Ehealth, Telemedicine and AI in Clinical Medicine

A special issue of Journal of Clinical Medicine (ISSN 2077-0383). This special issue belongs to the section "Epidemiology & Public Health".

Deadline for manuscript submissions: closed (15 July 2022) | Viewed by 48135

Special Issue Editor


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Guest Editor
1. Deputy Director of Nanomedicine Lab., Imagery & Therapeutics of Université de Franche Comté (UFC), Besançon, France
2. Research Team Leader of Health Systems Organization, Besançon, France
Interests: big data; artificial intelligence; deep learning; reinforcement learning; eHealth information processing; combinatorial optimization
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Special Issue Information

Dear Colleagues,

The importance of ehealth and telemedicine to citizens, patients, health providers, governments, and other stakeholders is rapidly increasing. They may improve access to services, reduce costs, and improve self-management. They may allow previously underserved populations to gain access to services. The health system is evolving, and so must its infrastructure and technology. As the importance of better health systems has increased among practitioners, healthcare needs intelligent systems that can deal with larger databases and provide better medical treatments. The rapid growth of the available digitised medical data has opened new challenges for the scientific research community. Artificial intelligence, big data and healthcare deal heavily with issues of complexity, efficacy, and societal impact. They are increasingly playing an important role in assisting physicians in various areas of medicine.

The Ehealth, Telemedicine and AI in Clinical Medicine Special Issue aims to collect the latest approaches and findings, as well as to discuss the current challenges of AI based ehealth and telemedicine applications. The focus is on the employment of AI in a big data context. We expect this Special Issue to increase the visibility and importance of this area, and contribute, in the short term, in pushing the state of the art.

Dr. Amir Hajjam El Hassani
Guest Editor

Manuscript Submission Information

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Keywords

  • Big data analytics
  • Machine learning, knowledge discovery and datamining
  • Pattern recognition in medicine
  • AI solutions for ambient assisted living, telemedicine and ehealth
  • Algorithms for decision support and therapy improvement
  • Clinical decision support systems (CDSSs)
  • Knowledge-based reasoning in biomedicine
  • Biomedical knowledge acquisition and management
  • Emerging architectures and technologies for ehealth and telemedicine
  • Medical device technologies

Published Papers (15 papers)

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23 pages, 23109 KiB  
Article
A Robustness Evaluation of Machine Learning Algorithms for ECG Myocardial Infarction Detection
by Mohamed Sraitih, Younes Jabrane and Amir Hajjam El Hassani
J. Clin. Med. 2022, 11(17), 4935; https://doi.org/10.3390/jcm11174935 - 23 Aug 2022
Cited by 5 | Viewed by 1995
Abstract
An automatic electrocardiogram (ECG) myocardial infarction detection system needs to satisfy several requirements to be efficient in real-world practice. These requirements, such as reliability, less complexity, and high performance in decision-making, remain very important in a realistic clinical environment. In this study, we [...] Read more.
An automatic electrocardiogram (ECG) myocardial infarction detection system needs to satisfy several requirements to be efficient in real-world practice. These requirements, such as reliability, less complexity, and high performance in decision-making, remain very important in a realistic clinical environment. In this study, we investigated an automatic ECG myocardial infarction detection system and presented a new approach to evaluate its robustness and durability performance in classifying the myocardial infarction (with no feature extraction) under different noise types. We employed three well-known supervised machine learning models: support vector machine (SVM), k-nearest neighbors (KNN), and random forest (RF), and tested the performance and robustness of these techniques in classifying normal (NOR) and myocardial infarction (MI) using real ECG records from the PTB database after normalization and segmentation of the data, with a suggested inter-patient paradigm separation as well as noise from the MIT-BIH noise stress test database (NSTDB). Finally, we measured four metrics: accuracy, precision, recall, and F1-score. The simulation revealed that all of the models performed well, with values of over 0.50 at lower SNR levels, in terms of all the metrics investigated against different types of noise, indicating that they are encouraging and acceptable under extreme noise situations are are thus considered sustainable and robust models for specific forms of noise. All of the methods tested could be used as ECG myocardial infarction detection tools in real-world practice under challenging circumstances. Full article
(This article belongs to the Special Issue Ehealth, Telemedicine and AI in Clinical Medicine)
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12 pages, 638 KiB  
Article
Identifying Patients with Familial Chylomicronemia Syndrome Using FCS Score-Based Data Mining Methods
by Ákos Németh, Mariann Harangi, Bálint Daróczy, Lilla Juhász, György Paragh and Péter Fülöp
J. Clin. Med. 2022, 11(15), 4311; https://doi.org/10.3390/jcm11154311 - 25 Jul 2022
Cited by 4 | Viewed by 1624
Abstract
Background: There are no exact data about the prevalence of familial chylomicronemia syndrome (FCS) in Central Europe. We aimed to identify FCS patients using either the FCS score proposed by Moulin et al. or with data mining, and assessed the diagnostic applicability of [...] Read more.
