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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: 28 February 2027 | Viewed by 5223

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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Published Papers (5 papers)

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Research

Jump to: Review

14 pages, 412 KB  
Article
Impact of Prehospital Lung Ultrasound on Diagnostic Precision and Hospital Transport in Patients with Dyspnea and Respiratory Failure: A Retrospective Comparative Analysis
by Damian Kowalczyk and Mikołaj Tyczyński
Diagnostics 2026, 16(9), 1297; https://doi.org/10.3390/diagnostics16091297 - 26 Apr 2026
Viewed by 496
Abstract
Background: Dyspnea is a common reason for emergency medical service (EMS) interventions and is associated with a substantial risk of severe clinical course, complications, and hospital admission. Its differential diagnosis in the prehospital setting remains challenging due to the limited availability of imaging [...] Read more.
Background: Dyspnea is a common reason for emergency medical service (EMS) interventions and is associated with a substantial risk of severe clinical course, complications, and hospital admission. Its differential diagnosis in the prehospital setting remains challenging due to the limited availability of imaging modalities. Point-of-care ultrasound (POCUS), including lung ultrasound (LUS), is a rapid, field-applicable technique recommended in numerous acute respiratory diagnostic scenarios. Objective: To evaluate the use of lung ultrasound in the prehospital setting and its association with the precision of diagnoses related to respiratory failure, the frequency of transport to the emergency department (ED) among patients presenting with dyspnea/respiratory failure, and to characterize the profile of sonographic findings with their correlation to clinical diagnostic categories. Additionally, transport rates in the study population were compared with aggregated regional data for the Masovian Voivodeship (excluding the analyzed county). Methods: A retrospective observational study was conducted on EMS interventions performed between 01 January 2025 and 30 June 2025 in Legionowo County (N = 353). The analysis included ICD-10 codes assigned in prehospital documentation (one primary code and up to two additional codes) in patients presenting with dyspnea and/or respiratory failure, the performance of ultrasound examination, and resulting LUS findings (absence of pleural sliding and/or lung point; B-lines; consolidations; C-lines; pleural effusion). Descriptive analyses, frequency comparison tests (χ2/Fisher), estimation of relative risk (RR) with 95% confidence intervals (CI), and agreement analysis using Cohen’s kappa coefficient (κ) between etiological categories derived from ICD-10 codes and those inferred from LUS profiles were performed (κ with 95% CI estimated using bootstrap resampling). The study was reported in accordance with the STROBE guidelines for observational studies. Additionally, the distribution of ICD-10 coding and the proportion of hospital transports across the entire Masovian Voivodeship were compared with those observed in the analyzed area. Results: Ultrasound examination was performed in 72/353 (20.4%) EMS interventions; transport to the emergency department occurred in 239/353 (67.7%) cases. The most frequent clinical categories based on ICD-10 codes were: general/symptom-based 182/353 (51.6%), inflammatory 77/353 (21.8%), obstructive 66/353 (18.7%), and cardiological 20/353 (5.7%). Among abnormal LUS findings, the most common were B-lines (43/72; 61.4%) and consolidations (29/72; 41.4%). Consolidations were strongly associated with the inflammatory category (OR 9.72; p < 0.001), whereas B-lines were associated with the cardiological category (OR 23.41; p = 0.0011) among cases in which LUS was performed. Ultrasound use was associated with a higher frequency of assigning at least one targeted (non-symptom-based) diagnosis within ICD coding: 53/72 (73.6%) vs. 111/278 (39.9%), RR 1.84 (95% CI 1.51–2.25; p < 0.001). Agreement between the ICD-10 etiological category (inflammatory/cardiological/obstructive/other) and the category inferred from the LUS profile was moderate: κ = 0.36 (95% CI 0.21–0.51), with an observed agreement of 54.2%. Compared with aggregated regional data (Masovian Voivodeship excluding the analyzed county), the overall transport rate for comparable ICD-10 codes was lower in the study unit: 279/409 (68.2%) vs. 11,351/13,785 (82.3%), RR 0.83 (95% CI 0.78–0.89; p < 0.001). The largest differences were observed for dyspnea (R06.0: 72.9% vs. 88.2%; RR 0.83) and obstructive codes (J44/J45/J46 combined: 43.1% vs. 67.0%; RR 0.64). Conclusions: In this retrospective analysis, an EMS unit with systematically implemented ultrasound demonstrated a lower frequency of hospital transport for selected dyspnea/respiratory failure codes compared with regional data and greater precision in ICD-10 diagnostic coding in cases where ultrasound was performed. The profile of LUS findings correlated with clinical categories in a manner consistent with existing literature. Full article
(This article belongs to the Special Issue Application of Ultrasound Imaging in Clinical Diagnosis)
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14 pages, 4366 KB  
Article
From Ultrasound to Histopathology: Agreement Analysis and Depth Correction Model for Basal Cell Carcinoma Using a Portable 20 MHz High-Frequency Ultrasound Device
by Cristina Popescu, Carmen Andrada Iliescu, Andreea Mihaela Truica, Liliana Gabriela Popa, Andrei Ludovic Porosnicu, Irina Tudose, Carmen Boeru and Marius Nicolae Popescu
