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Special Issue Editors

Prof. Dr. Reza Saatchi

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Guest Editor

Department of Engineering and Mathematics, Sheffield Hallam University, Sheffield S1 1WB, UK
Interests: medical engineering; electronic design aspects; medical engineering; medical devices; signal and image processing; artificial intelligence; machine learning; big data analysis; data mining; wireless communication; quality of service
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Derek P. Burke

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Guest Editor

The General Hospital Jamestown, Jamestown STHL 1ZZ, Saint Helena
Interests: emergency medicine; applications of AI to medicine

Prof. Dr. Shammi Ramlakhan

E-Mail Website
Guest Editor

Sheffield Children's Hospital, Sheffield S10 2TH, UK
Interests: emergency medicine; AI in acute care, health service delivery and diagnostics

Special Issue Information

Dear Colleagues,

Emergency medicine relies on specialised expertise and facilities to prevent, diagnose, monitor, and manage acute and urgent illnesses and injuries in all age groups. Clinicians in emergency medicine need to carry out timely assessment and treatment of patients to achieve optimum outcomes.

Recent innovations in artificial intelligence have provided exciting opportunities in emergency medicine for diagnosis, monitoring and management of a broad range of conditions. These innovations have the potential to improve patient care and treatment, reducing risk to patients, optimising resource allocation, and providing an improved experience for clinicians and their patients.

Artificial intelligence techniques are usually most effective when they can show transparency in their decision making. Furthermore, combinations of techniques such as neuro–fuzzy can build on the strengths of individual techniques. Adaptive systems with an ability to learn from their operating environment can assist with complex decision-making tasks, resulting in higher chances of patient surviving their trauma.

Data preparation methods such as feature selection and extraction, as well as statistical techniques to interpret the outputs, are important for effective applications of these techniques.

Artificial intelligence applications such as machine learning, fuzzy logic, neural computing, deep learning, genetic algorithms, expert systems, robotics, and natural language processing have been gaining popularity in emergency medicine.

You are warmly invited to submit your original articles to the Special Issue “Artificial Intelligence Innovations and Applications in Trauma, Urgent and Emergency Care: Current and Prospects”.

Thank you.

Prof. Dr. Reza Saatchi
Prof. Dr. Derek P. Burke
Prof. Dr. Shammi Ramlakhan
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. Healthcare 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 2700 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
machine learning
neural networks and neural computing
deep learning
fuzzy logic
neuro–fuzzy
expert systems
genetic algorithms
robotics
natural language processing
Bayesian models
random forest
probabilistic techniques
clustering techniques
hybrid artificial intelligence techniques
feature selection and extraction techniques for artificial intelligence
statistical techniques for artificial intelligence
signal and image processing techniques for artificial intelligence

Published Papers (1 paper)

Research

16 pages, 2966 KiB

Open AccessArticle

Convolutional Neural Network to Classify Infrared Thermal Images of Fractured Wrists in Pediatrics

by Olamilekan Shobayo, Reza Saatchi and Shammi Ramlakhan

Healthcare 2024, 12(10), 994; https://doi.org/10.3390/healthcare12100994 - 11 May 2024

Viewed by 480

Abstract

Convolutional neural network (CNN) models were devised and evaluated to classify infrared thermal (IRT) images of pediatric wrist fractures. The images were recorded from 19 participants with a wrist fracture and 21 without a fracture (sprain). The injury diagnosis was by X-ray radiography. For each participant, 299 IRT images of their wrists were recorded. These generated 11,960 images (40 participants × 299 images). For each image, the wrist region of interest (ROI) was selected and fast Fourier transformed (FFT) to obtain a magnitude frequency spectrum. The spectrum was resized to 100 × 100 pixels from its center as this region represented the main frequency components. Image augmentations of rotation, translation and shearing were applied to the 11,960 magnitude frequency spectra to assist with the CNN generalization during training. The CNN had 34 layers associated with convolution, batch normalization, rectified linear unit, maximum pooling and SoftMax and classification. The ratio of images for the training and test was 70:30, respectively. The effects of augmentation and dropout on CNN performance were explored. Wrist fracture identification sensitivity and accuracy of 88% and 76%, respectively, were achieved. The CNN model was able to identify wrist fractures; however, a larger sample size would improve accuracy. Full article

(This article belongs to the Special Issue Artificial Intelligence Innovations and Applications in Trauma, Urgent and Emergency Care: Current and Prospects)

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