Background: There are no exact data about the prevalence of familial chylomicronemia syndrome (FCS) in Central Europe. We aimed to identify FCS patients using either the FCS score proposed by Moulin et al. or with data mining, and assessed the diagnostic applicability of the FCS score. Methods: Analyzing medical records of 1,342,124 patients, the FCS score of each patient was calculated. Based on the data of previously diagnosed FCS patients, we trained machine learning models to identify other features that may improve FCS score calculation. Results: We identified 26 patients with an FCS score of ≥10. From the trained models, boosting tree models and support vector machines performed the best for patient recognition with overall AUC above 0.95, while artificial neural networks accomplished above 0.8, indicating less efficacy. We identified laboratory features that can be considered as additions to the FCS score calculation. Conclusions: The estimated prevalence of FCS was 19.4 per million in our region, which exceeds the prevalence data of other European countries. Analysis of larger regional and country-wide data might increase the number of FCS cases. Although FCS score is an excellent tool in identifying potential FCS patients, consideration of some other features may improve its accuracy. Full article
(This article belongs to the Special Issue Ehealth, Telemedicine and AI in Clinical Medicine)
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16 pages, 2269 KiB  
Article
Traumatology: Adoption of the Sm@rtEven Application for the Remote Evaluation of Patients and Possible Medico-Legal Implications
by Giuseppe Basile, Riccardo Accetta, Susanna Marinelli, Riccardo D’Ambrosi, Quirino Alessandro Petrucci, Arianna Giorgetti, Alessandro Nuara, Simona Zaami and Stefania Fozzato
J. Clin. Med. 2022, 11(13), 3644; https://doi.org/10.3390/jcm11133644 - 23 Jun 2022
Cited by 19 | Viewed by 1420
Abstract
Telemedicine is the combination of technologies and activities that offer new remote ways of medical care. The Sm@rtEven application project is a remote assistance service that follows patients affected by lower limb fractures surgically treated at Galeazzi Orthopedic Institute (Milan, Italy). The Sm@rtEven [...] Read more.
Telemedicine is the combination of technologies and activities that offer new remote ways of medical care. The Sm@rtEven application project is a remote assistance service that follows patients affected by lower limb fractures surgically treated at Galeazzi Orthopedic Institute (Milan, Italy). The Sm@rtEven application aims to evaluate the clinical conditions of patients treated for lower limb fracture after discharge from hospital using remote follow-up (FU). The project is not a substitute for traditional clinical consultations but an additional tool for a more complete and prolonged view over time. The Sm@rtEven application is installed on patients’ smartphones and is used daily to communicate with healthcare personnel. In the first protocol, patients had to complete different tasks for 30 days, such as monitoring the load progression on the affected limb, the number of steps during the day, and body temperature and completing a questionnaire. A simplified protocol was proposed due to the pandemic and logistical issues. The revised protocol enrolled patients after more than 30 days of their operation, prioritized the rehabilitation phase, and required patients to use the app for fewer days. After an initial phase of correct use, a reduction in patient compliance was gradually reported in the first protocol. However, patient compliance in the second protocol remained high (96.25%) in the recording of all the required parameters. The Sm@rtEven application has proven to be a valuable tool for following patients remotely, especially during the pandemic. Telemedicine has the same value as traditional clinical evaluations, and it enables patients to be followed over long distances and over time, minimizing any discomfort. Full article
(This article belongs to the Special Issue Ehealth, Telemedicine and AI in Clinical Medicine)
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10 pages, 583 KiB  
Article
Using “Diraya” System as a Complementary Tool in Nursing Process Education: A Controlled Clinical Study
by Lourdes Díaz-Rodríguez, Keyla Vargas-Román, María del Mar Díaz-Rodríguez, Juan Carlos Sánchez-García, Antonio Liñán-González and Raquel Rodríguez-Blanque
J. Clin. Med. 2022, 11(10), 2771; https://doi.org/10.3390/jcm11102771 - 14 May 2022
Viewed by 1504
Abstract
Background: Healthcare has been revolutionized by the application of information and communication technologies. The implementation of electronic health record systems improves the quality and safety of patient healthcare. Nursing students who start learning the nursing process without contact with real patients experience difficulties [...] Read more.
