ArticleJACC. Cardiovascular imaging2018
Deep Learning for Prediction of Obstructive Disease From Fast Myocardial Perfusion SPECT: A Multicenter Study.
Article in JACC. Cardiovascular imaging, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 159 papers, 2 of them syntheses that pooled it.
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
159 citing papers in PubMed, 2 syntheses or guidelines pooled it, 342 citations in OpenAlex.
- Comparing machine learning models and traditional approaches for predicting obstructive coronary artery disease: a systematic review and meta-analysis.Frontiers in cardiovascular medicine · 2026Pooled it
- E-Cardiac Care: A Comprehensive Systematic Literature Review.Sensors (Basel, Switzerland) · 2022Pooled it
- Artificial intelligence fully automated myocardial strain quantification for risk stratification following acute myocardial infarction.Scientific reports · 2022Trial
- Deep Learning-Based Quantification of Epicardial Adipose Tissue Volume and Attenuation Predicts Major Adverse Cardiovascular Events in Asymptomatic Subjects.Circulation. Cardiovascular imaging · 2020Trial
- A deep learning-based algorithm for detecting significant in-stent restenosis in coronary CT angiography.The international journal of cardiovascular imaging · 2026Article
- Variational autoencoders can detect coronary artery disease in myocardial perfusion SPECT images.European journal of nuclear medicine and molecular imaging · 2026Article
- From Automated ECG Interpretation to Multimodal Cardiovascular Intelligence: The Evolution of Artificial Intelligence in Cardiovascular Medicine.Medical sciences (Basel, Switzerland) · 2026Review
- A Prototype-Guided 3D Deep Learning Framework for Myocardial Perfusion Scintigraphy Segmentation.Journal of clinical medicine · 2026Review
- Explainable and Trustworthy Artificial Intelligence in Cardiology: A Narrative Review of Clinical Applications, Operational Integration, and Future Directions.Journal of clinical medicine · 2026Review
- Artificial intelligence across the cardiovascular diagnostic pathway: a case-based narrative review.Cardiovascular diagnosis and therapy · 2026Review
- Artificial intelligence in cardiology in the current era: a narrative review.Cardiovascular diagnosis and therapy · 2026Review
- Clinical Applications of Artificial Intelligence in Cardiovascular Imaging: Where Do We Stand?Life (Basel, Switzerland) · 2026Review
- Artificial Intelligence in Cardiovascular Imaging: From Automated Acquisition to Precision Diagnostics and Clinical Decision Support.Medical sciences (Basel, Switzerland) · 2026Review
- Deep learning-based obstructive coronary artery disease prediction from myocardial perfusion SPECT.European journal of nuclear medicine and molecular imaging · 2026Article
- Artificial Intelligence and the Expanding Universe of Cardio-Oncology: Beyond Detection Toward Prediction and Prevention of Therapy-Related Cardiotoxicity-A Comprehensive Review.Diagnostics (Basel, Switzerland) · 2026Review
- Traditional Chinese Medicine Modernization in Diagnosis and Treatment: Utilizing Artificial Intelligence and Nanotechnology.MedComm · 2026Review
- D-SPECT combined with deep learning predicts obstructive coronary artery disease.Frontiers in cardiovascular medicine · 2026Article
- Machine learning and multi-omics technologies for precision cardiovascular medicine: advancing diagnosis, risk prediction, and therapeutic guidance.Frontiers in cardiovascular medicine · 2026Review
- Artificial Intelligence in Nuclear Cardiology.Journal of clinical medicine · 2025Review
- Cross-institutional validation of a polar map-free 3D deep learning model for obstructive coronary artery disease prediction using myocardial perfusion imaging: insights into generalizability and bias.European journal of nuclear medicine and molecular imaging · 2025Article
99 more citing papers are in PubMed but not listed here.
Corrections and comments
- Commented on by
Authors and funding
20 authors at 9 institutions in 4 countries.
Funding
Abstract
objectivesThe study evaluated the automatic prediction of obstructive disease from myocardial perfusion imaging (MPI) by deep learning as compared with total perfusion deficit (TPD).
backgroundDeep convolutional neural networks trained with a large multicenter population may provide improved prediction of per-patient and per-vessel coronary artery disease from single-photon emission computed tomography MPI.
methodsA total of 1,638 patients (67% men) without known coronary artery disease, undergoing stress
resultsA total of 1,018 (62%) patients and 1,797 of 4,914 (37%) arteries had obstructive disease. Area under the receiver-operating characteristic curve for disease prediction by deep learning was higher than for TPD (per patient: 0.80 vs. 0.78; per vessel: 0.76 vs. 0.73: p < 0.01). With deep learning threshold set to the same specificity as TPD, per-patient sensitivity improved from 79.8% (TPD) to 82.3% (deep learning) (p < 0.05), and per-vessel sensitivity improved from 64.4% (TPD) to 69.8% (deep learning) (p < 0.01).
conclusionsDeep learning has the potential to improve automatic interpretation of MPI as compared with current clinical methods.
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.