ArticleJournal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology2022
"Global" cardiac atherosclerotic burden assessed by artificial intelligence-based versus manual segmentation in
Article in Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- NaF-PET/CT imaging of atherosclerosis in type 2 diabetes: Associations with sex and history of cardiovascular events in a 2-year follow-up study.European journal of nuclear medicine and molecular imaging · 2025Article
- Training and assessing convolutional neural network performance in automatic vascular segmentation using Ga-68 DOTATATE PET/CT.The international journal of cardiovascular imaging · 2024Article
- Common carotid segmentation inClinical physiology and functional imaging · 2023Article
- NaF-PET Imaging of Atherosclerosis Burden.Journal of imaging · 2023Review
- "Global" cardiac atherosclerotic burden assessed by artificial intelligence-based versus manual segmentation inJournal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology · 2022Article
- Artificial intelligence-based quantification of cardiac 18F-sodium fluoride uptake.Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology · 2022Article
- Automated artificial intelligence quantification of aortic atherosclerotic calcifications by 18F-sodium fluoride PET/CT.Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology · 2022Article
- Alzheimer's Disease at a Crossroad: Time to Part from Amyloid to More Promising Aspects-Atherosclerosis for a Start.Journal of Alzheimer's disease : JAD · 2022Article
- PET-Based Imaging withDiagnostics (Basel, Switzerland) · 2021Review
- Alavi-Carlsen Calcification Score (ACCS): A Simple Measure of Global Cardiac Atherosclerosis Burden.Diagnostics (Basel, Switzerland) · 2021Article
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Abstract
backgroundArtificial intelligence (AI) is known to provide effective means to accelerate and facilitate clinical and research processes. So in this study it was aimed to compare a AI-based method for cardiac segmentation in positron emission tomography/computed tomography (PET/CT) scans with manual segmentation to assess global cardiac atherosclerosis burden.
methodsA trained convolutional neural network (CNN) was used for cardiac segmentation in
resultsMean (± SD) values with manual vs. CNN-based segmentation were Vol 617.65 ± 154.99 mL vs 625.26 ± 153.55 mL (P = .21), SUVmean 0.69 ± 0.15 vs 0.69 ± 0.15 (P = .26), SUVmax 2.68 ± 0.86 vs 2.77 ± 1.05 (P = .34), and SUVtotal 425.51 ± 138.93 vs 427.91 ± 132.68 (P = .62). Limits of agreement were - 89.42 to 74.2, - 0.02 to 0.02, - 1.52 to 1.32, and - 68.02 to 63.21, respectively. Manual segmentation lasted typically 30 minutes vs about one minute with the CNN-based approach. The maximal deviation at manual re-segmentation was for the four parameters 0% to 0.5% with the same and 0% to 1% with different operators.
conclusionThe CNN-based method was faster and provided values for Vol, SUVmean, SUVmax, and SUVtotal comparable to the manually obtained ones. This AI-based segmentation approach appears to offer a more reproducible and much faster substitute for slow and cumbersome manual segmentation of the heart.
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