ReviewFrontiers in cardiovascular medicine2023
Assisted probe guidance in cardiac ultrasound: A review.
Review in Frontiers in cardiovascular medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
15 citing papers in PubMed.
- Artificial Intelligence Across the Echocardiographic Workflow: A Narrative Review for Clinicians.Cureus · 2026Review
- Artificial intelligence in breaking the learning curve for echocardiography: a secondary analysis of a multicentre trial.European heart journal. Digital health · 2026Article
- Gastric ultrasound: Enhancing preoperative risk assessment and patient safety.Journal of perioperative practice · 2025Article
- Development and Validation of Echocardiography Artificial Intelligence Models: A Narrative Review.Journal of clinical medicine · 2025Review
- Deep learning framework for interpretable quality control of echocardiography video.Medical physics · 2025Article
- Application of ultrasound in combination with other methods in gynecological disease: artificial intelligence, surgery, and drugs.Frontiers in oncology · 2025Review
- Comparative Approach to Performance Estimation of Pulsed Wave Doppler Equipment Based on Kiviat Diagram.Sensors (Basel, Switzerland) · 2024Article
- Student ultrasound education, current view and controversies. Role of Artificial Intelligence, Virtual Reality and telemedicine.The ultrasound journal · 2024Review
- A multi-task deep learning approach for real-time view classification and quality assessment of echocardiographic images.Scientific reports · 2024Article
- Autonomous ultrasound scanning robotic system based on human posture recognition and image servo control: an application for cardiac imaging.Frontiers in robotics and AI · 2024Article
- Embracing AI: The Imperative Tool for Echo Labs to Stay Ahead of the Curve.Diagnostics (Basel, Switzerland) · 2023Article
- An AI-powered navigation framework to achieve an automated acquisition of cardiac ultrasound images.Scientific reports · 2023Article
- Applications of artificial intelligence in musculoskeletal ultrasound: narrative review.Frontiers in medicine · 2023Review
- Artificial Intelligence and Echocardiography: From Literacy to Leadership, Redefining Roles, Responsibilities, and Sustainability in the Artificial Intelligence-driven Echocardiography Era.Journal of cardiovascular echographyReview
- Determinants of image quality in transthoracic echocardiography: A retrospective cohort study.JRSM cardiovascular diseaseArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Echocardiography is the most frequently used imaging modality in cardiology. However, its acquisition is affected by inter-observer variability and largely dependent on the operator's experience. In this context, artificial intelligence techniques could reduce these variabilities and provide a user independent system. In recent years, machine learning (ML) algorithms have been used in echocardiography to automate echocardiographic acquisition. This review focuses on the state-of-the-art studies that use ML to automate tasks regarding the acquisition of echocardiograms, including quality assessment (QA), recognition of cardiac views and assisted probe guidance during the scanning process. The results indicate that performance of automated acquisition was overall good, but most studies lack variability in their datasets. From our comprehensive review, we believe automated acquisition has the potential not only to improve accuracy of diagnosis, but also help novice operators build expertise and facilitate point of care healthcare in medically underserved areas.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.