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Diagnostics
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AI and Digital Health for Disease Diagnosis and Monitoring
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AI and Digital Health for Disease Diagnosis and Monitoring

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Machine Learning and Artificial Intelligence in Diagnostics".

Deadline for manuscript submissions: 31 March 2025 | Viewed by 3232

Special Issue Editors

Dr. Mohammad Ali Moni

E-Mail Website
Guest Editor
AI & Digital Health Technology, Artificial Intelligence and Cyber Futures Institute, Charles Sturt University, Bathurst, NSW 2795, Australia
Interests: artificial intelligence; computer vision; machine learning; deep-learning; medical imaging; medical image analysis; neuro imaging; digital health; data science; health informatics; clinical informatics; data mining; text mining; natural language processing; bioinformatics; systems biology; computational biology
Dr. AKM Azad

E-Mail Website
Guest Editor
Department of Mathematics and Statistics, College of Science, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia
Interests: bioinformatics; computational biology; systems biology; network biology; artificial intelligence; deep learning; drug repositioning
Dr. Hussain Mohammed Dipu Kabir

E-Mail Website
Guest Editor
1. Artificial Intelligence and Cyber Futures Institute, Charles Sturt University, Orange, NSW 2800, Australia
2. Rural Health Research Institute, Charles Sturt University, Orange, NSW 2800, Australia
Interests: artificial intelligence; uncertainty quantification; imbalanced data
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are pleased to invite you to contribute to our Special Issue, "Artificial Intelligence in Chronic Disease Monitoring and Prediction." Chronic diseases, such as cancer, cardiovascular disease, diabetes, and chronic respiratory conditions, pose significant long-term health challenges and economic burdens worldwide. The integration of Artificial Intelligence (AI) into healthcare has shown immense potential in revolutionizing the monitoring and prediction of these chronic conditions. By leveraging AI technologies, we can enhance early diagnosis, personalize treatment plans, and improve patient outcomes. This Special Issue aims to bring together cutting-edge research that explores the application of AI in chronic disease management, offering innovative solutions to these persistent health challenges.

This Special Issue aims to provide a comprehensive overview of the advancements and challenges in the application of AI for chronic disease monitoring and prediction. We seek to explore how AI techniques can be effectively utilized to improve the accuracy, efficiency, and scalability of chronic disease management. By aligning with the journal's focus on technological innovations and their impacts on health outcomes, this Special Issue will contribute to the ongoing discourse on the transformative potential of AI in healthcare. We encourage submissions that offer novel insights, propose new methodologies, and present real-world applications of AI in chronic disease contexts.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

  • AI-based diagnostic tools for chronic diseases;
  • Machine learning models for predicting disease progression;
  • Real-time monitoring systems for chronic disease patients;
  • AI-driven personalized treatment plans;
  • Integration of AI with wearable health technologies;
  • Big data analytics in chronic disease management;
  • AI applications in public health for chronic disease prevention.

We look forward to receiving insightful contributions.

Dr. Mohammad Ali Moni
Dr. AKM Azad
Dr. Hussain Mohammed Dipu Kabir
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. Diagnostics 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
  • chronic disease
  • disease monitoring
  • disease prediction
  • machine learning
  • personalized treatment
  • health technology
  • big data analytics
  • public health
  • wearable health devices

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (4 papers)

