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Applications of Deep Learning and Sensing Technologies in Healthcare Monitoring
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Applications of Deep Learning and Sensing Technologies in Healthcare Monitoring

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Biomedical Sensors".

Deadline for manuscript submissions: closed (7 April 2023) | Viewed by 20792

Special Issue Editors

Dr. Tao Tan

E-Mail Website
Guest Editor
Faulty of Applied Science, Macao Polytechnic University, Macao 999078, China
Interests: deep learning; patient care; health care; cancer screening; medical image processing
Dr. Jiangang Chen

E-Mail Website
Guest Editor
Shanghai Key Laboratory of Multidimensional Information Processing, School of Communication & Electronic Engineering, East China Normal University, Shanghai 200241, China
Interests: emergency and critical care ultrasound; biomedical ultrasonics; medical image analysis
Dr. Fan Huang

E-Mail Website
Guest Editor
Department of Diagnostic Radiology, The University of Hong Kong, Hong Kong 999077, China
Interests: image processing; retina imaging; pattern recognition; machine vision
Dr. Xiayu Xu

E-Mail Website
Guest Editor
Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi’an Jiaotong University, Xi’an 710000, China
Interests: image processing; point-of-care; pattern recognition; machine vision
Dr. Mengze Xu

E-Mail Website
Guest Editor
Faculty of Health Sciences, University of Macau, Taipa, Macau 999078, China
Interests: biomedical engineering; medical imaging; artificial intelligence; biomaterial applications; cancer theranostics; neuroimaging and regulation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) is now considered to be one of the most important areas of digital science and healthcare research. Artificial intelligence is now regarded as the source of breakthroughs for a new generation of industrial innovation. The COVID-19 pandemic has also accelerated investment in artificial intelligence, especially in healthcare.  The clinical impact of machine intelligence has enormous potential to enhance healthcare, making it more feasible, accessible and affordable. However, applications are still in their early stages due to numerous industrial constraints and technical limitations of AI such as the the lack of explainability. A complex set of ethical considerations also affect the speed of AI deployment. Currently, there is no clear definition of legal responsibility for AI systems in healthcare, which has raised concerns for both doctors and patients.

The objective of this Special Issue is to generate a comprehensive understanding of medical AI and biosensors in clinical applications. It will also highlight the latest industrial solutions to recent challenges for AI deployment in clinical settings. Authors are invited to submit outstanding and original unpublished research manuscripts focused on the latest findings in related fields. The topics of interest are limited to the following:

  • Artificial intelligence for medical imaging;
  • Explainable artificial intelligence in healthcare;
  • Artificial intelligence for healthcare sensing and monitoring;
  • Interventional tracking and navigation;
  • Medical robotics and haptics;
  • Biosensors;
  • Biosensing technique;
  • Wearable devices;
  • Medical artificial intelligence;
  • Diagnosis of artificial intelligence;
  • Predication of medical artificial intelligence;
  • Artificial intelligence ethical study in medicine;
  • Medical imaging understanding;
  • Image segmentation, registration, and fusion;
  • Image reconstruction and image quality;
  • Computer-aided diagnosis;
  • Population imaging and imaging genetics;
  • Applications of big data in imaging;
  • Integration of imaging with non-imaging biomarkers;
  • Visualization in biomedical Imaging;
  • Interventional imaging systems;
  • Image-guided interventions and surgery.

Dr. Tao Tan
Dr. Jiangang Chen
Dr. Fan Huang
Dr. Xiayu Xu
Dr. Mengze Xu
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • artificial intelligence
  • medical sensors
  • biosensors
  • medical imaging

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Published Papers (4 papers)

