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Recent Advances in Artificial Intelligence for Medicine and Healthcare Data Analysis

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

Deadline for manuscript submissions: closed (31 July 2023) | Viewed by 16170

Special Issue Editors

Prof. Dr. Li Zhang

E-Mail Website
Guest Editor
Department of Computer Science, Royal Holloway, University of London, Surrey TW20 0EX, UK
Interests: deep learning; machine learning; computer vision; medical imaging; intelligent robotics
Special Issues, Collections and Topics in MDPI journals
Dr. Richard Jiang

E-Mail Website
Guest Editor
School of Computing and Communication, Lancaster University, Lancaster LA1 4YW, UK
Interests: artificial intelligence; AI ethics; privacy-preserving machine learning; quantum AI; neuronal computation and biomedical image analysis
Special Issues, Collections and Topics in MDPI journals
Dr. Hua-Liang Wei

E-Mail Website
Guest Editor
Department of Automatic Control and Systems Engineering, The University of Sheffield, Sheffield S10 2TN, UK
Interests: bioscience signal processing; data modeling
Special Issues, Collections and Topics in MDPI journals
Dr. Yuzhu Guo

E-Mail Website
Guest Editor
School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China
Interests: brain dynamics and brain activities; brain–computer interfaces; AI for clinical disease diagnosis; neurorehabilitation; hybrid-augmented intelligence
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Yang Li

E-Mail Website
Guest Editor
School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China
Interests: brain informatics; medical image processing; deep learning; brain–computer interfaces

Special Issue Information

Dear Colleagues,

Recently accompanying the advancement of machine learning (especially deep learning), AI-assisted medical and healthcare data analysis have entered into a new era with exciting progress in many areas. It is an amazing golden age for the interface researchers involved in the development and applications of advanced machine learning techniques to solve long-standing problems with the merits of powerful AI techniques. This Special Issue aims to capture the most recent important advances in these cross-disciplinary areas and provide a platform for academics, scientists, researchers, clinicians, medical services, etc.

Topics include but are not limited to the following:

  • Medical image analysis.
  • Signal processing and medical imaging.
  • Deep learning for medical applications.
  • Automated medical diagnostics.
  • AI-assisted pathologies.
  • Brain and neuroscience.
  • Mental and psychological diagnosis.
  • Neurodegenerative diseases.
  • Detection and diagnosis of cardiovascular disease.
  • Cancer imaging and diagnosis.
  • Hematology and blood analysis.
  • Health and wellbeing data analysis.
  • Medical data management and EHR.
  • Medical biometrics.
  • Medical robotics.
  • AIoT medical devices.
  • Pandemic prediction and prevention.
  • Any AI-related topics.

Dr. Li Zhang
Dr. Richard Jiang
Dr. Hua-Liang Wei
Dr. Yuzhu Guo
Prof. Dr. Yang Li
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.

Published Papers (6 papers)