Background: Healthcare has been revolutionized by the application of information and communication technologies. The implementation of electronic health record systems improves the quality and safety of patient healthcare. Nursing students who start learning the nursing process without contact with real patients experience difficulties in its correct application. Purpose: To compare the acquisition of skills and competencies in the nursing process by undergraduate nursing students between conventional learning with books and learning with an academic electronic health record system (Diraya). Methods: A controlled experimental study was conducted and included 379 students with a mean age of 20.54 ± 5.09 years, enrolled in the “Nursing Process and Basic Care” degree course at the School of Health Sciences in Granada. All participants gave their informed consent and were allocated by convenience sampling to a control group (n = 187; 21.20 ± 5.77 years) or an experimental group (n = 192, 19.91 ± 4.24 years). Findings: The experimental and control groups did not differ in sex distribution (p = 0.20), mean age (p = 0.01), or previous knowledge of the nursing process (p = 0.96). The groups did not significantly differ in multi-choice test results on the acquisition of theoretical knowledge (p = 0.13). However, the experimental group scored higher on clinical case planning (9.47 ± 0.80 vs. 8.95 ± 1.17; p < 0.001), took less time to complete it (46.9 ± 8.76 min vs. 82.66 ± 13.14 min; p < 0.001), and needed fewer autonomous learning hours to prepare for the final examination (2.26 ± 2.41 vs. 9.58 ± 3.83; p < 0.001). Satisfaction with the program and the rating of its quality were generally higher in the experimental group, while greater difficulty with most phases of the nursing process was reported by the control group. Conclusions: The academic electronic health record system “Diraya” is a useful tool to improve the learning and implementation of the nursing process by undergraduate nursing students. Full article
(This article belongs to the Special Issue Ehealth, Telemedicine and AI in Clinical Medicine)
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19 pages, 2916 KiB  
Article
Prediction of Poststroke Depression Based on the Outcomes of Machine Learning Algorithms
by Yeong Hwan Ryu, Seo Young Kim, Tae Uk Kim, Seong Jae Lee, Soo Jun Park, Ho-Youl Jung and Jung Keun Hyun
J. Clin. Med. 2022, 11(8), 2264; https://doi.org/10.3390/jcm11082264 - 18 Apr 2022
Cited by 11 | Viewed by 2678
Abstract
Poststroke depression (PSD) is a major psychiatric disorder that develops after stroke; however, whether PSD treatment improves cognitive and functional impairments is not clearly understood. We reviewed data from 31 subjects with PSD and 34 age-matched controls without PSD; all subjects underwent neurological, [...] Read more.
Poststroke depression (PSD) is a major psychiatric disorder that develops after stroke; however, whether PSD treatment improves cognitive and functional impairments is not clearly understood. We reviewed data from 31 subjects with PSD and 34 age-matched controls without PSD; all subjects underwent neurological, cognitive, and functional assessments, including the National Institutes of Health Stroke Scale (NIHSS), the Korean version of the Mini-Mental Status Examination (K-MMSE), computerized neurocognitive test (CNT), the Korean version of the Modified Barthel Index (K-MBI), and functional independence measure (FIM) at admission to the rehabilitation unit in the subacute stage following stroke and 4 weeks after initial assessments. Machine learning methods, such as support vector machine, k-nearest neighbors, random forest, voting ensemble models, and statistical analysis using logistic regression were performed. PSD was successfully predicted using a support vector machine with a radial basis function kernel function (area under curve (AUC) = 0.711, accuracy = 0.700). PSD prognoses could be predicted using a support vector machine linear algorithm (AUC = 0.830, accuracy = 0.771). The statistical method did not have a better AUC than that of machine learning algorithms. We concluded that the occurrence and prognosis of PSD in stroke patients can be predicted effectively based on patients’ cognitive and functional statuses using machine learning algorithms. Full article
(This article belongs to the Special Issue Ehealth, Telemedicine and AI in Clinical Medicine)
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12 pages, 1160 KiB  
Article
Early Virtual-Reality-Based Home Rehabilitation after Total Hip Arthroplasty: A Randomized Controlled Trial
by Edoardo Fascio, Jacopo Antonino Vitale, Paolo Sirtori, Giuseppe Peretti, Giuseppe Banfi and Laura Mangiavini
J. Clin. Med. 2022, 11(7), 1766; https://doi.org/10.3390/jcm11071766 - 22 Mar 2022
Cited by 9 | Viewed by 2856
Abstract
The benefits of early virtual-reality-based home rehabilitation following total hip arthroplasty (THA) have not yet been assessed. The aim of this randomized controlled study was to compare the efficacy of early rehabilitation via the Virtual Reality Rehabilitation System (VRRS) versus traditional rehabilitation in [...] Read more.
