severity of Spinal Curvature on Radiographs","authors":"Jason Wong;Marek Reformat;Edmond Lou","doi":"10.1109/JTEHM.2023.3332618","DOIUrl":"10.1109/JTEHM.2023.3332618","url":null,"abstract":"Objective: Measuring the severity of the lateral spinal curvature, or Cobb angle, is critical for monitoring and making treatment decisions for children with adolescent idiopathic scoliosis (AIS). However, manual measurement is time-consuming and subject to human error. Therefore, clinicians seek an automated measurement method to streamline workflow and improve accuracy. This paper reports on a novel machine learning algorithm of cascaded convolutional neural networks (CNN) to measure the Cobb angle on spinal radiographs automatically. Methods: The developed method consisted of spinal column segmentation using a CNN, vertebra localization and segmentation using iterative vertebra body location coupled with another CNN, point-set registration to correct vertebra segmentations, and Cobb angle measurement using the final segmentations. Measurement performance was evaluated with the circular mean absolute error (CMAE) and percentage within clinical acceptance (\u0000<inline-formula> <tex-math>$le 5^{circ }$ </tex-math></inline-formula>\u0000) between automatic and manual measurements. Analysis was separated by curve severity to identify any potential systematic biases using independent samples Student’s t-tests. Results: The method detected 346 of the 352 manually measured Cobb angles (98%), with a CMAE of 2.8° and 91% of measurements within the 5° clinical acceptance. No statistically significant differences were found between the CMAEs of mild (\u0000<inline-formula> <tex-math>$ &lt; 25^{circ }$ </tex-math></inline-formula>\u0000), moderate (25°-45°), and severe (\u0000<inline-formula> <tex-math>$ge 45^{circ }$ </tex-math></inline-formula>\u0000) groups. The average measurement time per radiograph was 17.7±10.2s, improving upon the estimated average of 30s it takes an experienced rater to measure. Additionally, the algorithm outputs segmentations with the measurement, allowing clinicians to interpret measurement results. Discussion/Conclusion: The developed method measured Cobb angles on radiographs automatically with high accuracy, quick measurement time, and interpretability, suggesting clinical feasibility.","PeriodicalId":54255,"journal":{"name":"IEEE Journal of Translational Engineering in Health and Medicine-Jtehm","volume":"12 ","pages":"151-161"},"PeriodicalIF":3.4,"publicationDate":"2023-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10318103","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135704997","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Registration Sanity Check for AR-guided Surgical Interventions: Experience From Head and Face Surgery","authors":"Sara Condino;Fabrizio Cutolo;Marina Carbone;Laura Cercenelli;Giovanni Badiali;Nicola Montemurro;Vincenzo Ferrari","doi":"10.1109/JTEHM.2023.3332088","DOIUrl":"10.1109/JTEHM.2023.3332088","url":null,"abstract":"Achieving and maintaining proper image registration accuracy is an open challenge of image-guided surgery. This work explores and assesses the efficacy of a registration sanity check method for augmented reality-guided navigation (AR-RSC), based on the visual inspection of virtual 3D models of landmarks. We analyze the AR-RSC sensitivity and specificity by recruiting 36 subjects to assess the registration accuracy of a set of 114 AR images generated from camera images acquired during an AR-guided orthognathic intervention. Translational or rotational errors of known magnitude up to ±1.5 mm/±15.5°, were artificially added to the image set in order to simulate different registration errors. This study analyses the performance of AR-RSC when varying (1) the virtual models selected for misalignment evaluation (e. g., the model of brackets, incisor teeth, and gingival margins in our experiment), (2) the type (translation/rotation) of registration error, and (3) the level of user experience in using AR technologies. Results show that: 1) the sensitivity and specificity of the AR-RSC depends on the virtual models (globally, a median true positive rate of up to 79.2% was reached with brackets, and a median true negative rate of up to 64.3% with incisor teeth), 2) there are error components that are more difficult to identify visually, 3) the level of user experience does not affect the method. In conclusion, the proposed AR-RSC, tested also in the operating room, could represent an efficient method to monitor and optimize the registration accuracy