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Diagnostics
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AI and Big Data in Healthcare
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AI and Big Data in Healthcare

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: closed (31 March 2024) | Viewed by 10906

Special Issue Editors

Prof. Dr. Miguel Mascarenhas

E-Mail Website
Guest Editor
1. Gastroenterology Department, Centro Hospitalar Universitário de São João, Porto, Portugal
2. Faculty of Medicine, University of Porto, Porto, Portugal
Interests: inflammatory bowel disease; applied artificial intelligence; capsule endoscopy; neurogastroenterology; coloproctology
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Guilherme Macedo

E-Mail Website
Guest Editor
1. Gastroenterology Department, Centro Hospitalar Universitário de São João, Porto, Portugal
2. Faculty of Medicine, University of Porto, Porto, Portugal
Interests: gastroenterology; hepatology; liver transplantation; hepatocellular carcinoma; gastrointestinal diseases; biliary tract diseases; inflammatory bowel diseases
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Artificial intelligence has subtly and progressively been integrated into our daily life. The applicability of heuristic algorithms revolutionized how we manage extensive databases and AI, providing an adequate framework for systematic analysis, promptly revealing its enormous potential in healthcare.

The excitement of a plausible application of innovative systems in our practice generated overwhelming enthusiasm in our community, and clinicians rapidly dove into a new lexicon, such as convolutional neural network models, deep learning methods, training machines, computer-aided detection systems, etc. Soon, we all realized that cross-pollination research with biomedical engineers, informaticians, and clinicians was more than an episodic drift of our mindset but an indispensable move towards a new advancing frontier.

In this Special Issue, we aim to showcase the state of the art of AI in multiple fields of healthcare, with examples of cutting-edge research being carried out in this field.

You may choose our Joint Special Issue in Medicina.

Dr. Miguel Mascarenhas Saraiva
Prof. Dr. Guilherme Macedo
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
  • big data
  • healthcare
  • convolutional neural networks
  • precision medicine

Published Papers (5 papers)

