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A special issue of Healthcare (ISSN 2227-9032). This special issue belongs to the section "Artificial Intelligence in Medicine".

Deadline for manuscript submissions: closed (1 February 2022) | Viewed by 42804

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Special Issue Editor

Prof. Dr. Reza Saatchi

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Department of Engineering and Mathematics, Sheffield Hallam University, Sheffield S1 1WB, UK
Interests: medical engineering; electronic design aspects; medical engineering; medical devices; signal and image processing; artificial intelligence; machine learning; big data analysis; data mining; wireless communication; quality of service
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Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) for medical diagnosis and monitoring has gained interest from healthcare professionals and researchers. We cordially invite you to submit a paper to this Special Issue of Healthcare journal that will bring together latest AI developments for medical diagnosis and monitoring. The development may utilise deep learning, artificial neural networks, fuzzy logic, genetic algorithms, expert systems, robotics and machine learning. Developments of new AI techniques, applications of the existing AI techniques and hybrid AI techniques will be of interest.

An issue when it comes to the successful utilisation of AI is the preparation or pre-processing of data. New developments in these areas are of interest. Furthermore, articles that utilise AI-based technologies to allow patients to be remotely monitored (e.g., in their homes), time series predication, modelling and data mining are invited.

Prof. Dr. Reza Saatchi
Guest Editor

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. Healthcare 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 2700 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 (AI) techniques
medical diagnosis and monitoring
data pre-processing
hybrid AI techniques
time series prediction
modelling
data mining
big data analysis
pattern recognition
wireless body sensors
remote monitoring
tracking

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Published Papers (10 papers)

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Research

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15 pages, 7054 KiB

Open AccessArticle

Towards Facial Gesture Recognition in Photographs of Patients with Facial Palsy

by Gemma S. Parra-Dominguez, Raul E. Sanchez-Yanez and Carlos H. Garcia-Capulin

Healthcare 2022, 10(4), 659; https://doi.org/10.3390/healthcare10040659 - 31 Mar 2022

Cited by 5 | Viewed by 2338

Abstract

Humans express their emotions verbally and through actions, and hence emotions play a fundamental role in facial expressions and body gestures. Facial expression recognition is a popular topic in security, healthcare, entertainment, advertisement, education, and robotics. Detecting facial expressions via gesture recognition is a complex and challenging problem, especially in persons who suffer face impairments, such as patients with facial paralysis. Facial palsy or paralysis refers to the incapacity to move the facial muscles on one or both sides of the face. This work proposes a methodology based on neural networks and handcrafted features to recognize six gestures in patients with facial palsy. The proposed facial palsy gesture recognition system is designed and evaluated on a publicly available database with good results as a first attempt to perform this task in the medical field. We conclude that, to recognize facial gestures in patients with facial paralysis, the severity of the damage has to be considered because paralyzed organs exhibit different behavior than do healthy ones, and any recognition system must be capable of discerning these behaviors. Full article

(This article belongs to the Special Issue Artificial Intelligence Developments for Medical Diagnosis and Monitoring)

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9 pages, 1323 KiB

Open AccessArticle

Machine Learning for Predicting Risk of Early Dropout in a Recovery Program for Opioid Use Disorder

by Assaf Gottlieb, Andrea Yatsco, Christine Bakos-Block, James R. Langabeer and Tiffany Champagne-Langabeer

Healthcare 2022, 10(2), 223; https://doi.org/10.3390/healthcare10020223 - 25 Jan 2022

Cited by 7 | Viewed by 3383

Abstract

Background: An increase in opioid use has led to an opioid crisis during the last decade, leading to declarations of a public health emergency. In response to this call, the Houston Emergency Opioid Engagement System (HEROES) was established and created an emergency access pathway into long-term recovery for individuals with an opioid use disorder. A major contributor to the success of the program is retention of the enrolled individuals in the program. Methods: We have identified an increase in dropout from the program after 90 and 120 days. Based on more than 700 program participants, we developed a machine learning approach to predict the individualized risk for dropping out of the program. Results: Our model achieved sensitivity of 0.81 and specificity of 0.65 for dropout at 90 days and improved the performance to sensitivity of 0.86 and specificity of 0.66 for 120 days. Additionally, we identified individual risk factors for dropout, including previous overdose and relapse and improvement in reported quality of life. Conclusions: Our informatics approach provides insight into an area where programs may allocate additional resources in order to retain high-risk individuals and increase the chances of success in recovery. Full article

(This article belongs to the Special Issue Artificial Intelligence Developments for Medical Diagnosis and Monitoring)