The benefits of early virtual-reality-based home rehabilitation following total hip arthroplasty (THA) have not yet been assessed. The aim of this randomized controlled study was to compare the efficacy of early rehabilitation via the Virtual Reality Rehabilitation System (VRRS) versus traditional rehabilitation in improving functional outcomes after THA. Subjects were randomized either to an experimental (VRRS; n = 21) or a control group (control; n = 22). All participants were invited to perform a daily home exercise program for rehabilitation after THA with different administration methods—namely, an illustrated booklet for the control group and a tablet with wearable sensors for the VRRS group. The primary outcome was the hip disability (HOOS JR). Secondary outcomes were the level of independence and the degree of global perceived effect of the rehabilitation program (GPE). Outcomes were measured before surgery (T0) and at the 4th (T1), 7th (T2), and 15th (T3) day after surgery. Mixed-model ANOVA showed no significant group effect but a significant effect of time for all variables (p < 0.001); no differences were observed in HOOS JR between VRRS and the control at T0, T1, T2, or T3. Further, no differences in the level of independence were found between VRRS and the control, whereas the GPE was higher at T3 in VRSS compared to the control (4.76 ± 0.43 vs. 3.96 ± 0.65; p < 0.001). Virtual-reality-based home rehabilitation resulted in similar improvements in functional outcomes with a better GPE compared to the traditional rehabilitation program following THA. The application of new technologies could offer novel possibilities for service delivery in rehabilitation. Full article
(This article belongs to the Special Issue Ehealth, Telemedicine and AI in Clinical Medicine)
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21 pages, 3548 KiB  
Article
Organizational E-Readiness for the Digital Transformation of Primary Healthcare Providers during the COVID-19 Pandemic in Poland
by Agnieszka Kruszyńska-Fischbach, Sylwia Sysko-Romańczuk, Mateusz Rafalik, Renata Walczak and Magdalena Kludacz-Alessandri
J. Clin. Med. 2022, 11(1), 133; https://doi.org/10.3390/jcm11010133 - 27 Dec 2021
Cited by 15 | Viewed by 4132
Abstract
The COVID-19 pandemic has forced many countries to implement a variety of restrictive measures to prevent it from spreading more widely, including the introduction of medical teleconsultations and the use of various tools in the field of inpatient telemedicine care. Digital technologies provide [...] Read more.
The COVID-19 pandemic has forced many countries to implement a variety of restrictive measures to prevent it from spreading more widely, including the introduction of medical teleconsultations and the use of various tools in the field of inpatient telemedicine care. Digital technologies provide a wide range of treatment options for patients, and at the same time pose a number of organizational challenges for medical entities. Therefore, the question arises of whether organizations are ready to use modern telemedicine tools during the COVID-19 pandemic. The aim of this article is to examine two factors that impact the level of organizational e-readiness for digital transformation in Polish primary healthcare providers (PHC). The first factor comprises operational capabilities, which are the sum of valuable, scarce, unique, and irreplaceable resources and the ability to use them. The second factor comprises technological capabilities, which determine the adoption and usage of innovative technologies. Contrary to the commonly analyzed impacts of technology on operational capabilities, we state the reverse hypothesis. The verification confirms the significant influence of operational capabilities on technological capabilities. The research is conducted using a questionnaire covering organizational e-readiness for digital transformation prepared by the authors. Out of the 32 items examined, four are related to the operational capabilities and four to the technological capabilities. The result of our evaluation shows that: (i) a basic set of four variables can effectively measure the dimensions of OC, namely the degree of agility, level of process integration, quality of resources, and quality of cooperation; (ii) a basic set of three variables can effectively measure the dimensions of TC, namely adoption and usage of technologies, customer interaction, and process automation; (iii) the empirical results show that OC is on a higher level than TC in Polish PHCs; (iv) the assessment of the relationship between OC and TC reveals a significant influence of operational capabilities on technological capabilities with a structural coefficient of 0.697. We recommend increasing the level of technological capability in PHC providers in order to improve the contact between patients and general practitioners (GPs) via telemedicine in lockdown conditions. Full article
(This article belongs to the Special Issue Ehealth, Telemedicine and AI in Clinical Medicine)
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21 pages, 1889 KiB  
Article
Reasons for Utilizing Telemedicine during and after the COVID-19 Pandemic: An Internet-Based International Study
by Arriel Benis, Maxim Banker, David Pinkasovich, Mark Kirin, Bat-el Yoshai, Raquel Benchoam-Ravid, Shai Ashkenazi and Abraham Seidmann
J. Clin. Med. 2021, 10(23), 5519; https://doi.org/10.3390/jcm10235519 - 25 Nov 2021
Cited by 12 | Viewed by 4932
Abstract
The COVID-19 pandemic challenges healthcare services. Concomitantly, this pandemic had a stimulating effect on technological expansions related to telehealth and telemedicine. We sought to elucidate the principal patients’ reasons for using telemedicine during the COVID-19 pandemic and the propensity to use it thereafter. [...] Read more.
