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Diagnostics
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Artificial Intelligence (AI) and Machine Learning (ML) Applications Related to...
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Artificial Intelligence (AI) and Machine Learning (ML) Applications Related to Biomedical and Healthcare Industries

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 (30 June 2023) | Viewed by 8600

Special Issue Editors

Dr. Qaisar Abbas

E-Mail Website
Guest Editor
College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia
Interests: C++; Delphi; MATLAB; SPSS; CART; data analysis; Java programming; statistical software; skin; dermoscopy
Dr. Abdul Rauf Baig

E-Mail Website
Guest Editor
College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia
Interests: data mining; machine learning; computer vision; evolutionary computation

Special Issue Information

Dear Colleagues,

The healthcare industry is under enormous pressure to deliver high-quality care and healthcare services to the world's population, which is rapidly growing. People now more than ever want smart healthcare services, programs, and wearables that will improve their quality of life and increase their longevity. The healthcare industry has consistently been one of the biggest supporters of cutting-edge technology, and machine learning (ML), deep learning (DL), and artificial intelligence (AI) are no exception. Similarly to how AI, ML, and DL had a quick impact on the commercial and e-commerce sectors, a wide range of applications were also discovered in the healthcare sector, with AI and ML in particular having become incredibly important in the field of healthcare. They are used to improve the way healthcare services are delivered, lower costs, handle patient data, come up with new treatment methods and drugs, carry out remote monitoring, and a lot more.

The developments and applications of artificial intelligence and machine learning in biomedicine and healthcare are covered in this Special Issue. The integration of computer science, life science, healthcare, and statistical concepts into statistical models utilizing current data, finding patterns in data to extract information, and forecasting changes and diseases based on these data and models will all be covered. The practical uses of artificial intelligence and machine learning for illness diagnosis and management will also be covered. Using machine learning and artificial intelligence, this Special Issue (SI) will serve as a working example of how various forms of biological and healthcare data may be utilized to create models and forecast illnesses. With concrete examples, this SI will also discuss transfer learning, customized medicine, and precision medicine. The application of machine learning and artificial intelligence for disease visualization, prediction, detection, and diagnosis will also be covered. This SI could be helpful for programmers, medical professionals, and academics who want to learn more about how AI and ML are used in biomedical and healthcare informatics.

Dr. Qaisar Abbas
Dr. Abdul Rauf Baig
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

  • medical imaging diagnosis
  • behavioral challenges
  • physiological data analysis
  • AI, machine learning and deep learning for biomedical and health informatics
  • expert systems
  • predictive cardiovascular disease using electronic health records
  • image processing, computer vision, and pattern recognition
  • network and deep learning
  • Internet of Things (IoT)-based applications
  • artificial-intelligence- and machine-learning-based biological models

Published Papers (2 papers)

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Research

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15 pages, 5179 KiB  
Article
Development and Validation of a Novel Prognostic Tool to Predict Recurrence of Paroxysmal Atrial Fibrillation after the First-Time Catheter Ablation: A Retrospective Cohort Study
by Junjie Huang, Hao Chen, Quan Zhang, Rukai Yang, Shuai Peng, Zhijian Wu, Na Liu, Liang Tang, Zhenjiang Liu and Shenghua Zhou
Diagnostics 2023, 13(6), 1207; https://doi.org/10.3390/diagnostics13061207 - 22 Mar 2023
Viewed by 2945
Abstract
There is no gold standard to tell frustrating outcomes after the catheter ablation of paroxysmal atrial fibrillation (PAF). The study aims to construct a prognostic tool. We retrospectively analyzed 315 patients with PAF who underwent first-time ablation at the Second Xiangya Hospital of [...] Read more.
There is no gold standard to tell frustrating outcomes after the catheter ablation of paroxysmal atrial fibrillation (PAF). The study aims to construct a prognostic tool. We retrospectively analyzed 315 patients with PAF who underwent first-time ablation at the Second Xiangya Hospital of Central South University. The endpoint was identified as any documented relapse of atrial tachyarrhythmia lasting longer than 30 s after the three-month blanking period. Univariate Cox regression analyzed eleven preablation parameters, followed by two supervised machine learning algorithms and stepwise regression to construct a nomogram internally validated. Five factors related to ablation failure were as follows: female sex, left atrial appendage emptying flow velocity ≤31 cm/s, estimated glomerular filtration rate <65.8 mL/(min·1.73 m2), P wave duration in lead aVF ≥ 120 ms, and that in lead V1 ≥ 100 ms, which constructed a nomogram. It was correlated with the CHA2DS2-VASc score but outperformed the latter evidently in discrimination and clinical utility, not to mention its robust performances in goodness-of-fit and calibration. In addition, the nomogram-based risk stratification could effectively separate ablation outcomes. Patients at risk of relapse after PAF ablation can be recognized at baseline using the proposed five-factor nomogram. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI) and Machine Learning (ML) Applications Related to Biomedical and Healthcare Industries)
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Review

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22 pages, 5583 KiB  
Review
What Is Machine Learning, Artificial Neural Networks and Deep Learning?—Examples of Practical Applications in Medicine
by Jakub Kufel, Katarzyna Bargieł-Łączek, Szymon Kocot, Maciej Koźlik, Wiktoria Bartnikowska, Michał Janik, Łukasz Czogalik, Piotr Dudek, Mikołaj Magiera, Anna Lis, Iga Paszkiewicz, Zbigniew Nawrat, Maciej Cebula and Katarzyna Gruszczyńska
Diagnostics 2023, 13(15), 2582; https://doi.org/10.3390/diagnostics13152582 - 03 Aug 2023
Cited by 13 | Viewed by 4937
Abstract
Machine learning (ML), artificial neural networks (ANNs), and deep learning (DL) are all topics that fall under the heading of artificial intelligence (AI) and have gained popularity in recent years. ML involves the application of algorithms to automate decision-making processes using models that [...] Read more.
Machine learning (ML), artificial neural networks (ANNs), and deep learning (DL) are all topics that fall under the heading of artificial intelligence (AI) and have gained popularity in recent years. ML involves the application of algorithms to automate decision-making processes using models that have not been manually programmed but have been trained on data. ANNs that are a part of ML aim to simulate the structure and function of the human brain. DL, on the other hand, uses multiple layers of interconnected neurons. This enables the processing and analysis of large and complex databases. In medicine, these techniques are being introduced to improve the speed and efficiency of disease diagnosis and treatment. Each of the AI techniques presented in the paper is supported with an example of a possible medical application. Given the rapid development of technology, the use of AI in medicine shows promising results in the context of patient care. It is particularly important to keep a close eye on this issue and conduct further research in order to fully explore the potential of ML, ANNs, and DL, and bring further applications into clinical use in the future. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI) and Machine Learning (ML) Applications Related to Biomedical and Healthcare Industries)
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