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Artificial Intelligence in Clinical Medical Imaging: Volume 2

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A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Machine Learning and Artificial Intelligence in Diagnostics".

Deadline for manuscript submissions: 30 September 2024 | Viewed by 845

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Special Issue Editor

Prof. Dr. Daniele Giansanti

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Guest Editor

Centro Nazionale TISP, Istituto Superiore di Sanità, Rome, Italy
Interests: biomedical engineering; robotics; artificial intelligence; digital health; rehabilitation; smart technology; cybersecurity; mental health; animal-assisted therapy; social robotics; acceptance; diagnostic pathology and radiology; medical imaging; patient safety; healthcare quality; health assessment; chronic disease
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Special Issue Information

Dear Colleagues,

We are delighted to invite you to submit your latest research work to the second edition of the Special Issue "Artificial Intelligence in Clinical Medical Imaging". As a Guest Editor, I am excited to lead this Special Issue after the success of the first edition (https://0-www-mdpi-com.brum.beds.ac.uk/journal/diagnostics/special_issues/3FXN9682V0) and to provide a platform for the dissemination of cutting-edge research on the integration of AI into clinical medical imaging. The field of medical imaging has seen remarkable advancements in recent years, as demonstrated by the previous edition, particularly with the introduction of artificial intelligence (AI) techniques. AI has the potential to revolutionize clinical medical imaging by enabling more accurate, efficient, and personalized diagnoses and treatments. The Special Issue covers a range of topics related to AI in medical imaging, including (but not limited to) the following:

Deep learning techniques used for medical image analysis;

Image segmentation and feature extraction;

The computer-aided diagnosis and detection of diseases;

Disease progression prediction using imaging data;

Image registration and fusion for multimodal imaging;

Clinical decision support systems for medical imaging;

Image-based treatment planning and evaluation;

Data privacy and security in AI for medical imaging;

The standardization of imaging protocols for AI applications;

Ethical and social implications of AI in medical imaging.

We welcome original research articles, reviews, and case studies that cover any of these topics. Our goal is to provide a comprehensive overview of the current state of AI in clinical medical imaging and its potential to transform healthcare. We hope that authors will make a significant contribution to this Special Issue. We are confident that this Special Issue will be a valuable platform for researchers and practitioners working in the field of medical imaging.

Prof. Dr. Daniele Giansanti
Guest Editor

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. Diagnostics 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 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

diagnostics
medical imaging
artificial intelligence
medical decision
clinical medical imaging

Published Papers (2 papers)

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Research

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11 pages, 3182 KiB

Open AccessArticle

Opportunistic Screening for Acute Vertebral Fractures on a Routine Abdominal or Chest Computed Tomography Scans Using an Automated Deep Learning Model

by Ye Rin Kim, Yu Sung Yoon and Jang Gyu Cha

Diagnostics 2024, 14(7), 781; https://0-doi-org.brum.beds.ac.uk/10.3390/diagnostics14070781 - 08 Apr 2024

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Abstract

Objectives: To develop an opportunistic screening model based on a deep learning algorithm to detect recent vertebral fractures in abdominal or chest CTs. Materials and Methods: A total of 1309 coronal reformatted images (504 with a recent fracture from 119 patients, and 805 without fracture from 115 patients), from torso CTs, performed from September 2018 to April 2022, on patients who also had a spine MRI within two months, were included. Two readers participated in image selection and manually labeled the fractured segment on each selected image with Neuro-T (version 2.3.3; Neurocle Inc.) software. We split the images randomly into the training and internal test set (labeled: unlabeled = 480:700) and the secondary interval validation set (24:105). For the observer study, three radiologists reviewed the CT images in the external test set with and without deep learning assistance and scored the likelihood of an acute fracture in each image independently. Results: For the training and internal test sets, the AI achieved a 99.86% test accuracy, 91.22% precision, and 89.18% F1 score for detection of recent fracture. Then, in the secondary internal validation set, it achieved 99.90%, 74.93%, and 78.30%, respectively. In the observer study, with the assistance of the deep learning algorithm, a significant improvement was observed in the radiology resident’s accuracy, from 92.79% to 98.2% (p = 0.04). Conclusion: The model showed a high level of accuracy in the test set and also the internal validation set. If this algorithm is applied opportunistically to daily torso CT evaluation, it will be helpful for the early detection of fractures that require treatment. Full article

(This article belongs to the Special Issue Artificial Intelligence in Clinical Medical Imaging: Volume 2)

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Review

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27 pages, 2145 KiB

Open AccessReview

The Integration of Deep Learning in Radiotherapy: Exploring Challenges, Opportunities, and Future Directions through an Umbrella Review

by Andrea Lastrucci, Yannick Wandael, Renzo Ricci, Giovanni Maccioni and Daniele Giansanti

Diagnostics 2024, 14(9), 939; https://0-doi-org.brum.beds.ac.uk/10.3390/diagnostics14090939 (registering DOI) - 30 Apr 2024

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Abstract

This study investigates, through a narrative review, the transformative impact of deep learning (DL) in the field of radiotherapy, particularly in light of the accelerated developments prompted by the COVID-19 pandemic. The proposed approach was based on an umbrella review following a standard narrative checklist and a qualification process. The selection process identified 19 systematic review studies. Through an analysis of current research, the study highlights the revolutionary potential of DL algorithms in optimizing treatment planning, image analysis, and patient outcome prediction in radiotherapy. It underscores the necessity of further exploration into specific research areas to unlock the full capabilities of DL technology. Moreover, the study emphasizes the intricate interplay between digital radiology and radiotherapy, revealing how advancements in one field can significantly influence the other. This interdependence is crucial for addressing complex challenges and advancing the integration of cutting-edge technologies into clinical practice. Collaborative efforts among researchers, clinicians, and regulatory bodies are deemed essential to effectively navigate the evolving landscape of DL in radiotherapy. By fostering interdisciplinary collaborations and conducting thorough investigations, stakeholders can fully leverage the transformative power of DL to enhance patient care and refine therapeutic strategies. Ultimately, this promises to usher in a new era of personalized and optimized radiotherapy treatment for improved patient outcomes. Full article

(This article belongs to the Special Issue Artificial Intelligence in Clinical Medical Imaging: Volume 2)

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