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Diagnostics
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The Role of AI in Ultrasound
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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 April 2025 | Viewed by 6953

Special Issue Editor

Dr. Hyuksool Kwon

E-Mail Website
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 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

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

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Published Papers (4 papers)

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Research

18 pages, 1411 KiB  
Article
Comparing ChatGPT 4.0’s Performance in Interpreting Thyroid Nodule Ultrasound Reports Using ACR-TI-RADS 2017: Analysis Across Different Levels of Ultrasound User Experience
by Katharina Margherita Wakonig, Simon Barisch, Leonard Kozarzewski, Steffen Dommerich and Markus Herbert Lerchbaumer
Diagnostics 2025, 15(5), 635; https://doi.org/10.3390/diagnostics15050635 - 6 Mar 2025
Viewed by 612
Abstract
Background/Objectives: This study evaluates ChatGPT 4.0’s ability to interpret thyroid ultrasound (US) reports using ACR-TI-RADS 2017 criteria, comparing its performance with different levels of US users. Methods: A team of medical experts, an inexperienced US user, and ChatGPT 4.0 analyzed 100 fictitious thyroid [...] Read more.
Background/Objectives: This study evaluates ChatGPT 4.0’s ability to interpret thyroid ultrasound (US) reports using ACR-TI-RADS 2017 criteria, comparing its performance with different levels of US users. Methods: A team of medical experts, an inexperienced US user, and ChatGPT 4.0 analyzed 100 fictitious thyroid US reports. ChatGPT’s performance was assessed for accuracy, consistency, and diagnostic recommendations, including fine-needle aspirations (FNA) and follow-ups. Results: ChatGPT demonstrated substantial agreement with experts in assessing echogenic foci, but inconsistencies in other criteria, such as composition and margins, were evident in both its analyses. Interrater reliability between ChatGPT and experts ranged from moderate to almost perfect, reflecting AI’s potential but also its limitations in achieving expert-level interpretations. The inexperienced US user outperformed ChatGPT with a nearly perfect agreement with the experts, highlighting the critical role of traditional medical training in standardized risk stratification tools such as TI-RADS. Conclusions: ChatGPT showed high specificity in recommending FNAs but lower sensitivity and specificity for follow-ups compared to the medical student. These findings emphasize ChatGPT’s potential as a supportive diagnostic tool rather than a replacement for human expertise. Enhancing AI algorithms and training could improve ChatGPT’s clinical utility, enabling better support for clinicians in managing thyroid nodules and improving patient care. This study highlights both the promise and current limitations of AI in medical diagnostics, advocating for its refinement and integration into clinical workflows. However, it emphasizes that traditional clinical training must not be compromised, as it is essential for identifying and correcting AI-driven errors. Full article
(This article belongs to the Special Issue The Role of AI in Ultrasound)
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15 pages, 1223 KiB  
Article
Revolutionizing Bladder Health: Artificial-Intelligence-Powered Automatic Measurement of Bladder Volume Using Two-Dimensional Ultrasound
by Evan Avraham Alpert, Daniel David Gold, Deganit Kobliner-Friedman, Michael Wagner and Ziv Dadon
Diagnostics 2024, 14(16), 1829; https://doi.org/10.3390/diagnostics14161829 - 22 Aug 2024
Cited by 1 | Viewed by 1476
Abstract
Introduction: Measuring elevated post-void residual volume is important for diagnosing urinary outflow tract obstruction and cauda equina syndrome. Catheter placement is exact but painful, invasive, and may cause infection, whereas an ultrasound is accurate, painless, and safe. Aim: The purpose of this single-center [...] Read more.
Introduction: Measuring elevated post-void residual volume is important for diagnosing urinary outflow tract obstruction and cauda equina syndrome. Catheter placement is exact but painful, invasive, and may cause infection, whereas an ultrasound is accurate, painless, and safe. Aim: The purpose of this single-center study is to evaluate the accuracy of a module for artificial-intelligence (AI)-based fully automated bladder volume (BV) prospective measurement using two-dimensional ultrasound images, as compared with manual measurement by expert sonographers. Methods: Pairs of transverse and longitudinal bladder images were obtained from patients evaluated in an urgent care clinic. The scans were prospectively analyzed by the automated module using the prolate ellipsoid method. The same examinations were manually measured by a blinded expert sonographer. The two methods were compared using the Pearson correlation, kappa coefficients, and the Bland–Altman method. Results: A total of 111 pairs of transverse and longitudinal views were included. A very strong correlation was found between the manual BV measurements and the AI-based module with r = 0.97 [95% CI: 0.96–0.98]. The specificity and sensitivity for the diagnosis of an elevated post-void residual volume using a threshold ≥200 mL were 1.00 and 0.82, respectively. An almost-perfect agreement between manual and automated methods was obtained (kappa = 0.85). Perfect reproducibility was found for both inter- and intra-observer agreements. Conclusion: This AI-based module provides an accurate automated measurement of the BV based on ultrasound images. This novel method demonstrates a very strong correlation with the gold standard, making it a potentially valuable decision-support tool for non-experts in acute settings. Full article
(This article belongs to the Special Issue The Role of AI in Ultrasound)
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10 pages, 2215 KiB  
Article
Assessment of an Artificial Intelligence Tool for Estimating Left Ventricular Ejection Fraction in Echocardiograms from Apical and Parasternal Long-Axis Views
by Roberto Vega, Cherise Kwok, Abhilash Rakkunedeth Hareendranathan, Arun Nagdev and Jacob L. Jaremko
Diagnostics 2024, 14(16), 1719; https://doi.org/10.3390/diagnostics14161719 - 8 Aug 2024
Cited by 2 | Viewed by 2225
Abstract
This work aims to evaluate the performance of a new artificial intelligence tool (ExoAI) to compute the left ventricular ejection fraction (LVEF) in echocardiograms of the apical and parasternal long axis (PLAX) views. We retrospectively gathered echocardiograms from 441 individual patients (70% male, [...] Read more.
This work aims to evaluate the performance of a new artificial intelligence tool (ExoAI) to compute the left ventricular ejection fraction (LVEF) in echocardiograms of the apical and parasternal long axis (PLAX) views. We retrospectively gathered echocardiograms from 441 individual patients (70% male, age: 67.3 ± 15.3, weight: 87.7 ± 25.4, BMI: 29.5 ± 7.4) and computed the ejection fraction in each echocardiogram using the ExoAI algorithm. We compared its performance against the ejection fraction from the clinical report. ExoAI achieved a root mean squared error of 7.58% in A2C, 7.45% in A4C, and 7.29% in PLAX, and correlations of 0.79, 0.75, and 0.89, respectively. As for the detection of low EF values (EF < 50%), ExoAI achieved an accuracy of 83% in A2C, 80% in A4C, and 91% in PLAX. Our results suggest that ExoAI effectively estimates the LVEF and it is an effective tool for estimating abnormal ejection fraction values (EF < 50%). Importantly, the PLAX view allows for the estimation of the ejection fraction when it is not feasible to acquire apical views (e.g., in ICU settings where it is not possible to move the patient to obtain an apical scan). Full article
(This article belongs to the Special Issue The Role of AI in Ultrasound)
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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
Cited by 3 | Viewed by 1680
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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