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AI Technologies for eHealth and mHealth
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AI Technologies for eHealth and mHealth

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 10 March 2025 | Viewed by 9279

Special Issue Editors

Dr. István Vassányi

E-Mail Website
Guest Editor
Medical Informatics Research & Development Center, University of Pannonia, 8200 Veszprém, Hungary
Interests: health informatics; data modeling; data analysis; expert systems
Dr. István Kósa

E-Mail Website
Guest Editor
Department of Preventive Medicine, University of Szeged, 6700 Szeged, Hungary
Interests: medical informatics; m-health; telemedicine
Dr. László Balkányi

E-Mail Website
Guest Editor
Medical Informatics Research & Development Center, University of Pannonia, 8200 Veszprém, Hungary
Interests: medical knowledge management; coding systems

Special Issue Information

Dear Colleagues,

The ongoing ageing of modern societies will lead to a growing portion of society relying on the financial support of a shrinking workforce. At the same time, the sustainability of public health services is expected to be further affected by the application of new sophisticated and expensive imaging and diagnostic tools and processes developed for the care of widespread chronic diseases such as diabetes, cancer, and cardiovascular and neurological diseases. These trends will inevitably necessitate changes in the current health care system even in the short run, and the only realistic answer to this challenge is preventive self-management supported by ambient assisted living devices and IoT, and modern information technology relying on artificial intelligence. It is the objective of this Special Issue to give a cross-section of current research related to all applications of AI in the eHealth and mHealth domain, with an emphasis on the following fields:

  • Machine learning methods used for feature extraction, diagnostics and personalized treatment recommendations. Within this field, a special highlight is personalized and mobile lifestyle counselling for chronic disease prevention and management.
  • Medical expert systems using traditional rule-based or case-based reasoning, or evolutionary/swarm-intelligence algorithms, assisting medical professionals in research or daily care.
  • Natural language processing methods for analyzing current and legacy textual records and implementing intelligent chat support for patients.
  • This Special Issue welcomes original research articles and review papers. Case studies and reports on controlled clinical trials are especially welcome.

Dr. István Vassányi
Dr. István Kósa
Dr. László Balkányi
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. Applied Sciences 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 2400 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

  • eHealth
  • ambient assisted living
  • personalized care
  • machine learning
  • medical expert systems

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Published Papers (7 papers)

