ArticleCardiovascular diagnosis and therapy2020
Machine learning-based CT fractional flow reserve assessment in acute chest pain: first experience.
Article in Cardiovascular diagnosis and therapy, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT04682756 (A Multicenter Study on Early Diagnosis of NSTE-ACS Patients Based on Machine Learning Model), which is not on this map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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.
A Multicenter Study on Early Diagnosis of NSTE-ACS Patients Based on Machine Learning Model
Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it, 23 citations in OpenAlex.
- Functional Coronary Artery Assessment: a Systematic Literature Review.Wiener klinische Wochenschrift · 2022Pooled it
- A Novel Demography-Based Approach to Define Patient-Specific Outflow Boundary Conditions in CT-Based FFR Computations.Annals of biomedical engineering · 2026Article
- Clinical use of coronary computed tomography angiography-derived fractional flow reserve: expert consensus by an International Working Group.European radiology · 2026Article
- Plaque Progression and Rupture in Obstructive Coronary Artery Disease: A Review of Current Imaging Modalities.Reviews in cardiovascular medicine · 2026Review
- Article
- Increasing the rate of datasets amenable to CTEuropean journal of radiology open · 2024Article
- Early Diagnosis of Cardiovascular Diseases in the Era of Artificial Intelligence: An In-Depth Review.Cureus · 2024Review
- Evaluating the Efficacy of ChatGPT in Navigating the Spanish Medical Residency Entrance Examination (MIR): Promising Horizons for AI in Clinical Medicine.Clinics and practice · 2023Article
- AI Algorithm to Predict Acute Coronary Syndrome in Prehospital Cardiac Care: Retrospective Cohort Study.JMIR cardio · 2023Article
- Deep Learning Model for Coronary Angiography.Journal of cardiovascular translational research · 2023Article
- In search of new gatekeepers: coronary CT (Computed Tomography) in acute coronary syndrome.European heart journal supplements : journal of the European Society of Cardiology · 2023Article
- Artificial Intelligence - Advisory or Adversary?Interventional cardiology (London, England) · 2023Article
- Deep learning applications in coronary anatomy imaging: a systematic review and meta-analysis.Journal of medical artificial intelligence · 2022Article
- Clinical Applications of Artificial Intelligence-An Updated Overview.Journal of clinical medicine · 2022Review
- Long-term prognostic value of the serial changes of CT-derived fractional flow reserve and perivascular fat attenuation index.Quantitative imaging in medicine and surgery · 2022Article
- Artificial Intelligence-A Good Assistant to Multi-Modality Imaging in Managing Acute Coronary Syndrome.Frontiers in cardiovascular medicine · 2021Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundComputed tomography (CT)-derived fractional flow reserve (FFR
methodsFor this retrospective IRB-approved study, we included 56 patients (median age: 62 years, 14 females) with acute chest pain who underwent CPCT and who had at least a mild (≥25% diameter) coronary artery stenosis. CPCT was evaluated for the presence of acute plaque rupture and vulnerable plaque features. FFR
resultsFFR
conclusionsOur preliminary data show that FFR
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.