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Research

18 pages, 3838 KiB  
Article
Assessment of a Person’s Emotional State Based on His or Her Posture Parameters
by Yulia Shichkina, Olga Bureneva, Evgenii Salaurov and Ekaterina Syrtsova
Sensors 2023, 23(12), 5591; https://doi.org/10.3390/s23125591 - 15 Jun 2023
Viewed by 1074
Abstract
This article is devoted to the study of the correlation between the emotional state of a person and the posture of his or her body in the sitting position. In order to carry out the study, we developed the first version of the [...] Read more.
This article is devoted to the study of the correlation between the emotional state of a person and the posture of his or her body in the sitting position. In order to carry out the study, we developed the first version of the hardware-software system based on a posturometric armchair, allowing the characteristics of the posture of a sitting person to be evaluated using strain gauges. Using this system, we revealed the correlation between sensor readings and human emotional states. We showed that certain readings of a sensor group are formed for a certain emotional state of a person. We also found that the groups of triggered sensors, their composition, their number, and their location are related to the states of a particular person, which led to the need to build personalized digital pose models for each person. The intellectual component of our hardware–software complex is based on the concept of co-evolutionary hybrid intelligence. The system can be used during medical diagnostic procedures and rehabilitation processes, as well as in controlling people whose professional activity is connected with increased psycho-emotional load and can cause cognitive disorders, fatigue, and professional burnout and can lead to the development of diseases. Full article
(This article belongs to the Special Issue Recent Advances in Artificial Intelligence for Medicine and Healthcare Data Analysis)
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14 pages, 4003 KiB  
Article
Facial Feature Extraction Using a Symmetric Inline Matrix-LBP Variant for Emotion Recognition
by Eaby Kollonoor Babu, Kamlesh Mistry, Muhammad Naveed Anwar and Li Zhang
Sensors 2022, 22(22), 8635; https://doi.org/10.3390/s22228635 - 09 Nov 2022
Cited by 4 | Viewed by 1511
Abstract
With a large number of Local Binary Patterns (LBP) variants being currently used today, the significant and importance of visual descriptors in computer vision applications are prominent. This paper presents a novel visual descriptor, i.e., SIM-LBP. It employs a new matrix technique called [...] Read more.
With a large number of Local Binary Patterns (LBP) variants being currently used today, the significant and importance of visual descriptors in computer vision applications are prominent. This paper presents a novel visual descriptor, i.e., SIM-LBP. It employs a new matrix technique called the Symmetric Inline Matrix generator method, which acts as a new variant of LBP. The key feature that separates our variant from existing counterparts is that our variant is very efficient in extracting facial expression features like eyes, eye brows, nose and mouth in a wide range of lighting conditions. For testing our model, we applied SIM-LBP on the JAFFE dataset to convert all the images to its corresponding SIM-LBP transformed variant. These transformed images are then used to train a Convolution Neural Network (CNN) based deep learning model for facial expressions recognition (FER). Several performance evaluation metrics, i.e., recognition accuracy rate, precision, recall, and F1-score, were used to test mode efficiency in comparison with those using the traditional LBP descriptor and other LBP variants. Our model outperformed in all four matrices with the proposed SIM-LBP transformation on the input images against those of baseline methods. In comparison analysis with the other state-of-the-art methods, it shows the usefulness of the proposed SIM-LBP model. Our proposed SIM-LBP variant transformation can also be applied on facial images to identify a person’s mental states and predict mood variations. Full article
(This article belongs to the Special Issue Recent Advances in Artificial Intelligence for Medicine and Healthcare Data Analysis)
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20 pages, 3629 KiB  
Article
Development of an Obesity Information Diagnosis Model Reflecting Body Type Information Using 3D Body Information Values
by Changgyun Kim and Sekyoung Youm
Sensors 2022, 22(20), 7808; https://doi.org/10.3390/s22207808 - 14 Oct 2022
Viewed by 1667
Abstract
This study uses various body values (length, circumference, and volume) that can be derived from 3D data to determine variables and areas that substantially affect obesity and suggests guidelines for diagnosing obesity that are more elaborate than existing obesity indices. Body data for [...] Read more.
This study uses various body values (length, circumference, and volume) that can be derived from 3D data to determine variables and areas that substantially affect obesity and suggests guidelines for diagnosing obesity that are more elaborate than existing obesity indices. Body data for 170 participants (87 men and 73 women aged 20–30 years) are collected for the chest, abdomen, hips, and arms/legs. A 3D scanner, which can produce accurate body point results, and dual-energy X-ray (DEXA), which can accurately determine the fat percentage, are used to derive fat rates for each body part. The fat percentage and total fat percentage for each body part are used as learning data. For the derived data, the eigenvalue for each body part is derived using a principal component analysis, and the following four clusters are created for each part: underweight, normal, overweight, and obese. A comparison with the obesity index, which diagnoses obesity based on the cluster model, showed that the accuracy of the model proposed in this study is higher at 80%. Therefore, this model can determine the body information necessary for accurate obesity diagnosis and be used to diagnose obesity with greater accuracy than obesity indices without a body fat measurement machine such as DEXA. Full article
(This article belongs to the Special Issue Recent Advances in Artificial Intelligence for Medicine and Healthcare Data Analysis)
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16 pages, 1349 KiB  
Article
A Graph-Neural-Network-Based Social Network Recommendation Algorithm Using High-Order Neighbor Information
by Yonghong Yu, Weiwen Qian, Li Zhang and Rong Gao
Sensors 2022, 22(19), 7122; https://doi.org/10.3390/s22197122 - 20 Sep 2022