during the intervention, but special attention should be paid to the selection of the AR data chosen for the visual inspection of the registration accuracy.","PeriodicalId":54255,"journal":{"name":"IEEE Journal of Translational Engineering in Health and Medicine-Jtehm","volume":"12 ","pages":"258-267"},"PeriodicalIF":3.4,"publicationDate":"2023-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10315237","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135659363","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Letter to the Editor: “How Can Biomedical Engineers Help Empower Individuals With Intellectual Disabilities? The Potential Benefits and Challenges of AI Technologies to Support Inclusivity and Transform Lives”","authors":"Alessandro Di Nuovo","doi":"10.1109/JTEHM.2023.3331977","DOIUrl":"10.1109/JTEHM.2023.3331977","url":null,"abstract":"The rapid advancement of Artificial Intelligence (AI) is transforming healthcare and daily life, offering great opportunities but also posing ethical and societal challenges. To ensure AI benefits all individuals, including those with intellectual disabilities, the focus should be on adaptive technology that can adapt to the unique needs of the user. Biomedical engineers have an interdisciplinary background that helps them to lead multidisciplinary teams in the development of human-centered AI solutions. These solutions can personalize learning, enhance communication, and improve accessibility for individuals with intellectual disabilities. Furthermore, AI can aid in healthcare research, diagnostics, and therapy. The ethical use of AI in healthcare and the collaboration of AI with human expertise must be emphasized. Public funding for inclusive research is encouraged, promoting equity and economic growth while empowering those with intellectual disabilities in society.","PeriodicalId":54255,"journal":{"name":"IEEE Journal of Translational Engineering in Health and Medicine-Jtehm","volume":"12 ","pages":"256-257"},"PeriodicalIF":3.4,"publicationDate":"2023-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10314515","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135562888","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automated, Vision-Based Goniometry and Range of Motion Calculation in Individuals With Suspected Ehlers-Danlos Syndromes/Generalized Hypermobility Spectrum Disorders: A Comparison of Pose-Estimation Libraries to Goniometric Measurements","authors":"Andrea Sabo;Nimish Mittal;Amol Deshpande;Hance Clarke;Babak Taati","doi":"10.1109/JTEHM.2023.3327691","DOIUrl":"10.1109/JTEHM.2023.3327691","url":null,"abstract":"Generalized joint hypermobility (GJH) often leads clinicians to suspect a diagnosis of Ehlers Danlos Syndrome (EDS), but it can be difficult to objectively assess. Video-based goniometry has been proposed to objectively estimate joint range of motion in hyperextended joints. As part of an exam of joint hypermobility at a specialized EDS clinic, a mobile phone was used to record short videos of 97 adults (89 female, 35.0 ± 9.9 years old) undergoing assessment of the elbows, knees, shoulders, ankles, and fifth fingers. Five body keypoint pose-estimation libraries (AlphaPose, Detectron, MediaPipe-Body, MoveNet – Thunder, OpenPose) and two hand keypoint pose-estimation libraries (AlphaPose, MediaPipe-Hands) were used to geometrically calculate the maximum angle of hyperextension or hyperflexion of each joint. A custom domain-specific model with a MobileNet-v2 backbone finetuned on data collected as part of this study was also evaluated for the fifth finger movement. Spearman’s correlation was used to analyze the angles calculated from the tracked joint positions, the angles calculated from manually annotated keypoints, and the angles measured using a goniometer. Moderate correlations between the angles estimated using pose-tracked keypoints and the goniometer measurements were identified for the elbow (rho =.722; Detectron), knee (rho =.608; MoveNet – Thunder), shoulder (rho =.632; MoveNet – Thunder), and fifth finger (rho =.786; custom model) movements. The angles estimated from keypoints predicted by open-source libraries at the ankles were not significantly correlated with the goniometer measurements. Manually annotated angles at the elbows, knees, shoulders, and fifth fingers were moderately to strongly correlated to goniometer measurements but were weakly correlated for the ankles. There was not one pose-estimation library which performed best across all joints, so the library of choice must be selected separately for