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Artificial Intelligence and Multimodal Technology for Health Applications
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Artificial Intelligence and Multimodal Technology for Health Applications

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

Deadline for manuscript submissions: closed (30 June 2022) | Viewed by 14323

Special Issue Editors

Dr. Alessandro Ortis

E-Mail Website
Guest Editor
Department of Mathematics and Computer Science, University of Catania, 95125 Catania, Italy
Interests: computer vision; multimedia; image processing; machine learning
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Sebastiano Battiato

E-Mail Website
Guest Editor
Department of Mathematics and Computer Science, University of Catania, Viale A. Doria, 6, 95125 Catania, Italy
Interests: computer vision; multimedia; image processing; machine learning; digital forensics
Special Issues, Collections and Topics in MDPI journals
Dr. Francesco Rundo

E-Mail Website
Guest Editor
STMicroelectronics ADG—Central R&D, 95121 Catania, Italy
Interests: mathematical modeling; medical imaging; deep learning for automotive and industrial applications

Special Issue Information

Dear Colleagues,

With the rapid advance of technology, AI techniques are being effectively used in several fields, including health applications aimed to improve the efficiency of diagnosis and treatments to support therapeutic decisions as well as the prediction of outcome in many clinical scenarios. Modern health treatments are faced with the challenge of acquiring, analyzing, and applying the large amount of data, such as images and physiological raw signals, necessary to solve complex problems. The contribution from different sources of information is often complementary and, hence, multimodal approaches are often proposed. This Special Issue will provide a forum for the publication of articles that address broad challenges in both theoretical and application aspects of AI in health applications. We are inviting original research work covering novel theories, innovative methods, and meaningful applications that can potentially lead to significant advances in the field of AI and multimodal approaches for health applications as well as to stimulate continuing efforts in the application of AI approaches to solve health problems.

The following topics of this Special Issue explicitly include, but are not limited to:

  • Monitoring and decision-making support
  • Inference models for prevention, early detection, diagnosis, treatment, and prognosis
  • Interpretability and trustworthiness of AI methods in health applications
  • Sensor technology health applications
  • Legal and ethical aspects of health technology
  • Medical image acquisition and processing
  • Novel AI techniques in medical data processing and analysis
  • AI in the mental disorder detection
  • AI applications for COVID-19 pandemic
  • Virtual and augmented reality for health applications
  • Cyberpsychology

Dr. Alessandro Ortis
Prof. Sebastiano Battiato
Dr. Francesco Rundo
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
  • multimodal analysis and modeling
  • healthcare technology
  • digital health

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Published Papers (5 papers)