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Research

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14 pages, 4268 KiB  
Article
Advanced Sampling Technique in Radiology Free-Text Data for Efficiently Building Text Mining Models by Deep Learning in Vertebral Fracture
by Wei-Chieh Hung, Yih-Lon Lin, Chi-Wei Lin, Wei-Leng Chin and Chih-Hsing Wu
Diagnostics 2024, 14(2), 137; https://doi.org/10.3390/diagnostics14020137 - 08 Jan 2024
Viewed by 735
Abstract
This study aims to establish advanced sampling methods in free-text data for efficiently building semantic text mining models using deep learning, such as identifying vertebral compression fracture (VCF) in radiology reports. We enrolled a total of 27,401 radiology free-text reports of X-ray examinations [...] Read more.
This study aims to establish advanced sampling methods in free-text data for efficiently building semantic text mining models using deep learning, such as identifying vertebral compression fracture (VCF) in radiology reports. We enrolled a total of 27,401 radiology free-text reports of X-ray examinations of the spine. The predictive effects were compared between text mining models built using supervised long short-term memory networks, independently derived by four sampling methods: vector sum minimization, vector sum maximization, stratified, and simple random sampling, using four fixed percentages. The drawn samples were applied to the training set, and the remaining samples were used to validate each group using different sampling methods and ratios. The predictive accuracy was measured using the area under the receiver operating characteristics (AUROC) to identify VCF. At the sampling ratios of 1/10, 1/20, 1/30, and 1/40, the highest AUROC was revealed in the sampling methods of vector sum minimization as confidence intervals of 0.981 (95%CIs: 0.980–0.983)/0.963 (95%CIs: 0.961–0.965)/0.907 (95%CIs: 0.904–0.911)/0.895 (95%CIs: 0.891–0.899), respectively. The lowest AUROC was demonstrated in the vector sum maximization. This study proposes an advanced sampling method, vector sum minimization, in free-text data that can be efficiently applied to build the text mining models by smartly drawing a small amount of critical representative samples. Full article
(This article belongs to the Special Issue AI and Big Data in Healthcare)
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27 pages, 5634 KiB  
Article
U-Net-Based Models towards Optimal MR Brain Image Segmentation
by Rammah Yousef, Shakir Khan, Gaurav Gupta, Tamanna Siddiqui, Bader M. Albahlal, Saad Abdullah Alajlan and Mohd Anul Haq
Diagnostics 2023, 13(9), 1624; https://doi.org/10.3390/diagnostics13091624 - 04 May 2023
Cited by 12 | Viewed by 4613
Abstract
Brain tumor segmentation from MRIs has always been a challenging task for radiologists, therefore, an automatic and generalized system to address this task is needed. Among all other deep learning techniques used in medical imaging, U-Net-based variants are the most used models found [...] Read more.
Brain tumor segmentation from MRIs has always been a challenging task for radiologists, therefore, an automatic and generalized system to address this task is needed. Among all other deep learning techniques used in medical imaging, U-Net-based variants are the most used models found in the literature to segment medical images with respect to different modalities. Therefore, the goal of this paper is to examine the numerous advancements and innovations in the U-Net architecture, as well as recent trends, with the aim of highlighting the ongoing potential of U-Net being used to better the performance of brain tumor segmentation. Furthermore, we provide a quantitative comparison of different U-Net architectures to highlight the performance and the evolution of this network from an optimization perspective. In addition to that, we have experimented with four U-Net architectures (3D U-Net, Attention U-Net, R2 Attention U-Net, and modified 3D U-Net) on the BraTS 2020 dataset for brain tumor segmentation to provide a better overview of this architecture’s performance in terms of Dice score and Hausdorff distance 95%. Finally, we analyze the limitations and challenges of medical image analysis to provide a critical discussion about the importance of developing new architectures in terms of optimization. Full article
(This article belongs to the Special Issue AI and Big Data in Healthcare)
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28 pages, 6757 KiB  
Article
COVID-19 Diagnosis in Computerized Tomography (CT) and X-ray Scans Using Capsule Neural Network
by Andronicus A. Akinyelu and Bubacarr Bah
Diagnostics 2023, 13(8), 1484; https://doi.org/10.3390/diagnostics13081484 - 20 Apr 2023
Cited by 2 | Viewed by 1019
Abstract
This study proposes a deep-learning-based solution (named CapsNetCovid) for COVID-19 diagnosis using a capsule neural network (CapsNet). CapsNets are robust for image rotations and affine transformations, which is advantageous when processing medical imaging datasets. This study presents a performance analysis of CapsNets on [...] Read more.
This study proposes a deep-learning-based solution (named CapsNetCovid) for COVID-19 diagnosis using a capsule neural network (CapsNet). CapsNets are robust for image rotations and affine transformations, which is advantageous when processing medical imaging datasets. This study presents a performance analysis of CapsNets on standard images and their augmented variants for binary and multi-class classification. CapsNetCovid was trained and evaluated on two COVID-19 datasets of CT images and X-ray images. It was also evaluated on eight augmented datasets. The results show that the proposed model achieved classification accuracy, precision, sensitivity, and F1-score of 99.929%, 99.887%, 100%, and 99.319%, respectively, for the CT images. It also achieved a classification accuracy, precision, sensitivity, and F1-score of 94.721%, 93.864%, 92.947%, and 93.386%, respectively, for the X-ray images. This study presents a comparative analysis between CapsNetCovid, CNN, DenseNet121, and ResNet50 in terms of their ability to correctly identify randomly transformed and rotated CT and X-ray images without the use of data augmentation techniques. The analysis shows that CapsNetCovid outperforms CNN, DenseNet121, and ResNet50 when trained and evaluated on CT and X-ray images without data augmentation. We hope that this research will aid in improving decision making and diagnostic accuracy of medical professionals when diagnosing COVID-19. Full article
(This article belongs to the Special Issue AI and Big Data in Healthcare)
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Review