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18 pages, 4427 KiB

Open AccessArticle

An Efficient System for Eye Movement Desensitization and Reprocessing (EMDR) Therapy: A Pilot Study

by Nicolae Goga, Costin-Anton Boiangiu, Andrei Vasilateanu, Alexandru-Filip Popovici, Marius-Valentin Drăgoi, Ramona Popovici, Ionatan Octavian Gancea, Mihail Cristian Pîrlog, Ramona Cristina Popa and Anton Hadăr

Healthcare 2022, 10(1), 133; https://doi.org/10.3390/healthcare10010133 - 10 Jan 2022

Cited by 3 | Viewed by 5315

Abstract

In this paper, we describe an actuator-based EMDR (eye movement desensitization and reprocessing) virtual assistant system that can be used for the treatment of participants with traumatic memories. EMDR is a psychological therapy designed to treat emotional distress caused by a traumatic event from the past, most frequently in post-traumatic stress disorder treatment. We implemented a system based on video, tactile, and audio actuators which includes an artificial intelligence chatbot, making the system capable of acting autonomously. We tested the system on a sample of 31 participants. Our results showed the efficiency of the EMDR virtual assistant system in reducing anxiety, distress, and negative cognitions and emotions associated with the traumatic memory. There are no such systems reported in the existing literature. Through the present research, we fill this gap by describing a system that can be used by patients with traumatic memories. Full article

(This article belongs to the Special Issue Artificial Intelligence Developments for Medical Diagnosis and Monitoring)

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11 pages, 2814 KiB

Open AccessArticle

Prevalence of Patients Affected by Fibromyalgia in a Cohort of Women Underwent Mammography Screening

by Gianluca Gatta, Daniele La Forgia, Annarita Fanizzi, Raffaella Massafra, Francesco Somma, Maria Paola Belfiore, Daniela Pacella, Salvatore Cappabianca and Antonio Alessandro Heliot Salvia

Healthcare 2021, 9(10), 1340; https://doi.org/10.3390/healthcare9101340 - 9 Oct 2021

Cited by 1 | Viewed by 1712

Abstract

Fibromyalgia is a widespread condition which is currently underdiagnosed; therefore we conceived this study in order to assess whether a diagnostic suspicion may be assumed during widespread screening procedures, so that patients for which a reasonable diagnostic suspicion exist may be redirected towards rheumatologic evaluation. We analyzed a sample of 1060 patients, all of whom were female and undergoing standard breast cancer screening procedures, and proceeded to evaluate the level of pain they endured during mammographic exam. We also acquired a range of other information which we related to the level of pain endured; we suggested a rheumatologic examination for those patients who endured the highest level of pain and then we evaluated how many patients in this subgroup were actually diagnosed with fibromyalgia. Out of the 1060 patients who participated to our study, 139 presented level 4 pain intensity; One patient did not go for rheumatologic examination; the remaining 138 underwent rheumatologic evaluation, and 50 (36%, 28–44, 95% CI) were diagnosed with fibromyalgia. Our study shows that assessing the level of pain endured by patients during standard widespread screening procedures may be an effective asset in deciding whether or not to suggest specialist rheumatologic evaluation for fibromyalgia. Full article

(This article belongs to the Special Issue Artificial Intelligence Developments for Medical Diagnosis and Monitoring)

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18 pages, 3504 KiB

Open AccessArticle

Kohonen Neural Network Investigation of the Effects of the Visual, Proprioceptive and Vestibular Systems to Balance in Young Healthy Adult Subjects

by Oseikhuemen Davis Ojie and Reza Saatchi

Healthcare 2021, 9(9), 1219; https://doi.org/10.3390/healthcare9091219 - 16 Sep 2021

Cited by 5 | Viewed by 2430

Abstract

Kohonen neural network (KNN) was used to investigate the effects of the visual, proprioceptive and vestibular systems using the sway information in the mediolateral (ML) and anterior-posterior (AP) directions, obtained from an inertial measurement unit, placed at the lower backs of 23 healthy adult subjects (10 males, 13 females, mean (standard deviation) age: 24.5 (4.0) years, height: 173.6 (6.8) centimeter, weight: 72.7 (9.9) kg). The measurements were based on the modified Clinical Test of Sensory Interaction and Balance (mCTSIB). KNN clustered the subjects’ time-domain sway measures by processing their sway’s root mean square position, velocity, and acceleration. Clustering effectiveness was established using external performance indicators such as purity, precision-recall, and F-measure. Differences in these measures, from the clustering of each mCTSIB condition with its condition, were used to extract information about the balance-related sensory systems, where smaller values indicated reduced sway differences. The results for the parameters of purity, precision, recall, and F-measure were higher in the AP direction as compared to the ML direction by 7.12%, 11.64%, 7.12%, and 9.50% respectively, with their differences statistically significant (p < 0.05) thus suggesting the related sensory systems affect majorly the AP direction sway as compared to the ML direction sway. Sway differences in the ML direction were lowest in the presence of the visual system. It was concluded that the effect of the visual system on the balance can be examined mostly by the ML sway while the proprioceptive and vestibular systems can be examined mostly by the AP direction sway. Full article

