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Life
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New Insights into Artificial Intelligence in Medical Imaging
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New Insights into Artificial Intelligence in Medical Imaging

A special issue of Life (ISSN 2075-1729). This special issue belongs to the section "Radiobiology and Nuclear Medicine".

Deadline for manuscript submissions: 31 July 2024 | Viewed by 529

Special Issue Editors

Dr. Luigi La Via

E-Mail Website
Guest Editor
Department of Anesthesiology and Intensive Care, AOU “Policlinico-San Marco”, 95123 Catania, Italy
Interests: cardiac arrest; echocardiography; post-resuscitation care; oxygen; airways; simulation
Special Issues, Collections and Topics in MDPI journals
Dr. Antonino Maniaci

E-Mail Website
Guest Editor
Department of Medical and Surgical Sciences and Advanced Technologies “GF Ingrassia” ENT Section, University of Catania, 95123 Catania, Italy
Interests: ENT; otology; hearing loss; artificial intelligence; machine learning; dysphagia; voice disorders; vocal rehabilitation
Special Issues, Collections and Topics in MDPI journals
Dr. Caterina Gagliano

E-Mail Website
Guest Editor
Faculty of Medicine and Surgery, University of Enna "Kore", 94100 Enna, Italy
Interests: artificial intelligence; eye surgery; surgery
Dr. Salvatore Lavalle

E-Mail Website
Guest Editor
Faculty of Medicine and Surgery, University of Enna "Kore", 94100 Enna, Italy
Interests: artificial intelligence; COVID-19; prognosis; radiography; severe acute respiratory syndrome; image quality; MRI of the prostate; PI-QUAL; prostate cancer
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The field of medical imaging has witnessed remarkable advancements in recent years, driven by the integration of artificial intelligence (AI) techniques. AI has revolutionized medical imaging by enhancing diagnostic accuracy, improving image analysis, and enabling personalized patient care. The journal "Life" is pleased to announce a Special Issue focused on exploring the latest developments, challenges, and future prospects of AI in medical imaging.

The primary aim of this Special Issue is to provide a comprehensive platform for researchers, clinicians, and industry experts to share their state-of-the-art research, innovative methodologies, and clinical applications in the realm of AI in medical imaging. The Special Issue will cover a wide range of topics, including but not limited to:

  • AI-based algorithms for disease detection, classification, and prognosis using medical images.
  • Deep learning techniques for image segmentation, registration, and reconstruction.
  • AI-driven radiomics and radiogenomics for precision medicine and personalized treatment.
  • AI-based image-guided interventions, surgical planning, and navigation systems.
  • Development and validation of AI-assisted decision support systems for radiologists and clinicians.
  • Ethical considerations, regulatory challenges, and legal implications of AI adoption in medical imaging.

By bringing together the expertise and perspectives of researchers from various domains, this Special Issue aims to foster interdisciplinary collaboration and facilitate the exchange of knowledge and ideas. We welcome original research articles, reviews, and case studies that showcase novel applications, technical innovations, and clinical impacts of AI in medical imaging.

The expected outcomes of this Special Issue include advancing the field of medical imaging, guiding future research directions, informing clinical decision making, and inspiring the development of AI-powered tools and technologies that can revolutionize patient care and improve healthcare outcomes.

We invite researchers and practitioners to contribute their valuable insights and help shape the future of AI in medical imaging by submitting their original contributions to this Special Issue of "Life." Together, we can harness the potential of AI to transform medical imaging practices, ultimately leading to improved patient outcomes and enhanced healthcare delivery.

Dr. Luigi La Via
Dr. Antonino Maniaci
Dr. Caterina Gagliano
Dr. Salvatore Lavalle
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. Life is an international peer-reviewed open access monthly 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

  • artificial intelligence
  • machine learning
  • deep learning
  • image analysis
  • precision medicine
  • image-guided interventions
  • decision support systems

Published Papers (1 paper)

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Research

23 pages, 4679 KiB  
Article
Refined Detection and Classification of Knee Ligament Injury Based on ResNet Convolutional Neural Networks
by Ștefan-Vlad Voinea, Ioana Andreea Gheonea, Rossy Vlăduț Teică, Lucian Mihai Florescu, Monica Roman and Dan Selișteanu
Life 2024, 14(4), 478; https://doi.org/10.3390/life14040478 - 05 Apr 2024
Viewed by 405
Abstract
Currently, medical imaging has largely supplanted traditional methods in the realm of diagnosis and treatment planning. This shift is primarily attributable to the non-invasive nature, rapidity, and user-friendliness of medical-imaging techniques. The widespread adoption of medical imaging, however, has shifted the bottleneck to [...] Read more.
Currently, medical imaging has largely supplanted traditional methods in the realm of diagnosis and treatment planning. This shift is primarily attributable to the non-invasive nature, rapidity, and user-friendliness of medical-imaging techniques. The widespread adoption of medical imaging, however, has shifted the bottleneck to healthcare professionals who must analyze each case post-image acquisition. This process is characterized by its sluggishness and subjectivity, making it susceptible to errors. The anterior cruciate ligament (ACL), a frequently injured knee ligament, predominantly affects a youthful and sports-active demographic. ACL injuries often leave patients with substantial disabilities and alter knee mechanics. Since some of these cases necessitate surgery, it is crucial to accurately classify and detect ACL injury. This paper investigates the utilization of pre-trained convolutional neural networks featuring residual connections (ResNet) along with image-processing methods to identify ACL injury and differentiate between various tear levels. The ResNet employed in this study is not the standard ResNet but rather an adapted version capable of processing 3D volumes constructed from 2D image slices. Achieving a peak accuracy of 97.15% with a custom split, 96.32% through Monte-Carlo cross-validation, and 93.22% via five-fold cross-validation, our approach enhances the performance of three-class classifiers by over 7% in terms of raw accuracy. Moreover, we achieved an improvement of more than 1% across all types of evaluation. It is quite clear that the model’s output can effectively serve as an initial diagnostic baseline for radiologists with minimal effort and nearly instantaneous results. This advancement underscores the paper’s focus on harnessing deep learning for the nuanced detection and classification of ACL tears, demonstrating a significant leap toward automating and refining diagnostic accuracy in sports medicine and orthopedics. Full article
(This article belongs to the Special Issue New Insights into Artificial Intelligence in Medical Imaging)
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