Diagnostics 2026, 16(7), 978; https://doi.org/10.3390/diagnostics16070978 - 25 Mar 2026
Viewed by 536
Abstract
Background: High-frequency ultrasound (HFUS) represents a valuable non-invasive imaging modality for assessing cutaneous tumors. In basal cell carcinoma (BCC), preoperative estimation of tumor depth may support therapeutic decision-making and surgical planning. However, agreement between HFUS-derived and histopathologic depth measurements remains incompletely characterized, particularly [...] Read more.
Background: High-frequency ultrasound (HFUS) represents a valuable non-invasive imaging modality for assessing cutaneous tumors. In basal cell carcinoma (BCC), preoperative estimation of tumor depth may support therapeutic decision-making and surgical planning. However, agreement between HFUS-derived and histopathologic depth measurements remains incompletely characterized, particularly with 20 MHz probes in routine clinical practice. Objectives: To evaluate agreement between 20 MHz HFUS and histopathology for BCC tumor depth using Bland–Altman analysis and to derive a preliminary correction equation to estimate histologic depth from HFUS measurements. Methods: This prospective observational pilot study included 15 patients with 16 histologically confirmed BCC lesions. All lesions underwent preoperative 20 MHz HFUS followed by surgical excision, with HFUS-derived tumor depth compared with histopathologic depth. Agreement was assessed using Bland–Altman analysis, and linear regression was performed to derive a preliminary correction equation. Results: Sixteen BCC lesions were analyzed. The mean difference between HFUS and histopathologic tumor depth was −0.07 mm, with 95% limits of agreement from −1.58 to +1.45 mm. HFUS and histopathologic depth measurements were highly correlated (R2 = 0.99). A correction equation was derived: estimated histopathologic depth (mm) = −0.52 + 1.10 × HFUS depth (mm). Conclusions: Twenty MHz HFUS demonstrated good agreement with histopathology for tumor depth assessment in BCC, with clinically acceptable variability. The proposed correction equation may improve interpretation of HFUS measurements; however, further validation in larger cohorts is required. Full article
(This article belongs to the Special Issue Application of Ultrasound Imaging in Clinical Diagnosis)
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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
Cited by 1 | Viewed by 1251
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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20 pages, 2310 KB  
Review
Beyond Computer-Aided Diagnosis: Artificial Intelligence as a “Digital Mentor” for POCUS Image Acquisition and Quality Assurance: A Narrative Review
by Hyub Huh and Jeong Jun Park
Diagnostics 2026, 16(6), 858; https://doi.org/10.3390/diagnostics16060858 - 13 Mar 2026
Viewed by 845
Abstract
Point-of-care ultrasound (POCUS) is portable and radiation-free, but its clinical reliability is constrained by operator-dependent image acquisition and the limited scalability of expert quality assurance (QA) review. As handheld devices proliferate faster than mentorship capacity, trainees increasingly rely on heterogeneous free open access [...] Read more.
Point-of-care ultrasound (POCUS) is portable and radiation-free, but its clinical reliability is constrained by operator-dependent image acquisition and the limited scalability of expert quality assurance (QA) review. As handheld devices proliferate faster than mentorship capacity, trainees increasingly rely on heterogeneous free open access medical education (FOAMed) resources that rarely provide real-time psychomotor feedback. We conducted a structured narrative review (MEDLINE, Embase, Scopus, and Web of Science; last searched on 23 February 2026), with searches performed by H.H. and independently checked by J.J.P. (both POCUS-trained clinicians). After screening, 31 studies were included. We synthesized evidence on artificial intelligence (AI) systems that support bedside image acquisition and automate QA. The primary synthesis centered on key prospective or comparative clinical evaluations of AI-guided acquisition across echocardiography, focused assessment with sonography in trauma, abdominal aortic aneurysm screening, and lung ultrasound, complemented by peer-reviewed studies of FOAMed appraisal tools and online resource quality. These evaluations suggest that real-time probe guidance, view recognition, anatomy labeling, and automated capture may enable novices, after brief training, to acquire diagnostically adequate images for narrowly defined tasks. Early reports of automated QA scoring and program-level triage for expert review suggest potential to reduce expert workload and shorten feedback cycles, but external validation, generalizability across devices and patient habitus, and patient-centered outcomes remain limited. Acquisition-focused AI may therefore serve as an upstream “digital mentor” to improve novice image acquisition. We propose a practical pathway that integrates curated FOAMed resources and simulation with AI-guided bedside acquisition and continuous QA governance for safe deployment. Full article
(This article belongs to the Special Issue Application of Ultrasound Imaging in Clinical Diagnosis)
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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
Cited by 2 | Viewed by 1244
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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