ArticleEJNMMI research2022
Convolutional neural networks for detection of transthyretin amyloidosis in 2D scintigraphy images.
Article in EJNMMI research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
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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.
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Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it, 28 citations in OpenAlex.
- Prevalence of cardiac amyloidosis in screening studies: a systematic review and meta-analysis.ESC heart failure · 2026Pooled it
- Screening for patients at risk for cardiac amyloidosis via electronic health records: A multicenter machine learning development and validation study.PLOS digital health · 2026Article
- Artificial Intelligence in Cardiac Amyloidosis: A State-of-the-Art Review.Journal of clinical medicine · 2026Review
- Incidental Cardiac Uptake onDiseases (Basel, Switzerland) · 2026Review
- Artificial Intelligence for the Diagnosis of Transthyretin Amyloid Cardiomyopathy: A Systematic Review of Machine Learning Application.Cardiology research and practice · 2026Review
- Transthyretin cardiac amyloidosis: advances and ambiguities.Heart failure reviews · 2025Review
- Artificial Intelligence in Nuclear Cardiology.Journal of clinical medicine · 2025Review
- The Role of Artificial Intelligence in Cardiac Amyloidosis: A Focus on Diagnosis and Clinical Application.Journal of cardiovascular development and disease · 2025Review
- Artificial intelligence-based cardiac transthyretin amyloidosis detection and scoring in scintigraphy imaging: multi-tracer, multi-scanner, and multi-center development and evaluation study.European journal of nuclear medicine and molecular imaging · 2025Article
- 2025 Update Consensus ofActa Cardiologica Sinica · 2025Article
- Multi-modality artificial intelligence-based transthyretin amyloid cardiomyopathy detection in patients with severe aortic stenosis.European journal of nuclear medicine and molecular imaging · 2025Article
- Review
- How Artificial Intelligence Can Enhance the Diagnosis of Cardiac Amyloidosis: A Review of Recent Advances and Challenges.Journal of cardiovascular development and disease · 2024Review
- A review of evaluation approaches for explainable AI with applications in cardiology.Artificial intelligence review · 2024Article
- A Deep Learning-Based Model for Classifying Osteoporotic Lumbar Vertebral Fractures on Radiographs: A Retrospective Model Development and Validation Study.Journal of imaging · 2023Article
- Prevalence of Incidental Findings Suspicious for Transthyretin Cardiac Amyloidosis among Patients Undergoing Bone Scintigraphy: A Systematic Review and a Meta-Analysis.Journal of clinical medicine · 2023Review
- Deep Learning to Classify AL versus ATTR Cardiac Amyloidosis MR Images.Biomedicines · 2023Article
Corrections and comments
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Authors and funding
8 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundTransthyretin amyloidosis (ATTR) is a progressive disease which can be diagnosed non-invasively using bone avid [
resultsOur CNN models performed better than, or equally well as, the state-of-the-art models in detection and classification of cardiac uptake. Both models achieved area under the curve (AUC) ≥ 0.85 in the four-class Perugini grade classification. Accuracy was good in detection of negative vs. positive ATTR patients (grade < 2 vs grade ≥ 2, AUC > 0.88) and high-grade cardiac uptake vs. other patients (grade < 3 vs. grade 3, AUC = 0.94). Maximum activation maps demonstrated that the automated deep learning models were focused on detecting the myocardium and not extracardiac features.
conclusionAutomated convolutional neural networks can accurately detect and classify different grades of cardiac uptake on bone scintigraphy. The CNN models are focused on clinically relevant image features. Automated screening of bone scintigraphy images using CNN could improve the early diagnosis of ATTR.
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Registered trials
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