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Research

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12 pages, 6445 KiB  
Article
Differentiation between Phyllodes Tumors and Fibroadenomas through Breast Ultrasound: Deep-Learning Model Outperforms Ultrasound Physicians
by Zhaoting Shi, Yebo Ma, Xiaowen Ma, Anqi Jin, Jin Zhou, Na Li, Danli Sheng, Cai Chang, Jiangang Chen and Jiawei Li
Sensors 2023, 23(11), 5099; https://doi.org/10.3390/s23115099 - 26 May 2023
Cited by 2 | Viewed by 8067
Abstract
The preoperative differentiation of breast phyllodes tumors (PTs) from fibroadenomas (FAs) plays a critical role in identifying an appropriate surgical treatment. Although several imaging modalities are available, reliable differentiation between PT and FA remains a great challenge for radiologists in clinical work. Artificial [...] Read more.
The preoperative differentiation of breast phyllodes tumors (PTs) from fibroadenomas (FAs) plays a critical role in identifying an appropriate surgical treatment. Although several imaging modalities are available, reliable differentiation between PT and FA remains a great challenge for radiologists in clinical work. Artificial intelligence (AI)-assisted diagnosis has shown promise in distinguishing PT from FA. However, a very small sample size was adopted in previous studies. In this work, we retrospectively enrolled 656 breast tumors (372 FAs and 284 PTs) with 1945 ultrasound images in total. Two experienced ultrasound physicians independently evaluated the ultrasound images. Meanwhile, three deep-learning models (i.e., ResNet, VGG, and GoogLeNet) were applied to classify FAs and PTs. The robustness of the models was evaluated by fivefold cross validation. The performance of each model was assessed by using the receiver operating characteristic (ROC) curve. The area under the curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were also calculated. Among the three models, the ResNet model yielded the highest AUC value, of 0.91, with an accuracy value of 95.3%, a sensitivity value of 96.2%, and a specificity value of 94.7% in the testing data set. In contrast, the two physicians yielded an average AUC value of 0.69, an accuracy value of 70.7%, a sensitivity value of 54.4%, and a specificity value of 53.2%. Our findings indicate that the diagnostic performance of deep learning is better than that of physicians in the distinction of PTs from FAs. This further suggests that AI is a valuable tool for aiding clinical diagnosis, thereby advancing precision therapy. Full article
(This article belongs to the Special Issue Applications of Deep Learning and Sensing Technologies in Healthcare Monitoring)
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20 pages, 5382 KiB  
Article
Deep Learning Strategy for Sliding ECG Analysis during Cardiopulmonary Resuscitation: Influence of the Hands-Off Time on Accuracy
by Vessela Krasteva, Jean-Philippe Didon, Sarah Ménétré and Irena Jekova
Sensors 2023, 23(9), 4500; https://doi.org/10.3390/s23094500 - 5 May 2023
Cited by 7 | Viewed by 2434
Abstract
This study aims to present a novel deep learning algorithm for a sliding shock advisory decision during cardiopulmonary resuscitation (CPR) and its performance evaluation as a function of the cumulative hands-off time. We retrospectively used 13,570 CPR episodes from out-of-hospital cardiac arrest (OHCA) [...] Read more.
This study aims to present a novel deep learning algorithm for a sliding shock advisory decision during cardiopulmonary resuscitation (CPR) and its performance evaluation as a function of the cumulative hands-off time. We retrospectively used 13,570 CPR episodes from out-of-hospital cardiac arrest (OHCA) interventions reviewed in a period of interest from 30 s before to 10 s after regular analysis of automated external defibrillators (AEDs). Three convolutional neural networks (CNNs) with raw ECG input (duration of 5, 10, and 15 s) were applied for the shock advisory decision during CPR in 26 sequential analyses shifted by 1 s. The start and stop of chest compressions (CC) can occur at arbitrary times in sequential slides; therefore, the sliding hands-off time (sHOT) quantifies the cumulative CC-free portion of the analyzed ECG. An independent test with CPR episodes in 393 ventricular fibrillations (VF), 177 normal sinus rhythms (NSR), 1848 other non-shockable rhythms (ONR), and 3979 asystoles (ASYS) showed a substantial improvement of VF sensitivity when increasing the analysis duration from 5 s to 10 s. Specificity was not dependent on the ECG analysis duration. The 10 s CNN model presented the best performance: 92–94.4% (VF), 92.2–94% (ASYS), 96–97% (ONR), and 98.2–99.5% (NSR) for sliding decision times during CPR; 98–99% (VF), 98.2–99.8% (ASYS), 98.8–99.1 (ONR), and 100% (NSR) for sliding decision times after end of CPR. We identified the importance of sHOT as a reliable predictor of performance, accounting for the minimal sHOT interval of 2–3 s that provides a reliable rhythm detection satisfying the American Heart Association (AHA) standards for AED rhythm analysis. The presented technology for sliding shock advisory decision during CPR achieved substantial performance improvement in short hands-off periods (>2 s), such as insufflations or pre-shock pauses. The performance was competitive despite 1–2.8% point lower ASYS detection during CPR than the standard requirement (95%) for non-noisy ECG signals. The presented deep learning strategy is a basis for improved CPR practices involving both continuous CC and CC with insufflations, associated with minimal CC interruptions for reconfirmation of non-shockable rhythms (minimum hands-off time) and early treatment of VF (minimal pre-shock pauses). Full article
(This article belongs to the Special Issue Applications of Deep Learning and Sensing Technologies in Healthcare Monitoring)
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23 pages, 1840 KiB  
Article
A Tiny Matched Filter-Based CNN for Inter-Patient ECG Classification and Arrhythmia Detection at the Edge
by Mohammed M. Farag
Sensors 2023, 23(3), 1365; https://doi.org/10.3390/s23031365 - 26 Jan 2023
Cited by 24 | Viewed by 4021
Abstract
Automated electrocardiogram (ECG) classification using machine learning (ML) is extensively utilized for arrhythmia detection. Contemporary ML algorithms are typically deployed on the cloud, which may not always meet the availability and privacy requirements of ECG monitoring. Edge inference is an emerging alternative that [...] Read more.
Automated electrocardiogram (ECG) classification using machine learning (ML) is extensively utilized for arrhythmia detection. Contemporary ML algorithms are typically deployed on the cloud, which may not always meet the availability and privacy requirements of ECG monitoring. Edge inference is an emerging alternative that overcomes the concerns of cloud inference; however, it poses new challenges due to the demanding computational requirements of modern ML algorithms and the tight constraints of edge devices. In this work, we propose a tiny convolutional neural network (CNN) classifier for real-time monitoring of ECG at the edge with the aid of the matched filter (MF) theory. The MIT-BIH dataset with inter-patient division is used for model training and testing. The model generalization capability is validated on the INCART, QT, and PTB diagnostic databases, and the model performance in the presence of noise is experimentally analyzed. The proposed classifier can achieve average accuracy, sensitivity, and F1 scores of 98.18%, 91.90%, and 92.17%, respectively. The sensitivity of detecting supraventricular and ventricular ectopic beats (SVEB and VEB) is 85.3% and 96.34%, respectively. The model is 15 KB in size, with an average inference time of less than 1 ms. The proposed model achieves superior classification and real-time performance results compared to the state-of-the-art ECG classifiers while minimizing the model complexity. The proposed classifier can be readily deployed on a wide range of resource-constrained edge devices for arrhythmia monitoring, which can save millions of cardiovascular disease patients. Full article
(This article belongs to the Special Issue Applications of Deep Learning and Sensing Technologies in Healthcare Monitoring)
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Review

