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Artificial Intelligence in Medicine and Healthcare—2nd Edition
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Artificial Intelligence in Medicine and Healthcare—2nd Edition

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 (31 December 2025) | Viewed by 1313

Special Issue Editor

Prof. Dr. Kelvin K. L. Wong

E-Mail Website
Guest Editor
School of Electrical and Electronics Engineering, University of Adelaide, Adelaide, SA 5005, Australia
Interests: biomedical engineering; artificial intelligence; deep learning; computational hemodynamics; image processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Artificial intelligence technology is a broad, cross-cutting frontier subject. In recent years, computer software and hardware technologies have developed rapidly. Artificial intelligence is a branch of computer science that involves the research and application of intelligent machines. Its main goal is to study certain intellectual abilities of computers that imitate the human brain. The technology involves intelligent expert systems, language processing, intelligent data retrieval, intelligent control, and many other aspects and has made outstanding achievements.

In recent years, artificial intelligence technology has been widely used in the medical and health fields, including in areas such as disease prediction and diagnosis and treatment, drug research and development, etc., which have greatly improved medical service capabilities, effectively alleviating the problems mentioned above, and promoted the reform and development of medical health. The ever-increasing demand for medical and health care has led to the unprecedented development of smart health. Compared with traditional medical care, smart health has the characteristics of personalized health, big data diagnosis and treatment, multi-participant cooperation and collaboration, and full-process intelligence in the medical process. Vigorously promoting "artificial intelligence + medical health" can give new vitality to the medical industry and will effectively promote the innovative supply of medical services and the open sharing of information resources.

Potential topics include, but are not limited to, the following:

  • Disease diagnosis using machine learning;
  • Medical image processing and intelligent perception;
  • Medical data modeling based on cluster computing;
  • Protein structure prediction using high-performance computing;
  • Sequencing data processing on the Internet-of-Medical Things platform;
  • Expression profiling data analysis using convolutional neural networks.

Prof. Dr. Kelvin K. L. Wong
Guest Editor

Manuscript Submission Information

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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

  • artificial intelligence
  • machine learning
  • medical image processing
  • data processing
  • Internet of Medical Things

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Published Papers (1 paper)

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Research

50 pages, 12973 KB  
Article
Deepening the Diagnosis: Detection of Midline Shift Using an Advanced Deep Learning Architecture
by Tuğrul Hakan Gençtürk, İsmail Kaya and Fidan Kaya Gülağız
Appl. Sci. 2026, 16(2), 890; https://doi.org/10.3390/app16020890 - 15 Jan 2026
Viewed by 769
Abstract
Midline shift (MLS) is one of the conditions that strongly affects mortality and prognosis in critical neurological emergencies such as traumatic brain injury (TBI). Especially, MLS over 5 mm requires urgent diagnosis and treatment. Despite widespread tomography imaging capabilities, the lack of radiologists [...] Read more.
Midline shift (MLS) is one of the conditions that strongly affects mortality and prognosis in critical neurological emergencies such as traumatic brain injury (TBI). Especially, MLS over 5 mm requires urgent diagnosis and treatment. Despite widespread tomography imaging capabilities, the lack of radiologists capable of interpreting the images causes delays in the diagnosis process. Therefore, there is a need for AI-supported diagnostic systems specifically tailored to the field for MLS detection. However, the lack of open, disorder-specific datasets in the literature has limited research in the field and hindered the ability to make comparisons against a reliable reference point. Therefore, the current state of deep learning (DL) methods in the field is not sufficiently addressed. Within the scope of this study, a DL architecture is proposed for MLS detection as a classification task, with millimeter-scale MLS measurements used for evaluation and stratified analysis. This process also comprehensively addresses the status of MLS detection in contemporary DL architecture. Furthermore, to address the lack of open datasets in the literature, two publicly available datasets originally collected with a primary focus on TBI have been annotated for MLS detection. The proposed model was tested on two different open datasets and achieved mean sensitivity values of 0.9467–0.9600 for the Radiological Society of North America (RSNA) dataset and 0.8623–0.8984 for the CQ500 dataset in detecting MLS presence above 5 mm across two different scenarios. It achieved a mean Area Under the Curve-Receiver Operating Characteristic (AUC-ROC) value of 0.9219–0.9816 for the RSNA dataset and 0.9443–0.9690 for the CQ500 dataset. The aim of the study is to detect not only emergency cases but also small MLSs independent of quantity for patient follow-up, so the overall performance of the proposed model (MLS present/absent) was calculated without an MLS quantity threshold. Mean F1 Score values of 0.7403 for the RSNA dataset and 0.7271 for the CQ500 dataset were obtained, along with mean AUC-ROC values of 0.8941 for the RSNA dataset and 0.9301 for the CQ500 dataset. The study presents a clinically applicable, optimized, fast, reliable, up-to-date, and successful DL solution for the rapid diagnosis of MLS, intervention in emergencies, and monitoring of small MLS. It also contributes to the literature by enabling a high level of reproducibility in the scientific community with labeled open data. Full article
(This article belongs to the Special Issue Artificial Intelligence in Medicine and Healthcare—2nd Edition)
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