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Research

14 pages, 1684 KiB  
Article
Erroneous Classification and Coding as a Limitation for Big Data Analyses: Causes and Impacts Illustrated by the Diagnosis of Clavicle Injuries
by Robert Raché, Lara-Sophie Claudé, Marcus Vollmer, Lyubomir Haralambiev, Denis Gümbel, Axel Ekkernkamp, Martin Jordan, Stefan Schulz-Drost and Mustafa Sinan Bakir
Diagnostics 2025, 15(2), 131; https://doi.org/10.3390/diagnostics15020131 - 8 Jan 2025
Viewed by 310
Abstract
Background/Objectives: Clavicle injuries are common and seem to be frequently subject to diagnostic misclassification. The accurate identification of clavicle fractures is essential, particularly for registry and Big Data analyses. This study aims to assess the frequency of diagnostic errors in clavicle injury [...] Read more.
Background/Objectives: Clavicle injuries are common and seem to be frequently subject to diagnostic misclassification. The accurate identification of clavicle fractures is essential, particularly for registry and Big Data analyses. This study aims to assess the frequency of diagnostic errors in clavicle injury classifications. Methods: This retrospective study analyzed patient data from two Level 1 trauma centers, covering the period from 2008 to 2019. Included were cases with ICD-coded diagnoses of medial, midshaft, and lateral clavicle fractures, as well as sternoclavicular and acromioclavicular joint dislocations. Radiological images were re-evaluated, and discharge summaries, radiological reports, and billing codes were examined for diagnostic accuracy. Results: A total of 1503 patients were included, accounting for 1855 initial injury diagnoses. In contrast, 1846 were detected upon review. Initially, 14.4% of cases were coded as medial clavicle fractures, whereas only 5.2% were confirmed. The misclassification rate was 82.8% for initial medial fractures (p < 0.001), 42.5% for midshaft fractures (p < 0.001), and 34.2% for lateral fractures (p < 0.001). Billing codes and discharge summaries were the most error-prone categories, with error rates of 64% and 36% of all misclassified cases, respectively. Over three-quarters of the cases with discharge summary errors also exhibited errors in other categories, while billing errors co-occurred with other category errors in just over half of the cases (p < 0.001). The likelihood of radiological diagnostic error increased with the number of imaging modalities used, from 19.7% with a single modality to 30.5% with two and 40.7% with three. Conclusions: Our findings indicate that diagnostic misclassification of clavicle fractures is common, particularly between medial and midshaft fractures, often resulting from errors in multiple categories. Further prospective studies are needed, as accurate classification is foundational for the reliable application of Big Data and AI-based analyses in clinical research. Full article
(This article belongs to the Special Issue AI and Digital Health for Disease Diagnosis and Monitoring)
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18 pages, 3755 KiB  
Article
Combining Postural Sway Parameters and Machine Learning to Assess Biomechanical Risk Associated with Load-Lifting Activities
by Giuseppe Prisco, Maria Agnese Pirozzi, Antonella Santone, Mario Cesarelli, Fabrizio Esposito, Paolo Gargiulo, Francesco Amato and Leandro Donisi
Diagnostics 2025, 15(1), 105; https://doi.org/10.3390/diagnostics15010105 - 4 Jan 2025
Viewed by 605
Abstract
Background/Objectives: Long-term work-related musculoskeletal disorders are predominantly influenced by factors such as the duration, intensity, and repetitive nature of load lifting. Although traditional ergonomic assessment tools can be effective, they are often challenging and complex to apply due to the absence of [...] Read more.
Background/Objectives: Long-term work-related musculoskeletal disorders are predominantly influenced by factors such as the duration, intensity, and repetitive nature of load lifting. Although traditional ergonomic assessment tools can be effective, they are often challenging and complex to apply due to the absence of a streamlined, standardized framework. Recently, integrating wearable sensors with artificial intelligence has emerged as a promising approach to effectively monitor and mitigate biomechanical risks. This study aimed to evaluate the potential of machine learning models, trained on postural sway metrics derived from an inertial measurement unit (IMU) placed at the lumbar region, to classify risk levels associated with load lifting based on the Revised NIOSH Lifting Equation. Methods: To compute postural sway parameters, the IMU captured acceleration data in both anteroposterior and mediolateral directions, aligning closely with the body’s center of mass. Eight participants undertook two scenarios, each involving twenty consecutive lifting tasks. Eight machine learning classifiers were tested utilizing two validation strategies, with the Gradient Boost Tree algorithm achieving the highest accuracy and an Area under the ROC Curve of 91.2% and 94.5%, respectively. Additionally, feature importance analysis was conducted to identify the most influential sway parameters and directions. Results: The results indicate that the combination of sway metrics and the Gradient Boost model offers a feasible approach for predicting biomechanical risks in load lifting. Conclusions: Further studies with a broader participant pool and varied lifting conditions could enhance the applicability of this method in occupational ergonomics. Full article