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Research

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33 pages, 2977 KiB  
Article
Dependency Factors in Evidence Theory: An Analysis in an Information Fusion Scenario Applied in Adverse Drug Reactions
by Luiz Alberto Pereira Afonso Ribeiro, Ana Cristina Bicharra Garcia and Paulo Sérgio Medeiros dos Santos
Sensors 2022, 22(6), 2310; https://doi.org/10.3390/s22062310 - 16 Mar 2022
Cited by 2 | Viewed by 2172
Abstract
Multisensor information fusion brings challenges such as data heterogeneity, source precision, and the merger of uncertainties that impact the quality of classifiers. A widely used approach for classification problems in a multisensor context is the Dempster–Shafer Theory. This approach considers the beliefs attached [...] Read more.
Multisensor information fusion brings challenges such as data heterogeneity, source precision, and the merger of uncertainties that impact the quality of classifiers. A widely used approach for classification problems in a multisensor context is the Dempster–Shafer Theory. This approach considers the beliefs attached to each source to consolidate the information concerning the hypotheses to come up with a classifier with higher precision. Nevertheless, the fundamental premise for using the approach is that sources are independent and that the classification hypotheses are mutually exclusive. Some approaches ignore this premise, which can lead to unreliable results. There are other approaches, based on statistics and machine learning techniques, that expurgate the dependencies or include a discount factor to mitigate the risk of dependencies. We propose a novel approach based on Bayesian net, Pearson’s test, and linear regression to adjust the beliefs for more accurate data fusion, mitigating possible correlations or dependencies. We tested our approach by applying it in the domain of adverse drug reactions discovery. The experiment used nine databases containing data from 50,000 active patients of a Brazilian cancer hospital, including clinical exams, laboratory tests, physicians’ anamnesis, medical prescriptions, clinical notes, medicine leaflets packages, international classification of disease, and sickness diagnosis models. This study had the hospital’s ethical committee approval. A statistically significant improvement in the precision and recall of the results was obtained compared with existing approaches. The results obtained show that the credibility index proposed by the model significantly increases the quality of the evidence generated with the algorithm Random Forest. A benchmark was performed between three datasets, incremented gradually with attributes of a credibility index, obtaining a precision of 92%. Finally, we performed a benchmark with a public base of heart disease, achieving good results. Full article
(This article belongs to the Special Issue Artificial Intelligence and Multimodal Technology for Health Applications)
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21 pages, 5441 KiB  
Article
Association of Neuroimaging Data with Behavioral Variables: A Class of Multivariate Methods and Their Comparison Using Multi-Task FMRI Data
by M. A. B. S. Akhonda, Yuri Levin-Schwartz, Vince D. Calhoun and Tülay Adali
Sensors 2022, 22(3), 1224; https://doi.org/10.3390/s22031224 - 5 Feb 2022
Cited by 5 | Viewed by 3222
Abstract
It is becoming increasingly common to collect multiple related neuroimaging datasets either from different modalities or from different tasks and conditions. In addition, we have non-imaging data such as cognitive or behavioral variables, and it is through the association of these two sets [...] Read more.
It is becoming increasingly common to collect multiple related neuroimaging datasets either from different modalities or from different tasks and conditions. In addition, we have non-imaging data such as cognitive or behavioral variables, and it is through the association of these two sets of data—neuroimaging and non-neuroimaging—that we can understand and explain the evolution of neural and cognitive processes, and predict outcomes for intervention and treatment. Multiple methods for the joint analysis or fusion of multiple neuroimaging datasets or modalities exist; however, methods for the joint analysis of imaging and non-imaging data are still in their infancy. Current approaches for identifying brain networks related to cognitive assessments are still largely based on simple one-to-one correlation analyses and do not use the cross information available across multiple datasets. This work proposes two approaches based on independent vector analysis (IVA) to jointly analyze the imaging datasets and behavioral variables such that multivariate relationships across imaging data and behavioral features can be identified. The simulation results show that our proposed methods provide better accuracy in identifying associations across imaging and behavioral components than current approaches. With functional magnetic resonance imaging (fMRI) task data collected from 138 healthy controls and 109 patients with schizophrenia, results reveal that the central executive network (CEN) estimated in multiple datasets shows a strong correlation with the behavioral variable that measures working memory, a result that is not identified by traditional approaches. Most of the identified fMRI maps also show significant differences in activations across healthy controls and patients potentially providing a useful signature of mental disorders. Full article
(This article belongs to the Special Issue Artificial Intelligence and Multimodal Technology for Health Applications)
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17 pages, 3714 KiB  
Article
Advanced eNose-Driven Pedestrian Tracking Pipeline for Intelligent Car Driver Assisting System: Preliminary Results
by Francesco Rundo, Ilaria Anfuso, Maria Grazia Amore, Alessandro Ortis, Angelo Messina, Sabrina Conoci and Sebastiano Battiato
Sensors 2022, 22(2), 674; https://doi.org/10.3390/s22020674 - 16 Jan 2022
Cited by 2 | Viewed by 2715
Abstract
From a biological point of view, alcohol human attentional impairment occurs before reaching a Blood Alcohol Content (BAC index) of 0.08% (0.05% under the Italian legislation), thus generating a significant impact on driving safety if the drinker subject is driving a car. Car [...] Read more.
From a biological point of view, alcohol human attentional impairment occurs before reaching a Blood Alcohol Content (BAC index) of 0.08% (0.05% under the Italian legislation), thus generating a significant impact on driving safety if the drinker subject is driving a car. Car drivers must keep a safe driving dynamic, having an unaltered physiological status while processing the surrounding information coming from the driving scenario (e.g., traffic signs, other vehicles and pedestrians). Specifically, the identification and tracking of pedestrians in the driving scene is a widely investigated problem in the scientific community. The authors propose a full, deep pipeline for the identification, monitoring and tracking of the salient pedestrians, combined with an intelligent electronic alcohol sensing system to properly assess the physiological status of the driver. More in detail, the authors propose an intelligent sensing system that makes a common air quality sensor selective to alcohol. A downstream Deep 1D Temporal Residual Convolutional Neural Network architecture will be able to learn specific embedded alcohol-dynamic features in the collected sensing data coming from the GHT25S air-quality sensor of STMicroelectronics. A parallel deep attention-augmented architecture identifies and tracks the salient pedestrians in the driving scenario. A risk assessment system evaluates the sobriety of the driver in case of the presence of salient pedestrians in the driving scene. The collected preliminary results confirmed the effectiveness of the proposed approach. Full article
(This article belongs to the Special Issue Artificial Intelligence and Multimodal Technology for Health Applications)
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16 pages, 3185 KiB  
Article
Adaptive Neural Network Control of Time Delay Teleoperation System Based on Model Approximation
by Yaxiang Wang, Jiawei Tian, Yan Liu, Bo Yang, Shan Liu, Lirong Yin and Wenfeng Zheng
Sensors 2021, 21(22), 7443; https://doi.org/10.3390/s21227443 - 9 Nov 2021
Cited by 16 | Viewed by 2502
Abstract
A bilateral neural network adaptive controller is designed for a class of teleoperation systems with constant time delay, external disturbance and internal friction. The stability of the teleoperation force feedback system with constant communication channel delay and nonlinear, complex, and uncertain constant time [...] Read more.
A bilateral neural network adaptive controller is designed for a class of teleoperation systems with constant time delay, external disturbance and internal friction. The stability of the teleoperation force feedback system with constant communication channel delay and nonlinear, complex, and uncertain constant time delay is guaranteed, and its tracking performance is improved. In the controller design process, the neural network method is used to approximate the system model, and the unknown internal friction and external disturbance of the system are estimated by the adaptive method, so as to avoid the influence of nonlinear uncertainties on the system. Full article
(This article belongs to the Special Issue Artificial Intelligence and Multimodal Technology for Health Applications)
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Review