each joint of interest. This work evaluates several pose-estimation models as part of a vision-based system for estimating joint angles in individuals with suspected joint hypermobility. Future applications of the proposed system could facilitate objective assessment and screening of individuals referred to specialized EDS clinics.","PeriodicalId":54255,"journal":{"name":"IEEE Journal of Translational Engineering in Health and Medicine-Jtehm","volume":"12 ","pages":"140-150"},"PeriodicalIF":3.4,"publicationDate":"2023-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10309843","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135501586","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"TTN: Topological Transformer Network for Automated Coronary Artery Branch Labeling in Cardiac CT Angiography","authors":"Yuyang Zhang;Gongning Luo;Wei Wang;Shaodong Cao;Suyu Dong;Daren Yu;Xiaoyun Wang;Kuanquan Wang","doi":"10.1109/JTEHM.2023.3329031","DOIUrl":"10.1109/JTEHM.2023.3329031","url":null,"abstract":"Objective: Existing methods for automated coronary artery branch labeling in cardiac CT angiography face two limitations: 1) inability to model overall correlation of branches, since differences between branches cannot be captured directly. 2) a serious class imbalance between main and side branches. Methods and procedures: Inspired by the application of Transformer in sequence data, we propose a topological Transformer network (TTN), which solves the vessel branch labeling from a novel perspective of sequence labeling learning. TTN detects differences between branches by establishing their overall correlation. A topological encoding that represents the positions of vessel segments in the artery tree, is proposed to assist the model in classifying branches. Also, a segment-depth loss is introduced to solve the class imbalance between main and side branches. Results: On a dataset with 325 CCTA, our method obtains the best overall result on all branches, the best result on side branches, and a competitive result on main branches. Conclusion: TTN solves two limitations in existing methods perfectly, thus achieving the best result in coronary artery branch labeling task. It is the first Transformer based vessel branch labeling method and is notably different from previous methods. Clinical impact: This Pre-Clinical Research can be integrated into a computer-aided diagnosis system to generate cardiovascular disease diagnosis report, assisting clinicians in locating the atherosclerotic plaques.","PeriodicalId":54255,"journal":{"name":"IEEE Journal of Translational Engineering in Health and Medicine-Jtehm","volume":"12 ","pages":"129-139"},"PeriodicalIF":3.4,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10304172","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135319101","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Wearable Accelerometer and Gyroscope Sensors for Estimating the Severity of Essential Tremor","authors":"Sheik Mohammed Ali;Sridhar Poosapadi Arjunan;James Peter;Laura Perju-Dumbrava;Catherine Ding;Michael Eller;Sanjay Raghav;Peter Kempster;Mohammod Abdul Motin;P. J. Radcliffe;Dinesh Kant Kumar","doi":"10.1109/JTEHM.2023.3329344","DOIUrl":"10.1109/JTEHM.2023.3329344","url":null,"abstract":"Background: Several validated clinical scales measure the severity of essential tremor (ET). Their assessments are subjective and can depend on familiarity and training with scoring systems. Method: We propose a multi-modal sensing using a wearable inertial measurement unit for estimating scores on the Fahn-Tolosa-Marin tremor rating scale (FTM) and determine the classification accuracy within the tremor type. 17 ET participants and 18 healthy controls were recruited for the study. Two movement disorder neurologists who were blinded to prior clinical information viewed video recordings and scored the FTM. Participants drew a guided Archimedes spiral while wearing an inertial measurement unit placed at the mid-point between the lateral epicondyle of the humerus and the anatomical snuff box. Acceleration and gyroscope recordings were analyzed. The ratio of the power spectral density between frequency bands 0.5-4 Hz and 4–12 Hz, and the sum of power spectrum density over the entire spectrum of 2–74 Hz, for both accelerometer and gyroscope data, were computed. FTM was estimated using regression model and classification using SVM was validated using the leave-one-out method. Results: Regression analysis showed a moderate to good correlation when individual features were used, while correlation was high (\u0000<inline-formula> <tex-math>$r^{2}$ </tex-math></inline-formula>\u0000 = 0.818) when suitable features of the gyro and accelerometer were combined. The accuracy for two-class classification of the combined features using SVM was 91.42% while for four-class it was 68.57%. Conclusion: Potential applications of this novel wearable sensing method using a wearable Inertial Measurement Unit (IMU) include monitoring of ET and clinical trials of new treatments for the disorder.","PeriodicalId":54255,"journal":{"name":"IEEE Journal of Translational Engineering in Health and Medicine-Jtehm","volume":"12 ","pages":"194-203"},"PeriodicalIF":3.4,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10304233","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135319103","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An Interpretable Neonatal Lung Ultrasound Feature Extraction and Lung Sliding Detection System Using Object Detectors","authors":"Rodina Bassiouny;Adel Mohamed;Karthi Umapathy;Naimul Khan","doi":"10.1109/JTEHM.2023.3327424","DOIUrl":"10.1109/JTEHM.2023.3327424","url":null,"abstract":"The objective of this study was to develop an interpretable system that could detect specific lung features in neonates. A challenging aspect of this work was that normal lungs showed the same visual features (as that of Pneumothorax (PTX)). M-mode is typically necessary to differentiate between the two cases, but its generation in clinics is time-consuming and requires expertise for interpretation, which remains limited. Therefore, our system automates M-mode generation by extracting Regions of Interest (ROIs) without human in the loop. Object detection models such as faster Region Based Convolutional Neural Network (fRCNN) and RetinaNet models were employed to detect seven common Lung Ultrasound (LUS) features. fRCNN predictions were then stored and further used to generate M-modes. Beyond static feature extraction, we used a Hough transform based statistical method to detect “lung sliding” in these M-modes. Results showed that fRCNN achieved a greater mean Average Precision (mAP) of 86.57% (Intersection-over-Union (IoU) = 0.2) than RetinaNet, which only displayed a mAP of 61.15%. The calculated accuracy for the generated RoIs was 97.59% for Normal videos and 96.37% for PTX videos. Using this system, we successfully classified 5 PTX and 6 Normal video cases with 100% accuracy. Automating the process of detecting seven prominent LUS features addresses the time-consuming manual evaluation of Lung ultrasound in a fast paced environment. Clinical impact: Our research work provides a significant clinical impact as it provides a more accurate and efficient method for diagnosing lung diseases in neonates.","PeriodicalId":54255,"journal":{"name":"IEEE Journal of Translational Engineering in Health and Medicine-Jtehm","volume":"12 ","pages":"119-128"},"PeriodicalIF":3.4,"publicationDate":"2023-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10295523","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134981016","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparative Assessment of Physiological Responses to Emotional Elicitation by Auditory and Visual Stimuli","authors":"Edoardo M. Polo;Andrea Farabbi;Maximiliano Mollura;Alessia Paglialonga;Luca Mainardi;Riccardo Barbieri","doi":"10.1109/JTEHM.2023.3324249","DOIUrl":"10.1109/JTEHM.2023.3324249","url":null,"abstract":"The study of emotions through the analysis of the induced physiological responses gained increasing interest in the past decades. Emotion-related studies usually employ films or video clips, but these stimuli do not give the possibility to properly separate and assess the emotional content provided by sight or hearing in terms of physiological responses. In this study we have devised an experimental protocol to elicit emotions by using, separately and jointly, pictures and sounds from the widely used International Affective Pictures System and International Affective Digital Sounds databases. We processed galvanic skin response, electrocardiogram, blood volume pulse, pupillary signal and electroencephalogram from 21 subjects to extract both autonomic and central nervous system indices to assess physiological responses in relation to three types of stimulation: auditory, visual, and auditory/visual. Results show a higher galvanic skin response to sounds compared to images. Electrocardiogram and blood volume pulse show different trends between auditory and visual stimuli. The electroencephalographic signal reveals a greater attention paid by the subjects