The COVID-19 pandemic challenges healthcare services. Concomitantly, this pandemic had a stimulating effect on technological expansions related to telehealth and telemedicine. We sought to elucidate the principal patients’ reasons for using telemedicine during the COVID-19 pandemic and the propensity to use it thereafter. Our primary objective was to identify the reasons of the survey participants’ disparate attitudes toward the use of telemedicine. We performed an online, multilingual 30-question survey for 14 days during March–April 2021, focusing on the perception and usage of telemedicine and their intent to use it after the pandemic. We analyzed the data to identify the attributes influencing the intent to use telemedicine and built decision trees to highlight the most important related variables. We examined 473 answers: 272 from Israel, 87 from Uruguay, and 114 worldwide. Most participants were women (64.6%), married (63.8%) with 1–2 children (52.9%), and living in urban areas (84.6%). Only a third of the participants intended to continue using telemedicine after the COVID-19 pandemic. Our main findings are that an expected substitution effect, technical proficiency, reduced queueing times, and peer experience are the four major factors in the overall adoption of telemedicine. Specifically, (1) for most participants, the major factor influencing their telemedicine usage is the implicit expectation that such a visit will be a full substitute for an in-person appointment; (2) another factor affecting telemedicine usage by patients is their overall technical proficiency and comfort level in the use of common web-based tools, such as social media, while seeking relevant medical information; (3) time saving as telemedicine can allow for asynchronous communications, thereby reducing physical travel and queuing times at the clinic; and finally (4) some participants have also indicated that telemedicine seems more attractive to them after watching family and friends (peer experience) use it successfully. Full article
(This article belongs to the Special Issue Ehealth, Telemedicine and AI in Clinical Medicine)
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22 pages, 4428 KiB  
Article
An Automated System for ECG Arrhythmia Detection Using Machine Learning Techniques
by Mohamed Sraitih, Younes Jabrane and Amir Hajjam El Hassani
J. Clin. Med. 2021, 10(22), 5450; https://doi.org/10.3390/jcm10225450 - 22 Nov 2021
Cited by 42 | Viewed by 3634
Abstract
The new advances in multiple types of devices and machine learning models provide opportunities for practical automatic computer-aided diagnosis (CAD) systems for ECG classification methods to be practicable in an actual clinical environment. This imposes the requirements for the ECG arrhythmia classification methods [...] Read more.
