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Diagnostics
Special Issues
Application of Ultrasound Imaging in Clinical Diagnosis
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Application of Ultrasound Imaging in Clinical Diagnosis

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Medical Imaging and Theranostics".

Deadline for manuscript submissions: 31 May 2026 | Viewed by 1058

Special Issue Editor

Dr. Jinbum Kang

E-Mail Website
Guest Editor
Department of Biomedical Software Engineering, The Catholic University of Korea, Bucheon 14662, Republic of Korea
Interests: ultrasound imaging system; photoacoustic imaging and its clinical applications; signal and image processing

Special Issue Information

Dear Colleagues,

Ultrasound imaging has become an indispensable tool in modern medical diagnostics, offering real-time, non-invasive, and cost-effective evaluation of human tissues and organs. With advances in imaging systems, Doppler techniques, elastography, and contrast-enhanced ultrasound (CEUS), the diagnostic capability of ultrasound has expanded well beyond conventional gray-scale imaging. Furthermore, the integration of artificial intelligence and machine learning is accelerating the development of more precise, automated, and reproducible diagnostic tools. Point-of-care ultrasound (POCUS) and ultrasound-guided interventions also demonstrate the growing clinical impact of this versatile modality across diverse medical specialties.

This Special Issue aims to bring together recent innovations, methodologies, and clinical applications in diagnostic ultrasound. By covering both fundamental developments in ultrasound imaging systems and their translation into clinical practice, the Special Issue seeks to highlight the evolving role of ultrasound in enhancing diagnostic accuracy and patient outcomes. The topic aligns closely with the journal’s scope, emphasizing technological advances in medical imaging and their applications in healthcare.

In this Special Issue, original research articles and comprehensive reviews are welcome. Research areas may include, but are not limited to, the following:

  • Diagnostic ultrasound imaging and systems;
  • Doppler ultrasound techniques and applications;
  • Elastography for tissue characterization;
  • Contrast-enhanced ultrasound (CEUS);
  • Artificial intelligence in diagnostic ultrasound;
  • Point-of-care ultrasound (POCUS);
  • Ultrasound-guided interventions.

We look forward to receiving your valuable contributions.

Dr. Jinbum Kang
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 250 words) can be sent to the Editorial Office for assessment.

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

  • diagnostic ultrasound imaging and systems
  • doppler ultrasound techniques and applications
  • elastography for tissue characterization
  • contrast-enhanced ultrasound (CEUS)
  • artificial intelligence in diagnostic ultrasound
  • point-of-care ultrasound (POCUS)
  • ultrasound-guided interventions

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

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17 pages, 1504 KB  
Article
Unsupervised Beamforming with Optimized Coherence Loss for Clutter Suppression in Single Plane-Wave Ultrasound Imaging
by Seongbin Hwang, Hyunwoo Cho, Taejin Kim and Jinbum Kang
Diagnostics 2026, 16(1), 58; https://doi.org/10.3390/diagnostics16010058 - 24 Dec 2025
Viewed by 491
Abstract
Background: Single plane-wave ultrasound imaging (SPWI) enables acquisition speeds exceeding 1000 Hz, making it suitable for real-time applications requiring high temporal resolution. However, SPWI suffers from clutter artifacts, such as multipath reverberations, which degrade image contrast and diagnostic reliability. Methods: To [...] Read more.
Background: Single plane-wave ultrasound imaging (SPWI) enables acquisition speeds exceeding 1000 Hz, making it suitable for real-time applications requiring high temporal resolution. However, SPWI suffers from clutter artifacts, such as multipath reverberations, which degrade image contrast and diagnostic reliability. Methods: To address this limitation, we propose an unsupervised beamforming approach based on optimized deep coherence loss (UBF-DCLopt), which adaptively performs signal coherence computation according to the inter-frame decorrelation of plane-wave data. In addition, optimal plane-wave frames for coherence loss calculation are adaptively determined by physics-based criteria that account for steering angle and broadband pulse characteristics. To evaluate the proposed method, simulation, phantom and in vivo studies were conducted. For training and validation, publicly available datasets and data acquired from a fabricated clutter phantom were employed. Results: Experimental results demonstrated that the proposed UBF-DCLopt achieved contrast-to-noise ratio (CNR) improvements of 22% in phantom experiments and 32% in the in vivo studies compared to an unsupervised beamforming method using fixed deep coherence loss (UBF-DCL). Conclusions: These results demonstrate that the physics-informed unsupervised approach significantly suppresses reverberation artifacts while maintaining high spatiotemporal resolution, thereby enabling enhanced diagnostic accuracy in real-time ultrasound imaging. Full article
(This article belongs to the Special Issue Application of Ultrasound Imaging in Clinical Diagnosis)
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Review

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18 pages, 581 KB  
Review
AI-Enhanced POCUS in Emergency Care
by Monica Puticiu, Diana Cimpoesu, Florica Pop, Irina Ciumanghel, Luciana Teodora Rotaru, Bogdan Oprita, Mihai Alexandru Butoi, Vlad Ionut Belghiru, Raluca Mihaela Tat and Adela Golea
Diagnostics 2026, 16(2), 353; https://doi.org/10.3390/diagnostics16020353 - 21 Jan 2026
Viewed by 181
Abstract
Point-of-care ultrasound (POCUS) is an essential component of emergency medicine, enabling rapid bedside assessment across a wide spectrum of acute conditions. Its effectiveness, however, remains constrained by operator dependency, variable image quality, and time-critical decision-making. Recent advances in artificial intelligence (AI) offer opportunities [...] Read more.
Point-of-care ultrasound (POCUS) is an essential component of emergency medicine, enabling rapid bedside assessment across a wide spectrum of acute conditions. Its effectiveness, however, remains constrained by operator dependency, variable image quality, and time-critical decision-making. Recent advances in artificial intelligence (AI) offer opportunities to augment POCUS by supporting image acquisition, interpretation, and quantitative analysis. This narrative review synthesizes current evidence on AI-enhanced POCUS applications in emergency care, encompassing trauma, non-traumatic emergencies, integrated workflows, resource-limited settings, and education and training. Across trauma settings, AI-assisted POCUS has demonstrated promising performance for automated detection of pneumothorax, hemothorax, and free intraperitoneal fluid, supporting standardized eFAST examinations and rapid triage. In non-traumatic emergencies, AI-enabled cardiovascular, pulmonary, and abdominal applications provide automated measurements and pattern recognition that can approach expert-level performance when image quality is adequate. Integrated AI–POCUS systems and educational tools further highlight the potential to expand ultrasound access, support non-expert users, and standardize training. Nevertheless, important limitations persist, including limited generalizability, dataset bias, device heterogeneity, and uncertain impact on clinical decision-making and patient outcomes. In conclusion, AI-enhanced POCUS is transitioning from proof-of-concept toward early clinical integration in emergency medicine. While current evidence supports its role as a decision-support tool that may enhance consistency and efficiency, widespread adoption will require prospective multicentre validation, development of representative POCUS-specific datasets, vendor-agnostic solutions, and alignment with clinical, ethical, and regulatory frameworks. Full article
(This article belongs to the Special Issue Application of Ultrasound Imaging in Clinical Diagnosis)
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