when listening to sounds compared to watching images. In conclusion, these results suggest that emotional responses increase during auditory stimulation at both central and peripheral levels, demonstrating the importance of sounds for emotion recognition experiments and also opening the possibility toward the extension of auditory stimuli in other fields of psychophysiology. Clinical and Translational Impact Statement- These findings corroborate auditory stimuli’s importance in eliciting emotions, supporting their use in studying affective responses, e.g., mood disorder diagnosis, human-machine interaction, and emotional perception in pathology.","PeriodicalId":54255,"journal":{"name":"IEEE Journal of Translational Engineering in Health and Medicine-Jtehm","volume":"12 ","pages":"171-181"},"PeriodicalIF":3.4,"publicationDate":"2023-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10283859","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136303851","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Overground Walking With a Transparent Exoskeleton Shows Changes in Spatiotemporal Gait Parameters","authors":"Rafhael M. Andrade;Stefano Sapienza;Abolfazl Mohebbi;Eric E. Fabara;Paolo Bonato","doi":"10.1109/JTEHM.2023.3323381","DOIUrl":"10.1109/JTEHM.2023.3323381","url":null,"abstract":"Lower-limb gait training (GT) exoskeletons have been successfully used in rehabilitation programs to overcome the burden of locomotor impairment. However, providing suitable net interaction torques to assist patient movements is still a challenge. Previous transparent operation approaches have been tested in treadmill-based GT exoskeletons to improve user-robot interaction. However, it is not yet clear how a transparent lower-limb GT system affects user’s gait kinematics during overground walking, which unlike treadmill-based systems, requires active participation of the subjects to maintain stability. In this study, we implemented a transparent operation strategy on the ExoRoboWalker, an overground GT exoskeleton, to investigate its effect on the user’s gait. The approach employs a feedback zero-torque controller with feedforward compensation for the exoskeleton’s dynamics and actuators’ impedance. We analyzed the data of five healthy subjects walking overground with the exoskeleton in transparent mode (ExoTransp) and non-transparent mode (ExoOff) and walking without exoskeleton (NoExo). The transparent controller reduced the user-robot interaction torque and improved the user’s gait kinematics relative to ExoOff. No significant difference in stride length is observed between ExoTransp and NoExo (p = 0.129). However, the subjects showed a significant difference in cadence between ExoTransp (50.9± 1.1 steps/min) and NoExo (93.7 ± 8.7 steps/min) (p = 0.015), but not between ExoTransp and ExoOff (p = 0.644). Results suggest that subjects wearing the exoskeleton adjust their gait as in an attention-demanding task changing the spatiotemporal gait characteristics likely to improve gait balance.","PeriodicalId":54255,"journal":{"name":"IEEE Journal of Translational Engineering in Health and Medicine-Jtehm","volume":"12 ","pages":"182-193"},"PeriodicalIF":3.4,"publicationDate":"2023-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10275098","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136207717","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Letter to the Editor on “Leveraging Biomedical Engineering Engineers to Improve Obstructive Sleep Apnea (OSA) Care for Our Stroke Patients”","authors":"Sara E. Benjamin;Charlene E. Gamaldo","doi":"10.1109/JTEHM.2023.3318930","DOIUrl":"10.1109/JTEHM.2023.3318930","url":null,"abstract":"Obstructive sleep apnea (OSA), a condition of recurring, episodic complete or upper airway collapse, is a common disorder, affecting an estimated 17.4% of women and 33.9% of men in the United States \u0000<xref>[1]</xref>\u0000. The first line treatment for OSA is Continuous Positive Airway Pressure (CPAP) therapy, a medical device that delivers adequate airflow and oxygenation during sleep by way of a tube that connects an air compressor to a face mask that can fit over the nose, under the nose, or over the nose and mouth.","PeriodicalId":54255,"journal":{"name":"IEEE Journal of Translational Engineering in Health and Medicine-Jtehm","volume":"11 ","pages":"536-537"},"PeriodicalIF":3.4,"publicationDate":"2023-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10268080","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135844603","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}