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14 pages, 2085 KiB  
Review
Smart Endoscopy Is Greener Endoscopy: Leveraging Artificial Intelligence and Blockchain Technologies to Drive Sustainability in Digestive Health Care
by Miguel Mascarenhas, Tiago Ribeiro, João Afonso, Francisco Mendes, Pedro Cardoso, Miguel Martins, João Ferreira and Guilherme Macedo
Diagnostics 2023, 13(24), 3625; https://doi.org/10.3390/diagnostics13243625 - 08 Dec 2023
Cited by 1 | Viewed by 957
Abstract
The surge in the implementation of artificial intelligence (AI) in recent years has permeated many aspects of our life, and health care is no exception. Whereas this technology can offer clear benefits, some of the problems associated with its use have also been [...] Read more.
The surge in the implementation of artificial intelligence (AI) in recent years has permeated many aspects of our life, and health care is no exception. Whereas this technology can offer clear benefits, some of the problems associated with its use have also been recognised and brought into question, for example, its environmental impact. In a similar fashion, health care also has a significant environmental impact, and it requires a considerable source of greenhouse gases. Whereas efforts are being made to reduce the footprint of AI tools, here, we were specifically interested in how employing AI tools in gastroenterology departments, and in particular in conjunction with capsule endoscopy, can reduce the carbon footprint associated with digestive health care while offering improvements, particularly in terms of diagnostic accuracy. We address the different ways that leveraging AI applications can reduce the carbon footprint associated with all types of capsule endoscopy examinations. Moreover, we contemplate how the incorporation of other technologies, such as blockchain technology, into digestive health care can help ensure the sustainability of this clinical speciality and by extension, health care in general. Full article
(This article belongs to the Special Issue AI and Big Data in Healthcare)
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11 pages, 550 KiB  
Review
Enhancing the Evidence with Algorithms: How Artificial Intelligence Is Transforming Forensic Medicine
by Alin-Ionut Piraianu, Ana Fulga, Carmina Liana Musat, Oana-Roxana Ciobotaru, Diana Gina Poalelungi, Elena Stamate, Octavian Ciobotaru and Iuliu Fulga
Diagnostics 2023, 13(18), 2992; https://doi.org/10.3390/diagnostics13182992 - 19 Sep 2023
Cited by 4 | Viewed by 2836
Abstract
Background: The integration of artificial intelligence (AI) into various fields has ushered in a new era of multidisciplinary progress. Defined as the ability of a system to interpret external data, learn from it, and adapt to specific tasks, AI is poised to revolutionize [...] Read more.
Background: The integration of artificial intelligence (AI) into various fields has ushered in a new era of multidisciplinary progress. Defined as the ability of a system to interpret external data, learn from it, and adapt to specific tasks, AI is poised to revolutionize the world. In forensic medicine and pathology, algorithms play a crucial role in data analysis, pattern recognition, anomaly identification, and decision making. This review explores the diverse applications of AI in forensic medicine, encompassing fields such as forensic identification, ballistics, traumatic injuries, postmortem interval estimation, forensic toxicology, and more. Results: A thorough review of 113 articles revealed a subset of 32 papers directly relevant to the research, covering a wide range of applications. These included forensic identification, ballistics and additional factors of shooting, traumatic injuries, post-mortem interval estimation, forensic toxicology, sexual assaults/rape, crime scene reconstruction, virtual autopsy, and medical act quality evaluation. The studies demonstrated the feasibility and advantages of employing AI technology in various facets of forensic medicine and pathology. Conclusions: The integration of AI in forensic medicine and pathology offers promising prospects for improving accuracy and efficiency in medico-legal practices. From forensic identification to post-mortem interval estimation, AI algorithms have shown the potential to reduce human subjectivity, mitigate errors, and provide cost-effective solutions. While challenges surrounding ethical considerations, data security, and algorithmic correctness persist, continued research and technological advancements hold the key to realizing the full potential of AI in forensic applications. As the field of AI continues to evolve, it is poised to play an increasingly pivotal role in the future of forensic medicine and pathology. Full article
(This article belongs to the Special Issue AI and Big Data in Healthcare)
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