The Role of AI in Ultrasound

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 680

Special Issue Editor


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Guest Editor
Department of Emergency Medicine, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seoul 13620, Republic of Korea
Interests: medical ultrasound

Special Issue Information

Dear Colleagues,

The integration of artificial intelligence (AI) into medical ultrasound represents a transformative shift in diagnostic imaging, offering unprecedented opportunities to enhance accuracy, efficiency and patient outcomes. This Special Issue aims to explore the cutting-edge advancements and innovative applications of AI technologies in medical ultrasound. By bringing together research, reviews and case studies from leading experts, we seek to highlight the potential of AI to address the current challenges, optimize imaging protocols and unlock new diagnostic possibilities.

The importance of AI in medical ultrasound is multifaceted, encompassing automated image interpretation, improved diagnostic precision and the development of predictive models for patient management. These advancements promise to significantly reduce the variability in ultrasound interpretation and enable more personalized patient care. Furthermore, AI-driven ultrasound can expand access to high-quality diagnostic imaging in resource-limited settings, democratizing healthcare on a global scale.

This Special Issue will serve as a platform for disseminating novel research findings, sharing clinical experiences and discussing future directions in the integration of AI with medical ultrasound. Our goal is to foster a multidisciplinary dialogue that will spur innovation, optimize clinical workflows and ultimately improve patient care. Contributions are welcomed from researchers, clinicians and technologists who are at the forefront of applying AI in ultrasound imaging across various medical specialties.

Dr. Hyuksool Kwon
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at mdpi.longhoe.net 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

  • artificial intelligence
  • medical ultrasound
  • diagnostic imaging
  • automated image interpretation
  • predictive modeling
  • personalized medicine
  • clinical workflow optimization
  • healthcare access
  • multidisciplinary innovation

Published Papers (1 paper)

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Research

12 pages, 2454 KiB  
Article
Application of Quantitative Ultrasonography and Artificial Intelligence for Assessing Severity of Fatty Liver: A Pilot Study
by Hyuksool Kwon, Myeong-Gee Kim, SeokHwan Oh, Youngmin Kim, Guil Jung, Hyeon-Jik Lee, Sang-Yun Kim and Hyeon-Min Bae
Diagnostics 2024, 14(12), 1237; https://doi.org/10.3390/diagnostics14121237 - 12 Jun 2024
Viewed by 455
Abstract
Non-alcoholic fatty liver disease (NAFLD), prevalent among conditions like obesity and diabetes, is globally significant. Existing ultrasound diagnosis methods, despite their use, often lack accuracy and precision, necessitating innovative solutions like AI. This study aims to validate an AI-enhanced quantitative ultrasound (QUS) algorithm [...] Read more.
Non-alcoholic fatty liver disease (NAFLD), prevalent among conditions like obesity and diabetes, is globally significant. Existing ultrasound diagnosis methods, despite their use, often lack accuracy and precision, necessitating innovative solutions like AI. This study aims to validate an AI-enhanced quantitative ultrasound (QUS) algorithm for NAFLD severity assessment and compare its performance with Magnetic Resonance Imaging Proton Density Fat Fraction (MRI-PDFF), a conventional diagnostic tool. A single-center cross-sectional pilot study was conducted. Liver fat content was estimated using an AI-enhanced quantitative ultrasound attenuation coefficient (QUS-AC) of Barreleye Inc. with an AI-based QUS algorithm and two conventional ultrasound techniques, FibroTouch Ultrasound Attenuation Parameter (UAP) and Canon Attenuation Imaging (ATI). The results were compared with MRI-PDFF values. The intraclass correlation coefficient (ICC) was also assessed. Significant correlation was found between the QUS-AC and the MRI-PDFF, reflected by an R value of 0.95. On other hand, ATI and UAP displayed lower correlations with MRI-PDFF, yielding R values of 0.73 and 0.51, respectively. In addition, ICC for QUS-AC was 0.983 for individual observations. On the other hand, the ICCs for ATI and UAP were 0.76 and 0.39, respectively. Our findings suggest that AC with AI-enhanced QUS could serve as a valuable tool for the non-invasive diagnosis of NAFLD. Full article
(This article belongs to the Special Issue The Role of AI in Ultrasound)
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