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: 17 January 2025 | Viewed by 2145

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Department of Anesthesia and Intensive Care, Azienda Ospedaliero Universitaria Policlinico “G.Rodolico-San Marco”, 95123 Catania, Italy
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Department of Medical and Surgical Sciences and Advanced Technologies “GF Ingrassia” ENT Section, University of Catania, 95123 Catania, Italy
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Department of Medicine and Surgery, University of Enna “Kore”, Contrada Santa Panasia, 94100 Enna, Italy
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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
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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

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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. 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

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Published Papers (2 papers)

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Research

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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 - 5 Apr 2024
Cited by 1 | Viewed by 1031
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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Review

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14 pages, 1316 KiB  
Review
The Integration of Radiomics and Artificial Intelligence in Modern Medicine
by Antonino Maniaci, Salvatore Lavalle, Caterina Gagliano, Mario Lentini, Edoardo Masiello, Federica Parisi, Giannicola Iannella, Nicole Dalia Cilia, Valerio Salerno, Giacomo Cusumano and Luigi La Via
Life 2024, 14(10), 1248; https://doi.org/10.3390/life14101248 - 1 Oct 2024
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Abstract
With profound effects on patient care, the role of artificial intelligence (AI) in radiomics has become a disruptive force in contemporary medicine. Radiomics, the quantitative feature extraction and analysis from medical images, offers useful imaging biomarkers that can reveal important information about the [...] Read more.
With profound effects on patient care, the role of artificial intelligence (AI) in radiomics has become a disruptive force in contemporary medicine. Radiomics, the quantitative feature extraction and analysis from medical images, offers useful imaging biomarkers that can reveal important information about the nature of diseases, how well patients respond to treatment and patient outcomes. The use of AI techniques in radiomics, such as machine learning and deep learning, has made it possible to create sophisticated computer-aided diagnostic systems, predictive models, and decision support tools. The many uses of AI in radiomics are examined in this review, encompassing its involvement of quantitative feature extraction from medical images, the machine learning, deep learning and computer-aided diagnostic (CAD) systems approaches in radiomics, and the effect of radiomics and AI on improving workflow automation and efficiency, optimize clinical trials and patient stratification. This review also covers the predictive modeling improvement by machine learning in radiomics, the multimodal integration and enhanced deep learning architectures, and the regulatory and clinical adoption considerations for radiomics-based CAD. Particular emphasis is given to the enormous potential for enhancing diagnosis precision, treatment personalization, and overall patient outcomes. Full article
(This article belongs to the Special Issue New Insights Into Artificial Intelligence in Medical Imaging)
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