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Special Issue Title:

Deep Learning for Internet of Anesthesia, Intensive Care, Emergency and Pain Medicine

Deadline for manuscript submissions: 30 December 2021

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Special Issue Editor

  • Guest Editor

    Dr. Mazin Abed MohammedE-MailWebsite

    Lecturer, College of Computer Science and Information Technology, University of Anbar, Anbar, Iraq

    Interests: Artificial Intelligence, Medical image processing, machine learning, Computer Vision, computational intelligence, IoT, Biomedical Computing, bio-informatics, and fog Computing

  • Guest Editor

    Dr. Korhan CENGİZ E-MailWebsite

    Assistant Professor, Republic of Turkey Trakya University, Turkey

    Interests: Wireless sensor networks, Routing protocols, Wireless communications, Statistical signal processing, Spatial modulation

  • Guest Editor

    Dr. Mashael S. MaashiE-MailWebsite

    Assistant Professor, Software Engineering Department, College of Computer and Information Sciences, King Saud University, Saudi Arabia

    Interests: Evolutionary computation, Multi-objective optimization, Meta-heuristic, Hyper-heuristic, Software optimization, Intelligent computing and their applications

  • Guest Editor

    Dr. Oana GemanE-MailWebsite

    Assistant Professor, Department of Health and Human Development, Universitatea Stefan cel Mare din Suceava, Suceava, Romania

    Interests: Non-invasive measurements of biomedical signals, Wireless sensors, E-Health, Telemedicine, Signal processing, Nonlinear dynamics analysis, Classification and prediction, Data-Mining, Deep Learning, Intelligent systems, Bioinformatics, and Biomedical applications

Special Issue Information

Deep learning is a technique for implementing machine learning that provides an effective solution in health analytics encompassing a series of techniques which will be more helpful to human intelligence system for handling uncertainty and subjective vagueness in decision making process. Among them, artificial intelligence (AI) has been around for over three decades, and this new approach of artificial intelligence, due to enhancements in technology, both software, and hardware, has resulted in the fact that human decision-making is considered inferior and erratic in many fields: none more so than medicine. Deep Machine learning algorithms with access to large data sets can be trained to outperform clinicians in many respects. AI’s effectiveness in accurate diagnosis of various medical conditions and medical image interpretation is well documented. Modern AI technology has the potential to transform medicine to a level never seen before in terms of efficiency and accuracy; but is also potentially highly disruptive, creating insecurity and allowing the transfer of expert domain knowledge to machines. Anesthesia, Intensive Care, Emergency and Pain Medicine are some complex medical disciplines and assuming AI can easily replace experienced and knowledgeable medical practitioners is a very unrealistic expectation. AI can be used in anesthetics to develop, in some respects, more advanced clinical decision support tools based on machine learning.

This Special issue focuses on the complexity of both AI developments, deep learning, machine learning, neural networks, etc. and opportunities of AI in Anesthesia, Intensive Care, Emergency or Pain Medicine for the future. It will provide current advances in AI tools and hardware technologies as well as outlining how these can be used in the fields of Anesthesia, Intensive Care, Emergency or Pain Medicine.

The topics (in Anesthesia, Intensive Care, Emergency and Pain Medicine fields) of interest include, but are not limited to:

• Deep learning methodologies for medical data analysis

• Deep learning and block chain assisted medical efficient product designs

• Deep learning algorithm for medical decision support systems

• Cognitive deep learning for wearable medical devices

• Deep learning for energy management in IoMT devices

• Deep learning for data analytics in body sensor networks

• Pattern recognition

• Image retrieval. biological imaging Molecular/pathologic image analysis gene data analysis multiple modalities X-ray CT MRI PET ultrasound

• Deep learning algorithm for medical decision support systems in heart disease

• Machine learning applied to Healthcare Systems

• Computational methods for COVID-19 prediction and detection

• Disease diagnosis using deep learning in IoMT

...

Keywords

Deep learning, Artificial intelligence, Accurate diagnosis, Medical image, Anesthesia, Intensive Care, Emergency, Pain Medicine

Manuscript Submission Information

Manuscripts should be submitted online by submit system. Manuscripts can be submitted until the deadline. All papers will be 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. Original articles, case reports or comprehensive reviews 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 double-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Signa Viate is an international peer-reviewed open access journal published by MRE Press. As of January 2021, Signa Vitae will change to a bimonthly journal. Please visit the Instructions for Authors page before submitting a manuscript.The Article Processing Charge (APC) for publication in this open access journal is $1200. We normally offer a discount greater than 30% to all contributors invited by the Editor-in-Chief, Guest Editor (GE) and Editorial board member. Submitted papers should be well formatted and use good English.

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