The new advances in multiple types of devices and machine learning models provide opportunities for practical automatic computer-aided diagnosis (CAD) systems for ECG classification methods to be practicable in an actual clinical environment. This imposes the requirements for the ECG arrhythmia classification methods that are inter-patient. We aim in this paper to design and investigate an automatic classification system using a new comprehensive ECG database inter-patient paradigm separation to improve the minority arrhythmical classes detection without performing any features extraction. We investigated four supervised machine learning models: support vector machine (SVM), k-nearest neighbors (KNN), Random Forest (RF), and the ensemble of these three methods. We test the performance of these techniques in classifying: Normal beat (NOR), Left Bundle Branch Block Beat (LBBB), Right Bundle Branch Block Beat (RBBB), Premature Atrial Contraction (PAC), and Premature Ventricular Contraction (PVC), using inter-patient real ECG records from MIT-DB after segmentation and normalization of the data, and measuring four metrics: accuracy, precision, recall, and f1-score. The experimental results emphasized that with applying no complicated data pre-processing or feature engineering methods, the SVM classifier outperforms the other methods using our proposed inter-patient paradigm, in terms of all metrics used in experiments, achieving an accuracy of 0.83 and in terms of computational cost, which remains a very important factor in implementing classification models for ECG arrhythmia. This method is more realistic in a clinical environment, where varieties of ECG signals are collected from different patients. Full article
(This article belongs to the Special Issue Ehealth, Telemedicine and AI in Clinical Medicine)
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14 pages, 654 KiB  
Article
Polish Nurses’ Attitude to e-Health Solutions and Self-Assessment of Their IT Competence
by Anna Bartosiewicz, Joanna Burzyńska and Paweł Januszewicz
J. Clin. Med. 2021, 10(20), 4799; https://doi.org/10.3390/jcm10204799 - 19 Oct 2021
Cited by 11 | Viewed by 2715
Abstract
In many countries, the implementation and dissemination of e-services for healthcare systems are important aspects of projects and strategies, as they contribute to significantly improving the access to such a system. The aim of the study is to analyze nurses’ opinions on the [...] Read more.
In many countries, the implementation and dissemination of e-services for healthcare systems are important aspects of projects and strategies, as they contribute to significantly improving the access to such a system. The aim of the study is to analyze nurses’ opinions on the application of the e-health solutions at work and the self-assessment of their IT competence. A linear stepwise regression allowed for the visualization of independent variables significantly influencing considerably the level of IT competency. Reduced IT competency was found in the group of nurses who rated the impact of the Internet and the new technologies as lower on the health care and general lives of modern people (β = 0.203; p < 0.0001), recommended e-health solutions to a lesser extent (β = 0.175; p < 0.0001), rated e-health solutions lower in relation to the patient (β = 0.149; p < 0.0001), and were older in age (β = 0.095; p = 0.0032). IT competence has become an indispensable requirement for nurses in fulfilling their professional roles. The quality of using new technologies in the work of nurses depends on their IT competence. Full article
(This article belongs to the Special Issue Ehealth, Telemedicine and AI in Clinical Medicine)
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14 pages, 3931 KiB  
Article
Using Deep Neural Networks for Predicting Age and Sex in Healthy Adult Chest Radiographs
by Chung-Yi Yang, Yi-Ju Pan, Yen Chou, Chia-Jung Yang, Ching-Chung Kao, Kuan-Chieh Huang, Jing-Shan Chang, Hung-Chieh Chen and Kuei-Hong Kuo
J. Clin. Med. 2021, 10(19), 4431; https://doi.org/10.3390/jcm10194431 - 27 Sep 2021
Cited by 15 | Viewed by 2918
Abstract
Background: The performance of chest radiography-based age and sex prediction has not been well validated. We used a deep learning model to predict the age and sex of healthy adults based on chest radiographs (CXRs). Methods: In this retrospective study, 66,643 CXRs of [...] Read more.
Background: The performance of chest radiography-based age and sex prediction has not been well validated. We used a deep learning model to predict the age and sex of healthy adults based on chest radiographs (CXRs). Methods: In this retrospective study, 66,643 CXRs of 47,060 healthy adults were used for model training and testing. In total, 47,060 individuals (mean age ± standard deviation, 38.7 ± 11.9 years; 22,144 males) were included. By using chronological ages as references, mean absolute error (MAE), root mean square error (RMSE), and Pearson’s correlation coefficient were used to assess the model performance. Summarized class activation maps were used to highlight the activated anatomical regions. The area under the curve (AUC) was used to examine the validity for sex prediction. Results: When model predictions were compared with the chronological ages, the MAE was 2.1 years, RMSE was 2.8 years, and Pearson’s correlation coefficient was 0.97 (p < 0.001). Cervical, thoracic spines, first ribs, aortic arch, heart, rib cage, and soft tissue of thorax and flank seemed to be the most crucial activated regions in the age prediction model. The sex prediction model demonstrated an AUC of >0.99. Conclusion: Deep learning can accurately estimate age and sex based on CXRs. Full article
(This article belongs to the Special Issue Ehealth, Telemedicine and AI in Clinical Medicine)
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10 pages, 1649 KiB  
Article
Patients’ Habits and the Role of Pharmacists and Telemedicine as Elements of a Modern Health Care System during the COVID-19 Pandemic
by Patrycja Grosman-Dziewiszek, Benita Wiatrak, Izabela Jęśkowiak and Adam Szeląg
J. Clin. Med. 2021, 10(18), 4211; https://doi.org/10.3390/jcm10184211 - 17 Sep 2021
Cited by 9 | Viewed by 2632
Abstract
Aims/Introduction: The Polish government introduced the epidemic on 20 March 2020, after The World Health Organization (WHO) announced the new coronavirus disease (COVID-19) in January 2020. Patients’ access to specialist clinics and family medicine clinics was limited. In this situation, pharmacists were likely [...] Read more.