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15 pages, 7588 KiB  
Review
Deep Learning Aided Neuroimaging and Brain Regulation
by Mengze Xu, Yuanyuan Ouyang and Zhen Yuan
Sensors 2023, 23(11), 4993; https://doi.org/10.3390/s23114993 - 23 May 2023
Cited by 9 | Viewed by 5296
Abstract
Currently, deep learning aided medical imaging is becoming the hot spot of AI frontier application and the future development trend of precision neuroscience. This review aimed to render comprehensive and informative insights into the recent progress of deep learning and its applications in [...] Read more.
Currently, deep learning aided medical imaging is becoming the hot spot of AI frontier application and the future development trend of precision neuroscience. This review aimed to render comprehensive and informative insights into the recent progress of deep learning and its applications in medical imaging for brain monitoring and regulation. The article starts by providing an overview of the current methods for brain imaging, highlighting their limitations and introducing the potential benefits of using deep learning techniques to overcome these limitations. Then, we further delve into the details of deep learning, explaining the basic concepts and providing examples of how it can be used in medical imaging. One of the key strengths is its thorough discussion of the different types of deep learning models that can be used in medical imaging including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative adversarial network (GAN) assisted magnetic resonance imaging (MRI), positron emission tomography (PET)/computed tomography (CT), electroencephalography (EEG)/magnetoencephalography (MEG), optical imaging, and other imaging modalities. Overall, our review on deep learning aided medical imaging for brain monitoring and regulation provides a referrable glance for the intersection of deep learning aided neuroimaging and brain regulation. Full article
(This article belongs to the Special Issue Applications of Deep Learning and Sensing Technologies in Healthcare Monitoring)
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