Trial reportBiomedical engineering online2023
Machine learning-enhanced echocardiography for screening coronary artery disease.
Trial report in Biomedical engineering online, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT03905200 (Study on the Value of Three-dimensional Speckle Tracking Technique in the Diagnosis and Follow-up of Coronary Heart Disease), which is not on this map. Cited by 8 papers.
What it found
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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.
Study on the Value of Three-dimensional Speckle Tracking Technique in the Diagnosis and Follow-up of Coronary Heart Disease
Who cites it
8 citing papers in PubMed.
- Deep Learning for Cardiac Wall Motion Analysis: A Review of Methods, Challenges, and Clinical Applications.Annals of biomedical engineering · 2026Review
- Characterizing Acute Pulmonary Embolism After Off-Pump Coronary Artery Bypass Surgery Using a Predictive XGBoost Model.Journal of multidisciplinary healthcare · 2026Article
- IFN-γ and IL-6 as Key Predicting Biomarkers for Active TB Among PLWH: Results from Four Machine Learning Methods.International journal of general medicine · 2026Article
- Use of artificial intelligence to predict outcomes in mild aortic valve stenosis.European heart journal. Digital health · 2025Article
- Echocardiographic video-driven multi-task learning model for coronary artery disease diagnosis and severity grading.Frontiers in bioengineering and biotechnology · 2025Article
- The Role of Artificial Intelligence and Machine Learning in Cardiovascular Imaging and Diagnosis.Cureus · 2024Review
- Future Horizons: The Potential Role of Artificial Intelligence in Cardiology.Journal of personalized medicine · 2024Review
- Machine Learning in Cardiovascular Risk Prediction and Precision Preventive Approaches.Current atherosclerosis reports · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
Abstract
backgroundSince myocardial work (MW) and left atrial strain are valuable for screening coronary artery disease (CAD), this study aimed to develop a novel CAD screening approach based on machine learning-enhanced echocardiography.
methodsThis prospective study used data from patients undergoing coronary angiography, in which the novel echocardiography features were extracted by a machine learning algorithm. A total of 818 patients were enrolled and randomly divided into training (80%) and testing (20%) groups. An additional 115 patients were also enrolled in the validation group.
resultsThe superior diagnosis model of CAD was optimized using 59 echocardiographic features in a gradient-boosting classifier. This model showed that the value of the receiver operating characteristic area under the curve (AUC) was 0.852 in the test group and 0.834 in the validation group, with high sensitivity (0.952) and low specificity (0.691), suggesting that this model is very sensitive for detecting CAD, but its low specificity may increase the high false-positive rate. We also determined that the false-positive cases were more susceptible to suffering cardiac events than the true-negative cases.
conclusionsMachine learning-enhanced echocardiography can improve CAD detection based on the MW and left atrial strain features. Our developed model is valuable for estimating the pre-test probability of CAD and screening CAD patients in clinical practice.
trial registrationRegistered as NCT03905200 at ClinicalTrials.gov. Registered on 5 April 2019.
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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.