Aims/Introduction: The Polish government introduced the epidemic on 20 March 2020, after The World Health Organization (WHO) announced the new coronavirus disease (COVID-19) in January 2020. Patients’ access to specialist clinics and family medicine clinics was limited. In this situation, pharmacists were likely the first option for patient’s health information. On 18 March 2020, the National Health Fund issued modifications that increased the accessibility to primary health care such as telemedicine. The development of e-health in Poland during the COVID-19 pandemic included the implementation of electronic medical records (EDM), telemedicine development, e-prescription, and e-referrals implementation. We investigated this emergency’s effect on patients’ health habits, access to healthcare, and attitude to vaccination. Materials and methods: An anonymous study in the form of an electronic and paper questionnaire was conducted in March 2021 among 926 pharmacies patients in Poland. The content of the questionnaire included access to medical care, performing preventive examinations, implementation of e-prescriptions, patient satisfaction with telepathing, pharmaceutical care, and COVID-19 vaccination. Results: During the COVID-19 pandemic, 456 (49.2%) patients experienced worse access to a doctor. On the other hand, 483 (52.2%) patients did not perform preventive examinations during the COVID-19 pandemic. Almost half of the patients (45.4% (n = 420)) were not satisfied with the teleconsultation visit to the doctor. A total of 90% (n = 833) of the respondents do not need help in making an appointment with a doctor and buying medications prescribed by a doctor in the form of an e-prescription. In the absence of access to medical consultation, 38.2% (n = 354) of respondents choose the Internet as a source of medical advice. However, in the absence of contact with a doctor, 229 persons (24.7%) who took part in the survey consulted a pharmacist. In addition, 239 persons (25.8%) used pharmacist advice more often during the COVID-19 pandemic than before its outbreak on 12 March 2020. Moreover, 457 (49.4%) respondents are satisfied with the advice provided by pharmacists, and even 439 patients of pharmacies (47.4%) expect an increase in the scope of pharmaceutical care in the future, including medical advice provided by pharmacists. Most of the respondents, 45.6% (n = 422), want to be vaccinated in a hospital or clinic, but at the same time, for a slightly smaller number of people, 44.6% (n = 413), it has no meaning where they are will be vaccinated against COVID-19. Conclusions: Telemedicine is appreciated by patients but also has some limitations. The COVID-19 pandemic is the chance for telemedicine to transform from implementations to a routine healthcare system structure. However, some patients still need face-to-face contact with the doctor or pharmacist. Pharmacists are essential contributors to public health and play an essential role during the COVID-19 pandemic. Integration of pharmaceutical care with public health care and strong growth in the professional group of pharmacists may have optimized patient care. Full article
(This article belongs to the Special Issue Ehealth, Telemedicine and AI in Clinical Medicine)
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Review

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19 pages, 2183 KiB  
Review
Applications of Wearable Technology in a Real-Life Setting in People with Knee Osteoarthritis: A Systematic Scoping Review
by Tomasz Cudejko, Kate Button, Jake Willott and Mohammad Al-Amri
J. Clin. Med. 2021, 10(23), 5645; https://doi.org/10.3390/jcm10235645 - 30 Nov 2021
Cited by 16 | Viewed by 5283
Abstract
With the growing number of people affected by osteoarthritis, wearable technology may enable the provision of care outside a traditional clinical setting and thus transform how healthcare is delivered for this patient group. Here, we mapped the available empirical evidence on the utilization [...] Read more.