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Research

16 pages, 3501 KiB  
Article
Diabetic Retinopathy Detection Using Convolutional Neural Networks with Background Removal, and Data Augmentation
by Chaichana Suedumrong, Suriya Phongmoo, Tachanat Akarajaka and Komgrit Leksakul
Appl. Sci. 2024, 14(19), 8823; https://doi.org/10.3390/app14198823 - 30 Sep 2024
Viewed by 1016
Abstract
Diabetic retinopathy (DR) is a potentially blinding complication affecting individuals with diabetes, where early diagnosis and treatment are crucial to preventing vision loss. Recent advances in deep learning have shown promise in automating DR diagnosis, offering faster, more reliable, and cost-effective solutions. Our [...] Read more.
Diabetic retinopathy (DR) is a potentially blinding complication affecting individuals with diabetes, where early diagnosis and treatment are crucial to preventing vision loss. Recent advances in deep learning have shown promise in automating DR diagnosis, offering faster, more reliable, and cost-effective solutions. Our study employed convolutional neural networks (CNNs) to classify the severity of DR using retinal images from the EyePACS dataset, which includes 35,155 images categorized into five classes. Building on previous research that often classified DR into two classes, such as no DR and varying levels of DR, we found that while these studies typically used models like Inception V3, VGGNet, and ResNet, they focused on simplifying the diagnostic process by reducing the number of classes. However, our approach utilized a smaller, more flexible CNN architecture, allowing for a more detailed classification into five stages of DR. We employed various image preprocessing techniques, including grayscale conversion, background removal, and data augmentation, with our findings indicating that background removal significantly enhanced model performance, achieving a validation accuracy of 90.60%. This underscores the importance of sophisticated data preprocessing in medical imaging, and our study contributes to the ongoing development of automated DR diagnosis, potentially easing the burden on healthcare systems and improving patient outcomes. Full article
(This article belongs to the Special Issue AI Technologies for eHealth and mHealth)
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14 pages, 4136 KiB  
Article
Accurate and Reliable Food Nutrition Estimation Based on Uncertainty-Driven Deep Learning Model
by DaeHan Ahn
Appl. Sci. 2024, 14(18), 8575; https://doi.org/10.3390/app14188575 - 23 Sep 2024
Viewed by 930
Abstract
Mobile Near-Infrared Spectroscopy (NIR) devices are increasingly being used to estimate food nutrients, offering substantial benefits to individuals with diabetes and obesity, who are particularly sensitive to food intake. However, most existing solutions prioritize accuracy, often neglecting to ensure reliability. This oversight can [...] Read more.
Mobile Near-Infrared Spectroscopy (NIR) devices are increasingly being used to estimate food nutrients, offering substantial benefits to individuals with diabetes and obesity, who are particularly sensitive to food intake. However, most existing solutions prioritize accuracy, often neglecting to ensure reliability. This oversight can endanger individuals sensitive to specific foods, as it may lead to significant errors in nutrient estimation. To address these issues, we propose an accurate and reliable food nutrient prediction model. Our model introduces a loss function designed to minimize prediction errors by leveraging the relationships among food nutrients. Additionally, we developed a method that enables the model to autonomously estimate its own uncertainty based on the loss, reducing the risk to users. Comparative experiments demonstrate that our model achieves superior performance, with an R2 value of 0.98 and an RMSE of 0.40, reflecting a 5–15% improvement over other models. The autonomous result rejection mechanism showing a 40.6% improvement further enhances robustness, particularly in handling uncertain predictions. These findings highlight the potential of our approach for precise and trustworthy nutritional assessments in real-world applications. Full article
(This article belongs to the Special Issue AI Technologies for eHealth and mHealth)
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20 pages, 4604 KiB  
Article
On-Edge Deployment of Vision Transformers for Medical Diagnostics Using the Kvasir-Capsule Dataset
by Dara Varam, Lujain Khalil and Tamer Shanableh
Appl. Sci. 2024, 14(18), 8115; https://doi.org/10.3390/app14188115 - 10 Sep 2024
Viewed by 854
Abstract
This paper aims to explore the possibility of utilizing vision transformers (ViTs) for on-edge medical diagnostics by experimenting with the Kvasir-Capsule image classification dataset, a large-scale image dataset of gastrointestinal diseases. Quantization techniques made available through TensorFlow Lite (TFLite), including post-training float-16 (F16) [...] Read more.