Cited by 8 | Viewed by 2450
Abstract
Social-network-based recommendation algorithms leverage rich social network information to alleviate the problem of data sparsity and boost the recommendation performance. However, traditional social-network-based recommendation algorithms ignore high-order collaborative signals or only consider the first-order collaborative signal when learning users’ and items’ latent representations, [...] Read more.
Social-network-based recommendation algorithms leverage rich social network information to alleviate the problem of data sparsity and boost the recommendation performance. However, traditional social-network-based recommendation algorithms ignore high-order collaborative signals or only consider the first-order collaborative signal when learning users’ and items’ latent representations, resulting in suboptimal recommendation performance. In this paper, we propose a graph neural network (GNN)-based social recommendation model that utilizes the GNN framework to capture high-order collaborative signals in the process of learning the latent representations of users and items. Specifically, we formulate the representations of entities, i.e., users and items, by stacking multiple embedding propagation layers to recursively aggregate multi-hop neighborhood information on both the user–item interaction graph and the social network graph. Hence, the collaborative signals hidden in both the user–item interaction graph and the social network graph are explicitly injected into the final representations of entities. Moreover, we ease the training process of the proposed GNN-based social recommendation model and alleviate overfitting by adopting a lightweight GNN framework that only retains the neighborhood aggregation component and abandons the feature transformation and nonlinear activation components. The experimental results on two real-world datasets show that our proposed GNN-based social recommendation method outperforms the state-of-the-art recommendation algorithms. Full article
(This article belongs to the Special Issue Recent Advances in Artificial Intelligence for Medicine and Healthcare Data Analysis)
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25 pages, 677 KiB  
Article
A Deep Ensemble Neural Network with Attention Mechanisms for Lung Abnormality Classification Using Audio Inputs
by Conor Wall, Li Zhang, Yonghong Yu, Akshi Kumar and Rong Gao
Sensors 2022, 22(15), 5566; https://doi.org/10.3390/s22155566 - 26 Jul 2022
Cited by 16 | Viewed by 2422
Abstract
Medical audio classification for lung abnormality diagnosis is a challenging problem owing to comparatively unstructured audio signals present in the respiratory sound clips. To tackle such challenges, we propose an ensemble model by incorporating diverse deep neural networks with attention mechanisms for undertaking [...] Read more.
Medical audio classification for lung abnormality diagnosis is a challenging problem owing to comparatively unstructured audio signals present in the respiratory sound clips. To tackle such challenges, we propose an ensemble model by incorporating diverse deep neural networks with attention mechanisms for undertaking lung abnormality and COVID-19 diagnosis using respiratory, speech, and coughing audio inputs. Specifically, four base deep networks are proposed, which include attention-based Convolutional Recurrent Neural Network (A-CRNN), attention-based bidirectional Long Short-Term Memory (A-BiLSTM), attention-based bidirectional Gated Recurrent Unit (A-BiGRU), as well as Convolutional Neural Network (CNN). A Particle Swarm Optimization (PSO) algorithm is used to optimize the training parameters of each network. An ensemble mechanism is used to integrate the outputs of these base networks by averaging the probability predictions of each class. Evaluated using respiratory ICBHI, Coswara breathing, speech, and cough datasets, as well as a combination of ICBHI and Coswara breathing databases, our ensemble model and base networks achieve ICBHI scores ranging from 0.920 to 0.9766. Most importantly, the empirical results indicate that a positive COVID-19 diagnosis can be distinguished to a high degree from other more common respiratory diseases using audio recordings, based on the combined ICBHI and Coswara breathing datasets. Full article
(This article belongs to the Special Issue Recent Advances in Artificial Intelligence for Medicine and Healthcare Data Analysis)
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25 pages, 91129 KiB  
Article
Fine-Tuned DenseNet-169 for Breast Cancer Metastasis Prediction Using FastAI and 1-Cycle Policy
by Adarsh Vulli, Parvathaneni Naga Srinivasu, Madipally Sai Krishna Sashank, Jana Shafi, Jaeyoung Choi and Muhammad Fazal Ijaz
Sensors 2022, 22(8), 2988; https://doi.org/10.3390/s22082988 - 13 Apr 2022
Cited by 110 | Viewed by 5679
Abstract
Lymph node metastasis in breast cancer may be accurately predicted using a DenseNet-169 model. However, the current system for identifying metastases in a lymph node is manual and tedious. A pathologist well-versed with the process of detection and characterization of lymph nodes goes [...] Read more.
Lymph node metastasis in breast cancer may be accurately predicted using a DenseNet-169 model. However, the current system for identifying metastases in a lymph node is manual and tedious. A pathologist well-versed with the process of detection and characterization of lymph nodes goes through hours investigating histological slides. Furthermore, because of the massive size of most whole-slide images (WSI), it is wise to divide a slide into batches of small image patches and apply methods independently on each patch. The present work introduces a novel method for the automated diagnosis and detection of metastases from whole slide images using the Fast AI framework and the 1-cycle policy. Additionally, it compares this new approach to previous methods. The proposed model has surpassed other state-of-art methods with more than 97.4% accuracy. In addition, a mobile application is developed for prompt and quick response. It collects user information and models to diagnose metastases present in the early stages of cancer. These results indicate that the suggested model may assist general practitioners in accurately analyzing breast cancer situations, hence preventing future complications and mortality. With digital image processing, histopathologic interpretation and diagnostic accuracy have improved considerably. Full article
(This article belongs to the Special Issue Recent Advances in Artificial Intelligence for Medicine and Healthcare Data Analysis)
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