(This article belongs to the Special Issue AI and Digital Health for Disease Diagnosis and Monitoring)
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19 pages, 2110 KiB  
Article
Transforming Cancer Classification: The Role of Advanced Gene Selection
by Abrar Yaqoob, Mushtaq Ahmad Mir, G. V. V. Jagannadha Rao and Ghanshyam G. Tejani
Diagnostics 2024, 14(23), 2632; https://doi.org/10.3390/diagnostics14232632 - 22 Nov 2024
Cited by 1 | Viewed by 655
Abstract
Background/Objectives: Accurate classification in cancer research is vital for devising effective treatment strategies. Precise cancer classification depends significantly on selecting the most informative genes from high-dimensional datasets, a task made complex by the extensive data involved. This study introduces the Two-stage MI-PSA Gene [...] Read more.
Background/Objectives: Accurate classification in cancer research is vital for devising effective treatment strategies. Precise cancer classification depends significantly on selecting the most informative genes from high-dimensional datasets, a task made complex by the extensive data involved. This study introduces the Two-stage MI-PSA Gene Selection algorithm, a novel approach designed to enhance cancer classification accuracy through robust gene selection methods. Methods: The proposed method integrates Mutual Information (MI) and Particle Swarm Optimization (PSO) for gene selection. In the first stage, MI acts as an initial filter, identifying genes rich in cancer-related information. In the second stage, PSO refines this selection to pinpoint an optimal subset of genes for accurate classification. Results: The experimental findings reveal that the MI-PSA method achieves a best classification accuracy of 99.01% with a selected subset of 19 genes, substantially outperforming the MI and SVM methods, which attain best accuracies of 93.44% and 91.26%, respectively, for the same gene count. Furthermore, MI-PSA demonstrates superior performance in terms of average and worst-case accuracy, underscoring its robustness and reliability. Conclusions: The MI-PSA algorithm presents a powerful approach for identifying critical genes essential for precise cancer classification, advancing both our understanding and management of this complex disease. Full article
(This article belongs to the Special Issue AI and Digital Health for Disease Diagnosis and Monitoring)
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30 pages, 2346 KiB  
Article
A Novel Method for 3D Lung Tumor Reconstruction Using Generative Models
by Hamidreza Najafi, Kimia Savoji, Marzieh Mirzaeibonehkhater, Seyed Vahid Moravvej, Roohallah Alizadehsani and Siamak Pedrammehr
Diagnostics 2024, 14(22), 2604; https://doi.org/10.3390/diagnostics14222604 - 20 Nov 2024
Cited by 1 | Viewed by 1089
Abstract
Background: Lung cancer remains a significant health concern, and the effectiveness of early detection significantly enhances patient survival rates. Identifying lung tumors with high precision is a challenge due to the complex nature of tumor structures and the surrounding lung tissues. Methods: To [...] Read more.
Background: Lung cancer remains a significant health concern, and the effectiveness of early detection significantly enhances patient survival rates. Identifying lung tumors with high precision is a challenge due to the complex nature of tumor structures and the surrounding lung tissues. Methods: To address these hurdles, this paper presents an innovative three-step approach that leverages Generative Adversarial Networks (GAN), Long Short-Term Memory (LSTM), and VGG16 algorithms for the accurate reconstruction of three-dimensional (3D) lung tumor images. The first challenge we address is the accurate segmentation of lung tissues from CT images, a task complicated by the overwhelming presence of non-lung pixels, which can lead to classifier imbalance. Our solution employs a GAN model trained with a reinforcement learning (RL)-based algorithm to mitigate this imbalance and enhance segmentation accuracy. The second challenge involves precisely detecting tumors within the segmented lung regions. We introduce a second GAN model with a novel loss function that significantly improves tumor detection accuracy. Following successful segmentation and tumor detection, the VGG16 algorithm is utilized for feature extraction, preparing the data for the final 3D reconstruction. These features are then processed through an LSTM network and converted into a format suitable for the reconstructive GAN. This GAN, equipped with dilated convolution layers in its discriminator, captures extensive contextual information, enabling the accurate reconstruction of the tumor’s 3D structure. Results: The effectiveness of our method is demonstrated through rigorous evaluation against established techniques using the LIDC-IDRI dataset and standard performance metrics, showcasing its superior performance and potential for enhancing early lung cancer detection. Conclusions:This study highlights the benefits of combining GANs, LSTM, and VGG16 into a unified framework. This approach significantly improves the accuracy of detecting and reconstructing lung tumors, promising to enhance diagnostic methods and patient results in lung cancer treatment. Full article
(This article belongs to the Special Issue AI and Digital Health for Disease Diagnosis and Monitoring)
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