Trial reportJournal of the American Heart Association2019
Angiography-Based Machine Learning for Predicting Fractional Flow Reserve in Intermediate Coronary Artery Lesions.
Trial report in Journal of the American Heart Association, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers, 1 of them a synthesis that pooled it.
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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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
35 citing papers in PubMed, 1 synthesis or guideline pooled it, 79 citations in OpenAlex.
- Artificial intelligence in estimating fractional flow reserve: a systematic literature review of techniques.BMC cardiovascular disorders · 2023Pooled it
- Angiography-Based Machine Learning for Predicting Fractional Flow Reserve in Intermediate Coronary Artery Lesions.Journal of the American Heart Association · 2019Trial
- Optimizing Non-invasive Fractional Flow Reserve Estimation with Machine Learning-Enhanced 1D Hemodynamic Modeling.Cardiovascular engineering and technology · 2026Article
- Integrating artificial intelligence into paediatric interventional radiology: a review of emerging applications and future directions.Pediatric radiology · 2026Review
- Artificial intelligence across the cardiovascular diagnostic pathway: a case-based narrative review.Cardiovascular diagnosis and therapy · 2026Review
- Article
- Mediation of stenosis-induced ischemia by proximal peri-coronary adipose tissue: A cross-sectional study.Medicine · 2026Article
- Clinical Performance Evaluation of an Artificial Intelligence-Based Tool for Predicting the Presence of Obstructive Coronary Artery Disease: Protocol for a Cohort Observational Study.JMIR research protocols · 2025Article
- Validation of machine learning angiography-derived physiological pattern of coronary artery disease.European heart journal. Digital health · 2025Article
- Review
- Artificial Intelligence based fractional flow reserve.Cardiology journal · 2025Review
- Old Habits Die Hard: Can AI Help Bring Coronary Angiography Into the 21st Century?JACC. Advances · 2024Article
- Temporal Relationship-Aware Treadmill Exercise Test Analysis Network for Coronary Artery Disease Diagnosis.Sensors (Basel, Switzerland) · 2024Article
- Non-invasive fractional flow reserve estimation using deep learning on intermediate left anterior descending coronary artery lesion angiography images.Scientific reports · 2024Article
- Assessment of the functional severity of coronary lesions from optical coherence tomography based on ensembled learning.Biomedical engineering online · 2023Article
- Artificial intelligence in cardiovascular diseases: diagnostic and therapeutic perspectives.European journal of medical research · 2023Review
- Artificial Intelligence, Augmented Reality, and Virtual Reality Advances and Applications in Interventional Radiology.Diagnostics (Basel, Switzerland) · 2023Review
- Assessment of fractional flow reserve in intermediate coronary stenosis using optical coherence tomography-based machine learning.Frontiers in cardiovascular medicine · 2023Article
- Deep learning-based prediction of future myocardial infarction using invasive coronary angiography: a feasibility study.Open heart · 2023Article
- Deep learning applications in coronary anatomy imaging: a systematic review and meta-analysis.Journal of medical artificial intelligence · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
17 authors at 5 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background An angiography-based supervised machine learning ( ML ) algorithm was developed to classify lesions as having fractional flow reserve ≤0.80 versus >0.80. Methods and Results With a 4:1 ratio, 1501 patients with 1501 intermediate lesions were randomized into training versus test sets. Between the ostium and 10 mm distal to the target lesion, a series of angiographic lumen diameter measurements along the centerline was plotted. The 24 computed angiographic features based on the diameter plot and 4 clinical features (age, sex, body surface area, and involve segment) were used for ML by XGBoost. The model was independently trained and tested by 2000 bootstrap iterations. External validation with 79 patients was conducted. Including all 28 features, the ML model with 5-fold cross-validation in the 1204 training samples predicted fractional flow reserve ≤0.80 with overall diagnostic accuracy of 78±4% (averaged area under the curve: 0.84±0.03). The 12 high-ranking features selected by scatter search were involved segment; body surface area; distal lumen diameter; minimal lumen diameter; length of a lumen diameter <2.0 mm, <1.5 mm, and <1.25 mm; mean lumen diameter within the worst segment; sex; diameter stenosis; distal 5-mm reference lumen diameter; and length of diameter stenosis >70%. Using those 12 features, the ML predicted fractional flow reserve ≤0.80 in the test set with sensitivity of 84%, specificity of 80%, and overall accuracy of 82% (area under the curve: 0.87). The averaged diagnostic accuracy in bootstrap replicates was 81±1% (averaged area under the curve: 0.87±0.01). External validation showed accuracy of 85% (area under the curve: 0.87). Conclusions Angiography-based ML showed good diagnostic performance in identifying ischemia-producing lesions and reduced the need for pressure wires.
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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.