ReviewLife (Basel, Switzerland)2023
Deep Learning for Echocardiography: Introduction for Clinicians and Future Vision: State-of-the-Art Review.
Review in Life (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 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
13 citing papers in PubMed, 18 citations in OpenAlex.
- Artificial Intelligence in Cardiovascular Risk Prediction: An Up-to-Date Narrative Review on the Emerging Role of Lipid Profile-Based Models.Journal of clinical medicine · 2026Review
- Automated HFrEF Diagnosis Using an Optimized TimeSformer Model in Echocardiography.Journal of imaging informatics in medicine · 2026Article
- Artificial intelligence in the clinical management and prognostication of mitral regurgitation: a systematic review.The Egyptian heart journal : (EHJ) : official bulletin of the Egyptian Society of Cardiology · 2026Review
- Machine Learning Applications for Risk Stratification in Heart Failure with Preserved Ejection Fraction: A New Era in Cardiology.Diagnostics (Basel, Switzerland) · 2026Review
- Deep Learning for Cardiac Wall Motion Analysis: A Review of Methods, Challenges, and Clinical Applications.Annals of biomedical engineering · 2026Review
- Artificial intelligence-enhanced echocardiography in cardiovascular disease management.Nature reviews. Cardiology · 2026Review
- Deep Learning-Driven Innovations in Echocardiography: Taxonomy, Clinical Impact, Challenges, and Opportunities.Annals of biomedical engineering · 2026Review
- Artificial intelligence in cardiovascular medicine: prevention, diagnosis, and intervention.Frontiers in artificial intelligence · 2026Review
- Article
- Gastric ultrasound: Enhancing preoperative risk assessment and patient safety.Journal of perioperative practice · 2025Article
- Artificial Intelligence in Clinics: Enhancing Cardiology Practice.JMA journal · 2025Review
- Technological Advancements in Augmented, Mixed, and Virtual Reality Technologies for Surgery: A Systematic Review.Cureus · 2024Review
- Deep learning of echocardiography distinguishes between presence and absence of late gadolinium enhancement on cardiac magnetic resonance in patients with hypertrophic cardiomyopathy.Echo research and practice · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Exponential growth in data storage and computational power is rapidly narrowing the gap between translating findings from advanced clinical informatics into cardiovascular clinical practice. Specifically, cardiovascular imaging has the distinct advantage in providing a great quantity of data for potentially rich insights, but nuanced interpretation requires a high-level skillset that few individuals possess. A subset of machine learning, deep learning (DL), is a modality that has shown promise, particularly in the areas of image recognition, computer vision, and video classification. Due to a low signal-to-noise ratio, echocardiographic data tend to be challenging to classify; however, utilization of robust DL architectures may help clinicians and researchers automate conventional human tasks and catalyze the extraction of clinically useful data from the petabytes of collected imaging data. The promise is extending far and beyond towards a contactless echocardiographic exam-a dream that is much needed in this time of uncertainty and social distancing brought on by a stunning pandemic culture. In the current review, we discuss state-of-the-art DL techniques and architectures that can be used for image and video classification, and future directions in echocardiographic research in the current era.
Indexed as
Identifiers
What OpenQuestion holds
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.