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Applied Sciences
Special Issues
Deep Learning in Health and Medicine
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Deep Learning in Health and Medicine

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

Deadline for manuscript submissions: closed (20 July 2022) | Viewed by 2028

Special Issue Editors

Prof. Dr. Yuko Ohno

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Guest Editor
Division of Robotics & Design for Innovative Healthcare, School of Medicine, Osaka University, Osaka Prefecture 565-0871, Japan
Interests: human-robot (computer) interactions for better care and rehabilitation; care biomechanics; posture cooperative analysis with AI; nursing engineering
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Hieyong Jeong

E-Mail Website
Guest Editor
Department of Artificial Intelligence Convergence, Chonnam National University, 77 Yongbongro, Bukgu, Gwangju 61186, Republic of Korea
Interests: human–robot (computer) interactions; care and rehabilitation; care biomechanics; posture cooperative analysis; nursing engineering
Special Issues, Collections and Topics in MDPI journals
Dr. Myoungjae Jun

E-Mail Website
Assistant Guest Editor
Division of Robotics & Design for Innovative Healthcare, School of Medicine, Osaka University, Osaka Prefecture 565-0871, Japan
Interests: sports medicine; physiotherapy; rehabilitation medicine

Special Issue Information

We hope that this Special Issue will become an opportunity to predict future work by examining the past and present of deep learning in health and medicine.

First, we would like to examine innovative research cases that deep learning has brought about in health and medicine: imaging analytics and diagnostics, natural language processing, drug discovery and precision medicine, clinical decision support, predictive analytics, etc.

Then, we would like to understand how different deep learning technologies can solve complicated research issues compared to conventional methods.

Finally, through our research we hope to better understand a newly emerging research topic in health and medicine.

Prof. Dr. Yuko Ohno
Prof. Dr. Hieyong Jeong
Dr. Myoungjae Jun
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

  • big data
  • capillaries
  • deep learning
  • early detection
  • health promotion
  • healthcare
  • human pose
  • machine learning
  • medicine
  • microscopy
  • non-invasive method
  • VR/AR/MR

Published Papers (1 paper)

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Research

12 pages, 4842 KiB  
Article
Machine Learning in Recognition of Basic Pulmonary Pathologies
by Jakub Płudowski and Jan Mulawka
Appl. Sci. 2022, 12(16), 8086; https://doi.org/10.3390/app12168086 - 12 Aug 2022
Cited by 1 | Viewed by 1303
Abstract
Nowadays, during the diagnosis process, the doctor is able to obtain access to much information describing the patient’s condition using appropriate tools. However, there are always two sides to the coin. The doctor has certain limitations regarding the amount of data they can [...] Read more.
Nowadays, during the diagnosis process, the doctor is able to obtain access to much information describing the patient’s condition using appropriate tools. However, there are always two sides to the coin. The doctor has certain limitations regarding the amount of data they can process at once. Information technology comes to the rescue, which with the help of computers is able to quickly and effectively separate important information from redundant information and support the doctor in making a diagnosis. In this work, a decision-making system was created to diagnose common lung pathologies in digital radiography images. Here, we consider four basic pulmonary diseases: pneumothorax, pneumonia, pulmonary consolidation, and lung lesions. Our objective is to develop a new automatic detection method of lung pathologies on chest X-ray radiographs using python programming language and its libraries. The approach uses solutions in the field of artificial intelligence, such as deep learning, convolutional neural network and segmentation to make a diagnosis that aims to help the radiologist at work. In the first sections, this work describes the fundamentals of the present form of diagnosis, a proposal to improve this process, the method of operation of the algorithms used, data acquisition, segmentation and processing methods. Then, the results of the operation of four different models and their implementation in a practical window program were presented. The best model, which detects pulmonary consolidation, achieves accuracy higher than 91%, which is a satisfactory result because they are not intended to replace radiologists but to improve their work. In the future, this type of program can be further developed by adding models that recognize other conditions. Full article
(This article belongs to the Special Issue Deep Learning in Health and Medicine)
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