ArticleThe international journal of cardiovascular imaging2021
Deep-learning-based cardiac amyloidosis classification from early acquired pet images.
Article in The international journal of cardiovascular imaging, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Diagnostic value of positron emission tomography using amyloid-binding radiotracers for cardiac amyloidosis: a systematic review and meta-analysis.European heart journal. Imaging methods and practice · 2026Article
- Artificial Intelligence for the Diagnosis of Transthyretin Amyloid Cardiomyopathy: A Systematic Review of Machine Learning Application.Cardiology research and practice · 2026Review
- Assessing the quality of reporting in artificial intelligence/machine learning research for cardiac amyloidosis.JAMIA open · 2025Article
- Automated Neural Architecture Search for Cardiac Amyloidosis Classification from [18F]-Florbetaben PET Images.Journal of imaging informatics in medicine · 2025Article
- Positron emission tomography in cardiac amyloidosis: current evidence and future directions.Heart failure reviews · 2025Review
- Multi-modality artificial intelligence-based transthyretin amyloid cardiomyopathy detection in patients with severe aortic stenosis.European journal of nuclear medicine and molecular imaging · 2025Article
- Novel Insights into Non-Invasive Diagnostic Techniques for Cardiac Amyloidosis: A Critical Review.Diagnostics (Basel, Switzerland) · 2024Review
- A look at radiation detectors and their applications in medical imaging.Japanese journal of radiology · 2024Review
- Examining the Difficulties in Identifying and Handling Cardiac Amyloidosis; Acquiring Important Knowledge and Robust Treatment Methods.Cardiovascular & hematological disorders drug targets · 2024Review
- 2022 Artificial intelligence primer for the nuclear cardiologist.Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology · 2023Review
- Bayesian Convolutional Neural Networks in Medical Imaging Classification: A Promising Solution for Deep Learning Limits in Data Scarcity Scenarios.Journal of digital imaging · 2023Article
- Deep Learning to Classify AL versus ATTR Cardiac Amyloidosis MR Images.Biomedicines · 2023Article
- AL Amyloidosis for Cardiologists: Awareness, Diagnosis, and Future Prospects:JACC. CardioOncology · 2022Review
- Nuclear Molecular Imaging of Disease Burden and Response to Treatment for Cardiac Amyloidosis.Biology · 2022Review
- Convolutional neural networks for detection of transthyretin amyloidosis in 2D scintigraphy images.EJNMMI research · 2022Article
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The objective of the present work was to evaluate the potential of deep learning tools for characterizing the presence of cardiac amyloidosis from early acquired PET images, i.e. 15 min after [18F]-Florbetaben tracer injection. 47 subjects were included in the study: 13 patients with transthyretin-related amyloidosis cardiac amyloidosis (ATTR-CA), 15 patients with immunoglobulin light-chain amyloidosis (AL-CA), and 19 control-patients (CTRL). [18F]-Florbetaben PET/CT images were acquired in list mode and data was sorted into a sinogram, covering a time interval of 5 min starting 15 min after the injection. The resulting sinogram was reconstructed using OSEM iterative algorithm. A deep convolutional neural network (CAclassNet) was designed and implemented, consisting of five 2D convolutional layers, three fully connected layers and a final classifier returning AL, ATTR and CTRL scores. A total of 1107 2D images (375 from AL-subtype patients, 312 from ATTR-subtype, and 420 from Controls) have been considered in the study and used to train, validate and test the proposed network. CAclassNet cross-validation resulted with train error mean ± sd of 2.001% ± 0.96%, validation error of 4.5% ± 2.26%, and net accuracy of 95.49% ± 2.26%. Network test error resulted in a mean ± sd values of 10.73% ± 0.76%. Sensitivity, specificity, and accuracy evaluated on the test dataset were respectively for AL-CA sub-type: 1, 0.912, 0.936; for ATTR-CA: 0.935, 0.897, 0.972; for control subjects: 0.809, 0.971, 0.909. In conclusion, the proposed CAclassNet model seems very promising as an aid for the clinician in the diagnosis of CA from cardiac [18F]-Florbetaben PET images acquired a few minutes after the injection.
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