Volver a Buscador de AI
Marleen Vonder, Sunyi Zheng, Monique D.Dorrius, Carlijn M.van der Aalst, Harry J.de Koning, Jaeyoun Yi, Donghoon Yu, Jan Willem C. Gratama, Dirkjan Kuijpers, Matthijs Oudkerk, “Deep learning for automatic calcium scoring in population based cardiovascular screening“, JACC: Cardiovascular Imaging 15 (2021), pp.366-367, https://doi.org/10.1016/j.jcmg.2021.07.012
Aview: CAC
Quantitatively analyzes coronary artery calcification with AI technology.
Coreline’s aview CAC is based on deep learning AI technology. Quantifies coronary artery calcification and measures the risk of coronal arterial disease. With CAC’s automatic segmentation of the heart and surrounding structures, CAC can accurately analyze the calcified plaques in coronary arteries. The quantitatively analyzed coronary artery calcification index is a major indicator for diag– nosing coronary artery disease and helps patients’ treatment and management.
Applications and diseases
During: perception aid (prompting all abnormalities/results/heatmaps), interactive decision support (shows abnormalities/results only on demand), report suggestion.
CAC score per branch, automatic segmentation, arterial age, Agatston, Volume, Mass score.
Detectable pathologies
Advantages
- Accurate calcification detection in coronary arteries (Medical AI diagnostic accuracy: 99.2%. Detection and classification concordance: 87%. Agatston score concordance: 95%).
- Coronary artery calcification can be quantified not only on heart CT images but also on chest CT images, helping early detection, and reducing patient exposure.
- Coronary artery calcification score is provided for each blood vessel, and risk distribution by age group according to clinical criteria and blood vessel age are provided to help patient understanding.
References
Marleen Vonder, Sunyi Zheng, Monique D.Dorrius, Carlijn M.van der Aalst, Harry J.de Koning, Jaeyoun Yi, Donghoon Yu, Jan Willem C. Gratama, Dirkjan Kuijpers, Matthijs Oudkerk, “Deep learning for automatic calcium scoring in population based cardiovascular screening“, JACC: Cardiovascular Imaging 15 (2021), pp.366-367, https://doi.org/10.1016/j.jcmg.2021.07.012