With the growing number of people affected by osteoarthritis, wearable technology may enable the provision of care outside a traditional clinical setting and thus transform how healthcare is delivered for this patient group. Here, we mapped the available empirical evidence on the utilization of wearable technology in a real-world setting in people with knee osteoarthritis. From an analysis of 68 studies, we found that the use of accelerometers for physical activity assessment is the most prevalent mode of use of wearable technology in this population. We identify low technical complexity and cost, ability to connect with a healthcare professional, and consistency in the analysis of the data as the most critical facilitators for the feasibility of using wearable technology in a real-world setting. To fully realize the clinical potential of wearable technology for people with knee osteoarthritis, this review highlights the need for more research employing wearables for information sharing and treatment, increased inter-study consistency through standardization and improved reporting, and increased representation of vulnerable populations. Full article
(This article belongs to the Special Issue Ehealth, Telemedicine and AI in Clinical Medicine)
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12 pages, 824 KiB  
Review
The Development of Electronic Health and Artificial Intelligence in Surgery after the SARS-CoV-2 Pandemic—A Scoping Review
by Stephanie Taha-Mehlitz, Ahmad Hendie and Anas Taha
J. Clin. Med. 2021, 10(20), 4789; https://doi.org/10.3390/jcm10204789 - 19 Oct 2021
Cited by 3 | Viewed by 2101
Abstract
Background: SARS-CoV-2 has significantly transformed the healthcare environment, and it has triggered the development of electronic health and artificial intelligence mechanisms, for instance. In this overview, we concentrated on enhancing the two concepts in surgery after the pandemic, and we examined the factors [...] Read more.
Background: SARS-CoV-2 has significantly transformed the healthcare environment, and it has triggered the development of electronic health and artificial intelligence mechanisms, for instance. In this overview, we concentrated on enhancing the two concepts in surgery after the pandemic, and we examined the factors on a global scale. Objective: The primary goal of this scoping review is to elaborate on how surgeons have used eHealth and AI before; during; and after the current global pandemic. More specifically, this review focuses on the empowerment of the concepts of electronic health and artificial intelligence after the pandemic; which mainly depend on the efforts of countries to advance the notions of surgery. Design: The use of an online search engine was the most applied method. The publication years of all the studies included in the study ranged from 2013 to 2021. Out of the reviewed studies; forty-four qualified for inclusion in the review. Discussion: We evaluated the prevalence of the concepts in different continents such as the United States; Europe; Asia; the Middle East; and Africa. Our research reveals that the success of eHealth and artificial intelligence adoption primarily depends on the efforts of countries to advance the notions in surgery. Conclusions: The study’s primary limitation is insufficient information on eHealth and artificial intelligence concepts; particularly in developing nations. Future research should focus on establishing methods of handling eHealth and AI challenges around confidentiality and data security. Full article
(This article belongs to the Special Issue Ehealth, Telemedicine and AI in Clinical Medicine)
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25 pages, 416 KiB  
Systematic Review
Need for Inclusive Consideration of Transgender and Gender Diverse People in E-Health Services: A Systematic Review
by Janis Renner, Lars Täuber and Timo O. Nieder
J. Clin. Med. 2022, 11(4), 1090; https://doi.org/10.3390/jcm11041090 - 18 Feb 2022
Cited by 7 | Viewed by 4846
Abstract
Many transgender and gender diverse (TGD) people use the internet to find ways out of isolation, network, and share information on health-related topics. Thus, e-health services could reduce the health burden of TGD people and facilitate access to health care. Following the PRISMA [...] Read more.
Many transgender and gender diverse (TGD) people use the internet to find ways out of isolation, network, and share information on health-related topics. Thus, e-health services could reduce the health burden of TGD people and facilitate access to health care. Following the PRISMA guidelines, we conducted a systematic review on e-health approaches that could improve trans health care (i.e., services directly for TGD people or training programs for health care professionals, HCPs) and their effectiveness, acceptability, and feasibility. We searched PubMed, Web of Science, and PubPsych databases for publications from January 2000 to June 2021 with final updates before publication. The systematic review identified e-health services across 27 studies from 8 different countries. Few studies evaluated e-health services exclusively for TGD people. However, use of an e-health service was found to be effective and beneficial: TGD people improved in health-related outcomes, and HCPs improved in professional expertise. Service users find e-health services helpful and easy to integrate into their daily lives. Recommendations for further development of e-health services in trans health care are provided. In the future, given the rapidly evolving e-health research and care field, new treatment approaches for TGD people should be subject to ongoing evaluation and development. Full article
(This article belongs to the Special Issue Ehealth, Telemedicine and AI in Clinical Medicine)
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