(This article belongs to the Special Issue Artificial Intelligence Developments for Medical Diagnosis and Monitoring)

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16 pages, 1516 KiB

Open AccessArticle

Impact of Using the Intelligent Physical Health Measurement System on Active Aging: A Survey in Taiwan

by Wen-Chou Chi, Wei-Chen Cheng, Ting-Hung Chen and Po-Jin Lin

Healthcare 2021, 9(9), 1142; https://doi.org/10.3390/healthcare9091142 - 1 Sep 2021

Cited by 2 | Viewed by 1989

Abstract

Background: In Taiwan, the Chiayi City Government and Industrial Development Bureau of the Ministry of Economic Affairs have worked together to promote smart health management in the community and encourage people to use the intelligent physical health measurement system (IPHMS) with Smart Body Health Measuring Machine. Volunteers help participants in the community to use the IPHMS to ensure that measurements are taken correctly. Objectives: This study aimed to explore volunteers’ satisfaction with using the IPHMS and the effects of the measurement service on the participants’ measurement behavior intention, and further explore the impact on their active aging. Methods: This study used a paper questionnaire to survey both the participants of the measurement service and the community volunteers from March to April 2021. A total of 180 valid responses were collected. Results: The sociodemographic information showed that the volunteers were mostly female, were aged over 61 years old, had received junior college education, had spent less than 3–6 years in community service, and had 6 months to 1 year of measured service experience. Additionally, the participants of the measurement service were mostly female, were aged over 61 years old, had received below middle school education, had spent less than 1–3 years in community service, and spent an average of 5 days in the community each week. Our results showed that the information quality (β = 0.352, p < 0.001) and system quality (β = 0.701, p < 0.001) had significant effects on volunteers’ satisfaction of using the IPHMS. Subjective norms had significant effects on participants’ perceived disease threat (β = 0.347, p < 0.001) and behavior intention of management service (β = 0.701, p < 0.001); furthermore, behavior intention had significant effects on their social participation for active aging (β = 0.430, p < 0.05). Conclusions: Improving the system and information quality is likely to improve volunteers’ satisfaction with the system. Active aging factors only affect social participation, which represents the measurement services promote for social interaction mostly. Full article

(This article belongs to the Special Issue Artificial Intelligence Developments for Medical Diagnosis and Monitoring)

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12 pages, 2046 KiB

Open AccessArticle

Detection of COVID-19 Patients from CT Scan and Chest X-ray Data Using Modified MobileNetV2 and LIME

by Md Manjurul Ahsan, Redwan Nazim, Zahed Siddique and Pedro Huebner

Healthcare 2021, 9(9), 1099; https://doi.org/10.3390/healthcare9091099 - 25 Aug 2021

Cited by 61 | Viewed by 8499

Abstract

The COVID-19 global pandemic caused by the widespread transmission of the novel coronavirus (SARS-CoV-2) has become one of modern history’s most challenging issues from a healthcare perspective. At its dawn, still without a vaccine, contagion containment strategies remained most effective in preventing the disease’s spread. Patient isolation has been primarily driven by the results of polymerase chain reaction (PCR) testing, but its initial reach was challenged by low availability and high cost, especially in developing countries. As a means of taking advantage of a preexisting infrastructure for respiratory disease diagnosis, researchers have proposed COVID-19 patient screening based on the results of Chest Computerized Tomography (CT) and Chest Radiographs (X-ray). When paired with artificial-intelligence- and deep-learning-based approaches for analysis, early studies have achieved a comparatively high accuracy in diagnosing the disease. Considering the opportunity to further explore these methods, we implement six different Deep Convolutional Neural Networks (Deep CNN) models—VGG16, MobileNetV2, InceptionResNetV2, ResNet50, ResNet101, and VGG19—and use a mixed dataset of CT and X-ray images to classify COVID-19 patients. Preliminary results showed that a modified MobileNetV2 model performs best with an accuracy of 95 ± 1.12% (AUC = 0.816). Notably, a high performance was also observed for the VGG16 model, outperforming several previously proposed models with an accuracy of 98.5 ± 1.19% on the X-ray dataset. Our findings are supported by recent works in the academic literature, which also uphold the higher performance of MobileNetV2 when X-ray, CT, and their mixed datasets are considered. Lastly, we further explain the process of feature extraction using Local Interpretable Model-Agnostic Explanations (LIME), which contributes to a better understanding of what features in CT/X-ray images characterize the onset of COVID-19. Full article