This paper aims to explore the possibility of utilizing vision transformers (ViTs) for on-edge medical diagnostics by experimenting with the Kvasir-Capsule image classification dataset, a large-scale image dataset of gastrointestinal diseases. Quantization techniques made available through TensorFlow Lite (TFLite), including post-training float-16 (F16) quantization and quantization-aware training (QAT), are applied to achieve reductions in model size, without compromising performance. The seven ViT models selected for this study are EfficientFormerV2S2, EfficientViT_B0, EfficientViT_M4, MobileViT_V2_050, MobileViT_V2_100, MobileViT_V2_175, and RepViT_M11. Three metrics are considered when analyzing a model: (i) F1-score, (ii) model size, and (iii) performance-to-size ratio, where performance is the F1-score and size is the model size in megabytes (MB). In terms of F1-score, we show that MobileViT_V2_175 with F16 quantization outperforms all other models with an F1-score of 0.9534. On the other hand, MobileViT_V2_050 trained using QAT was scaled down to a model size of 1.70 MB, making it the smallest model amongst the variations this paper examined. MobileViT_V2_050 also achieved the highest performance-to-size ratio of 41.25. Despite preferring smaller models for latency and memory concerns, medical diagnostics cannot afford poor-performing models. We conclude that MobileViT_V2_175 with F16 quantization is our best-performing model, with a small size of 27.47 MB, providing a benchmark for lightweight models on the Kvasir-Capsule dataset. Full article
(This article belongs to the Special Issue AI Technologies for eHealth and mHealth)
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17 pages, 3061 KiB  
Article
A Study on the Development of a Web Platform for Scalp Diagnosis Using EfficientNet
by Yea-Ju Jin, Yeon-Soo Park, Seong-Ho Kang, Dong-Hoon Kim and Ji-Yeoun Lee
Appl. Sci. 2024, 14(17), 7574; https://doi.org/10.3390/app14177574 - 27 Aug 2024
Viewed by 640
Abstract
Along with their physical health, modern people also need to manage the health of their scalp and hair due to changes in lifestyle habits, job stress, and environmental pollution. In this study, a machine learning model was developed to diagnose scalp conditions such [...] Read more.
Along with their physical health, modern people also need to manage the health of their scalp and hair due to changes in lifestyle habits, job stress, and environmental pollution. In this study, a machine learning model was developed to diagnose scalp conditions such as fine dandruff and perifollicular erythema. Then, transfer learning was conducted using EfficientNet-B0. A web platform that allows users to easily diagnose the condition of their scalp was also proposed. The results showed that the accuracy of the diagnosis model for fine dandruff and perifollicular erythema was 75% and 82%, respectively. It showed good performance in classifying normal, mild, moderate, and severe cases compared to previous studies. Finally, a fast and convenient web platform was developed where users can upload an image and immediately visualize their scalp condition, receive diagnostic results, and see similar cases and solutions. The analysis of user satisfaction indicates that this web application has achieved exceptional outcomes in terms of user satisfaction, garnering high evaluations for its usability, design effectiveness, and overall user experience. This setup enables users to easily check their scalp condition and is accessible to everyone, which is a significant advantage. This is expected to play a crucial role in contributing to global scalp health by advocating the benefits of the early detection and treatment of scalp-related conditions. Full article
(This article belongs to the Special Issue AI Technologies for eHealth and mHealth)
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16 pages, 4260 KiB  
Article
Convolution Neural Network Based Multi-Label Disease Detection Using Smartphone Captured Tongue Images
by Vibha Bhatnagar and Prashant P. Bansod
Appl. Sci. 2024, 14(10), 4208; https://doi.org/10.3390/app14104208 - 15 May 2024
Viewed by 1196
Abstract
Purpose: Tongue image analysis for disease diagnosis is an ancient, traditional, non-invasive diagnostic technique widely used by traditional medicine practitioners. Deep learning-based multi-label disease detection models have tremendous potential for clinical decision support systems because they facilitate preliminary diagnosis. Methods: In this work, [...] Read more.