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27 pages, 496 KiB  
Review
A Review on Planted (l, d) Motif Discovery Algorithms for Medical Diagnose
by Satarupa Mohanty, Prasant Kumar Pattnaik, Ahmed Abdulhakim Al-Absi and Dae-Ki Kang
Sensors 2022, 22(3), 1204; https://doi.org/10.3390/s22031204 - 5 Feb 2022
Cited by 3 | Viewed by 2303
Abstract
Personalized diagnosis of chronic disease requires capturing the continual pattern across the biological sequence. This repeating pattern in medical science is called “Motif”. Motifs are the short, recurring patterns of biological sequences that are supposed signify some health disorder. They identify the binding [...] Read more.
Personalized diagnosis of chronic disease requires capturing the continual pattern across the biological sequence. This repeating pattern in medical science is called “Motif”. Motifs are the short, recurring patterns of biological sequences that are supposed signify some health disorder. They identify the binding sites for transcription factors that modulate and synchronize the gene expression. These motifs are important for the analysis and interpretation of various health issues like human disease, gene function, drug design, patient’s conditions, etc. Searching for these patterns is an important step in unraveling the mechanisms of gene expression properly diagnose and treat chronic disease. Thus, motif identification has a vital role in healthcare studies and attracts many researchers. Numerous approaches have been characterized for the motif discovery process. This article attempts to review and analyze fifty-four of the most frequently found motif discovery processes/algorithms from different approaches and summarizes the discussion with their strengths and weaknesses. Full article
(This article belongs to the Special Issue Artificial Intelligence and Multimodal Technology for Health Applications)
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