(This article belongs to the Special Issue Artificial Intelligence Developments for Medical Diagnosis and Monitoring)

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14 pages, 3870 KiB

Open AccessCommunication

Modeling and Predicting the Cell Migration Properties from Scratch Wound Healing Assay on Cisplatin-Resistant Ovarian Cancer Cell Lines Using Artificial Neural Network

by Entaz Bahar and Hyonok Yoon

Healthcare 2021, 9(7), 911; https://doi.org/10.3390/healthcare9070911 - 19 Jul 2021

Cited by 15 | Viewed by 6249

Abstract

The study of artificial neural networks (ANN) has undergone a tremendous revolution in recent years, boosted by deep learning tools. The presence of a greater number of learning tools and their applications, in particular, favors this revolution. However, there is a significant need to deal with the issue of implementing a systematic method during the development phase of the ANN to increase its performance. A multilayer feedforward neural network (FNN) was proposed in this paper to predict the cell migration assay on cisplatin-sensitive and cisplatin-resistant (CisR) ovarian cancer (OC) cell lines via scratch wound healing assay. An FNN training algorithm model was generated using the MATLAB fitting function in a MATLAB script to accomplish this task. The input parameters were types of cell lines, times, and wound area, and outputs were relative wound area, percentage of wound closure, and wound healing speed. In addition, we tested and compared the initial accuracy of various supervised learning classifier and support vector regression (SVR) algorithms. The proposed ANN model achieved good agreement with the experimental data and minimized error between the estimated and experimental values. The conclusions drawn demonstrate that the developed ANN model is a useful, accurate, fast, and inexpensive method to predict cancerous cell migration characteristics evaluated via scratch wound healing assay. Full article

(This article belongs to the Special Issue Artificial Intelligence Developments for Medical Diagnosis and Monitoring)

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14 pages, 1350 KiB

Open AccessArticle

Real-Time Monitoring Electronic Triage Tag System for Improving Survival Rate in Disaster-Induced Mass Casualty Incidents

by Ju Young Park

Healthcare 2021, 9(7), 877; https://doi.org/10.3390/healthcare9070877 - 13 Jul 2021

Cited by 5 | Viewed by 5268

Abstract

This study was conducted to contribute to active disaster response by developing internet of things (IoT)-based vital sign monitoring e-triage tag system to improve the survival rate at disaster mass casualty incidents fields. The model used in this study for developing the e-triage tag system is the rapid prototyping model (RAD). The process comprised six steps: analysis, design, development, evaluation, implementation, and simulation. As a result of detailed assessment of the system design and development by an expert group, areas with the highest score in the triage sensor evaluation were rated “very good”, with 5 points for continuous vital sign data delivery, portability, and robustness. In addition, ease of use, wearability, and electricity consumption were rated 4.8, 4.7, and 4.6 points, respectively. In the triage application evaluation, the speed and utility scored a perfect 5 points, and the reliability and expressiveness were rated 4.9 points and 4.8 points, respectively. This study will contribute significantly to increasing the survival rate via the development of a conceptual prehospital triage for field applications and e-triage tag system implementation. Full article

(This article belongs to the Special Issue Artificial Intelligence Developments for Medical Diagnosis and Monitoring)

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Review

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13 pages, 292 KiB

Open AccessReview

Juvenile Idiopathic Arthritis: A Review of Novel Diagnostic and Monitoring Technologies

by Amelia J. Garner, Reza Saatchi, Oliver Ward and Daniel P. Hawley

Healthcare 2021, 9(12), 1683; https://doi.org/10.3390/healthcare9121683 - 4 Dec 2021

Cited by 8 | Viewed by 4133

Abstract

Juvenile idiopathic arthritis (JIA) is the most common rheumatic disease of childhood and is characterized by an often insidious onset and a chronic relapsing–remitting course, once diagnosed. With successive flares of joint inflammation, joint damage accrues, often associated with pain and functional disability. The progressive nature and potential for chronic damage and disability caused by JIA emphasizes the critical need for a prompt and accurate diagnosis. This article provides a review of recent studies related to diagnosis, monitoring and management of JIA and outlines recent novel tools and techniques (infrared thermal imaging, three-dimensional imaging, accelerometry, artificial neural networks and fuzzy logic) which have demonstrated potential value in assessment and monitoring of JIA. The emergence of novel techniques to assist clinicians’ assessments for diagnosis and monitoring of JIA has demonstrated promise; however, further research is required to confirm their clinical utility. Full article

(This article belongs to the Special Issue Artificial Intelligence Developments for Medical Diagnosis and Monitoring)

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