Purpose: Tongue image analysis for disease diagnosis is an ancient, traditional, non-invasive diagnostic technique widely used by traditional medicine practitioners. Deep learning-based multi-label disease detection models have tremendous potential for clinical decision support systems because they facilitate preliminary diagnosis. Methods: In this work, we propose a multi-label disease detection pipeline where observation and analysis of tongue images captured and received via smartphones assist in predicting the health status of an individual. Subjects, who consult collaborating physicians, voluntarily provide all images. Images thus acquired are first and foremost classified either into a diseased or a normal category by a 5-fold cross-validation algorithm using a convolutional neural network (MobileNetV2) model for binary classification. Once it predicts the diseased label, the disease prediction algorithm based on DenseNet-121 uses the image to diagnose single or multiple disease labels. Results: The MobileNetV2 architecture-based disease detection model achieved an average accuracy of 93% in distinguishing between diseased and normal, healthy tongues, whereas the multilabel disease classification model produced more than 90% accurate results for the disease class labels considered, strongly indicating a successful outcome with the smartphone-captured image dataset. Conclusion: AI-based image analysis shows promising results, and an extensive dataset could provide further improvements to this approach. Experimenting with smartphone images opens a great opportunity to provide preliminary health status to individuals at remote locations as well, prior to further treatment and diagnosis, using the concept of telemedicine. Full article
(This article belongs to the Special Issue AI Technologies for eHealth and mHealth)
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15 pages, 6993 KiB  
Article
Methodology of Labeling According to 9 Criteria of DSM-5
by Geonju Lee, Dabin Park and Hayoung Oh
Appl. Sci. 2023, 13(18), 10481; https://doi.org/10.3390/app131810481 - 20 Sep 2023
Viewed by 1098
Abstract
Depression disorder is a disease that causes a deterioration of daily function and can induce thoughts of suicide. The Diagnostic and Statistical Manual of Mental Disorders Fifth Edition (DSM-5), which is the official reference of the American Psychiatry Association and is also used [...] Read more.
Depression disorder is a disease that causes a deterioration of daily function and can induce thoughts of suicide. The Diagnostic and Statistical Manual of Mental Disorders Fifth Edition (DSM-5), which is the official reference of the American Psychiatry Association and is also used in Korea to identify depressive disorders, sets nine criteria for diagnosing depressive disorders. The lack of counseling personnel, including psychiatrists, and negative social perceptions of depressive disorders prevent counselors from being treated for depressive disorders. Natural language processing-based artificial intelligence (AI) services such as chatbots can help fill this need, but labeled datasets are needed to train AI services. In this study we collected data from AI Hub wellness consultations and crawls of the Reddit website to augment and build word dictionaries and analyze morphemes using the Kind Korean Morpheme Analyzer and Word2Vec. The collected datasets were labeled based on word dictionaries built according to nine DSM-5 depressive disorder diagnostic criteria. Full article
(This article belongs to the Special Issue AI Technologies for eHealth and mHealth)
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19 pages, 6846 KiB  
Article
A-Tuning Ensemble Machine Learning Technique for Cerebral Stroke Prediction
by Meshrif Alruily, Sameh Abd El-Ghany, Ayman Mohamed Mostafa, Mohamed Ezz and A. A. Abd El-Aziz
Appl. Sci. 2023, 13(8), 5047; https://doi.org/10.3390/app13085047 - 18 Apr 2023
Cited by 11 | Viewed by 2810
Abstract
A cerebral stroke is a medical problem that occurs when the blood flowing to a section of the brain is suddenly cut off, causing damage to the brain. Brain cells gradually die because of interruptions in blood supply and other nutrients to the [...] Read more.
A cerebral stroke is a medical problem that occurs when the blood flowing to a section of the brain is suddenly cut off, causing damage to the brain. Brain cells gradually die because of interruptions in blood supply and other nutrients to the brain, resulting in disabilities, depending on the affected region. Early recognition and detection of symptoms can aid in the rapid treatment of strokes and result in better health by reducing the severity of a stroke episode. In this paper, the Random Forest (RF), Extreme Gradient Boosting (XGBoost), and light gradient-boosting machine (LightGBM) were used as machine learning (ML) algorithms for predicting the likelihood of a cerebral stroke by applying an open-access stroke prediction dataset. The stroke prediction dataset was pre-processed by handling missing values using the KNN imputer technique, eliminating outliers, applying the one-hot encoding method, and normalizing the features with different ranges of values. After data splitting, synthetic minority oversampling (SMO) was applied to balance the stroke samples and no-stroke classes. Furthermore, to fine-tune the hyper-parameters of the ML algorithm, we employed a random search technique that could achieve the best parameter values. After applying the tuning process, we stacked the parameters to a tuning ensemble RXLM that was analyzed and compared with traditional classifiers. The performance metrics after tuning the hyper-parameters achieved promising results with all ML algorithms. Full article
(This article belongs to the Special Issue AI Technologies for eHealth and mHealth)
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