Clinician-reviewed AI-guided scans were rapid, sensitive for DVT, and specific for prioritizing patients requiring standard of care ultrasounds, suggesting AI-guided ultrasound may be a scalable triage strategy to expand DVT evaluation access, particularly in resource-constrained and after-hours settings.
It is demonstrated that the hybrid AI-human model achieved high diagnostic performance for ruling out proximal DVT, with a sensitivity of 90-100% and a negative predictive value (NVP) of 87.5-100%.
Syed Zulfiqar Ali Shah, Muhammad Kaleem Akhtar, Waqar Ahmad et al.· Veredas do Direito· 0 citations
AI should be viewed not as a replacement for ultrasound professionals but as a decision-support technology that may improve consistency, efficiency, and diagnostic confidence when carefully validated in real-world clinical settings.
Bayan Alghamdi, Eman M Alrewily, Sharefa S Alghamdi· Ultrasound Quarterly· 0 citations
Importance
Timely identification of aortic stenosis (AS) is essential for appropriate clinical management, yet screening remains limited by dependence on comprehensive echocardiography and trained imaging personnel.
Objective
To develop and validate a deep learning algorithm for detection of moderate or greater AS and prospectively evaluate its performance using artificial intelligence (AI)-guided focused cardiac ultrasound (FoCUS) acquired by novice operators.
Design, Setting, and Participants
This diagnostic study included retrospective algorithm development and validation and prospective evaluation of AI-guided FoCUS across Mayo Clinic sites in the Midwest, Arizona, and Florida. The model was developed using 6753 patients and evaluated in internal validation (n = 852), internal test (n = 844), and validation (n = 1912) cohorts. Performance was assessed on FoCUS acquired by experienced sonographers (n = 602) and prospectively by novice operators (n = 1302). The retrospective model development and validation cohorts comprised studies performed from January 2005 through September 2022. Prospective study was conducted in 2 enrollment periods from June to August 2024 and from June to September 2025. Participants from both periods were combined to comprise the final prospective cohort.
Exposure
AI-guided FoCUS acquisition and automated deep learning-based assessment for detection of moderate or greater AS.
Main Outcomes and Measures
Detection of moderate or greater AS. Performance was assessed using area under the receiver operating characteristic curve (AUROC), sensitivity, specificity, and positive predictive value.
Results
The model demonstrated excellent discrimination in the internal test cohort (AUROC, 0.99; 95% CI, 0.98-1.00) and geographically distinct validation cohorts in Arizona (AUROC, 0.99; 95% CI, 0.97-1.00) and Florida (AUROC, 0.99; 95% CI, 0.96-1.00). Among FoCUS examinations acquired by experienced sonographers, sensitivity was 95% (95% CI, 82-99) and specificity was 97% (95% CI, 95-98). In the prospective novice-operator cohort, 1258 of 1302 examinations (96.6%) were suitable for automated analysis. Sensitivity was 93% (95% CI, 82-99) and specificity was 96% (95% CI, 95-97). Expert review of AI-positive and uninterpretable examinations increased the positive predictive value from 49.4% to 91.1%, with sensitivity of 85.4%.
Conclusions and Relevance
A deep learning algorithm accurately detected moderate or greater AS across validation cohorts in this study. In prospective evaluation, novice operators were able to acquire AI-guided FoCUS examinations that enabled accurate detection of moderate or greater AS. These findings support a scalable strategy that may expand access to AS detection in settings with limited echocardiography resources.
Eunjung Lee, J. Naser, Conor J. Kane et al.· JAMA cardiology· 1 citation
The development of AI systems to support LUS interpretation shows high potential; however, current studies exhibit significant heterogeneity in their objectives, methodologies, and evaluation metrics.
Julia López-Canay, Alberto Fernández-Villar, Cristina Ramos-Hernández et al.· Frontiers in Radiology· 1 citation
Artificial intelligence (AI) is increasingly used in echocardiography and point-of-care ultrasound (POCUS) to support image acquisition, view recognition, image-quality assessment, segmentation, automated quantification, disease classification, reporting, and bedside decision support. This narrative review summarizes clinically relevant applications, with emphasis on clinical integration, pediatric and congenital heart disease considerations, and safe implementation. The strongest clinical evidence supports automated left ventricular segmentation and ejection fraction estimation, AI-guided acquisition, and workflow efficiency. Video-based deep learning has enabled beat-to-beat assessment of ventricular function, and a randomized workflow trial showed that AI-generated initial ejection fraction assessment was noninferior to sonographer assessment and required fewer cardiologist corrections. Regulatory-authorized acquisition and analysis tools demonstrate growing clinical adoption for specified adult indications. Recent multiview and disease-phenotyping models extend AI toward more comprehensive interpretation, while AI-enabled POCUS may improve focused image acquisition by non-expert users. However, external validation, pediatric and congenital heart disease data, cross-device generalizability, clinical outcome evidence, uncertainty communication, automation bias, and medicolegal responsibility remain important limitations. AI should currently be viewed as an augmentative rather than autonomous technology. The safest near-term model is human-AI collaboration, in which validated tools improve acquisition, reproducibility, and workflow while clinicians retain responsibility for interpretation and patient-centered decisions. Pediatric and congenital heart disease applications require age- and anatomy-specific datasets, multicenter validation, local performance monitoring, and clinician-supervised deployment. AI can assist across the echocardiography workflow, from acquisition guidance and view recognition to segmentation, quantification, disease screening, and structured reporting. The most mature clinical evidence supports left ventricular segmentation, ejection fraction estimation, AI-guided acquisition, and workflow efficiency rather than autonomous diagnosis. AI-enabled POCUS may improve acquisition by non-expert users, but image adequacy, interpretation, and clinical integration must remain clinician-supervised. Pediatric and congenital heart disease applications are promising but remain less mature than adult applications and require anatomy-specific datasets and multicenter validation. Safe implementation requires external validation, local monitoring, bias assessment, uncertainty display, audit trails, and institutional governance.
F. Savorgnan, Pranathi Pilla, Sarah Visokay et al.· Current Pediatrics Reports· 0 citations
Ultrasound is central to vascular imaging because of its accessibility, safety, and real-time capabilities, yet conventional duplex interpretation remains limited by operator dependence and qualitative assessment. Ultrasound-based radiomics and artificial intelligence (AI) offer opportunities to extract quantitative imaging biomarkers and improve reproducibility in vascular disease evaluation.
A narrative review was conducted using PubMed and Scopus, including studies published between 2018 and 2025 that evaluated ultrasound-based radiomics or AI in vascular disease. Emphasis was placed on duplex ultrasound applications relevant to arterial, venous, and aneurysmal pathology, while computed tomography- or magnetic resonance imaging-only radiomics studies were excluded.
Evidence indicates that ultrasound-based radiomics and AI enhance plaque characterization, automate segmentation, and support risk stratification in carotid artery disease, peripheral arterial disease, venous thromboembolism, and abdominal aortic aneurysms. These approaches reduce interobserver variability and enable standardized longitudinal assessment, although most studies remain retrospective.
Clinical translation is constrained by acquisition variability, limited external validation, and challenges integrating AI tools into established vascular laboratory workflows. Current evidence supports AI primarily as a decision-support adjunct rather than a replacement for expert interpretation.
Ultrasound-based radiomics and AI are evolving adjuncts to vascular ultrasound. With standardized acquisition and validation, these techniques may enhance reproducibility and care.
A. W. Islam, N. Tariq, Subair Sabbar Ahmed· Journal for Vascular Ultraso...· 0 citations
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