News & Publications
News & Publications
News & Publications
News
News
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NIH STTR Phase I grant award (Cardiovascular risk prediction from AI analysis of coronary calcifications, R41HL180169, 08/2025 – 08/2026). We will create software for predicting cardiovascular health from a low-cost CT calcium score examination. We will use AI to greatly improve the existing CT calcium score method, creating a method that physicians and patients can use in shared decision-making to personalize interventions.
NIH STTR Phase I grant award (Cardiovascular risk prediction from AI analysis of coronary calcifications, R41HL180169, 08/2025 – 08/2026). We will create software for predicting cardiovascular health from a low-cost CT calcium score examination. We will use AI to greatly improve the existing CT calcium score method, creating a method that physicians and patients can use in shared decision-making to personalize interventions.
NIH STTR Phase I grant award (Cardiovascular risk prediction from AI analysis of coronary calcifications, R41HL180169, 08/2025 – 08/2026). We will create software for predicting cardiovascular health from a low-cost CT calcium score examination. We will use AI to greatly improve the existing CT calcium score method, creating a method that physicians and patients can use in shared decision-making to personalize interventions.
Journal publications
Journal publications
1.
1.
J.N. Kim, et al., Hybrid deep learning time-to-event modeling of major adverse cardiovascular events using coronary artery calcium score scans, European Journal of Radiology Artificial Intelligence, 100109, 2026.
J.N. Kim, et al., Hybrid deep learning time-to-event modeling of major adverse cardiovascular events using coronary artery calcium score scans, European Journal of Radiology Artificial Intelligence, 100109, 2026.
2.
2.
P. Singh, et al., Sub-Agatston coronary calcification on non-contrast cardiac CT and cardiovascular risk, European Journal of Preventive Cardiology, zwag285, 2026.
P. Singh, et al., Sub-Agatston coronary calcification on non-contrast cardiac CT and cardiovascular risk, European Journal of Preventive Cardiology, zwag285, 2026.
3.
H. Wu, et al., Quantitative cardiac CT perfusion: physiologically-inspired model and identifying microvascular disease from discordant CTA CAD-RADS, Frontiers in Cardiovascular Medicine, 12:1621443, 2025.
H. Wu, et al., Quantitative cardiac CT perfusion: physiologically-inspired model and identifying microvascular disease from discordant CTA CAD-RADS, Frontiers in Cardiovascular Medicine, 12:1621443, 2025.
4.
4.
J. Lee, et al., Computational analysis of intravascular OCT images for future clinical support: a comprehensive review, IEEE Reviews in Biomedical Engineering, 14, 2025.
J. Lee, et al., Computational analysis of intravascular OCT images for future clinical support: a comprehensive review, IEEE Reviews in Biomedical Engineering, 14, 2025.
5.
5.
J. Lee, et al., Prediction of obstructive coronary artery disease using coronary calcification and epicardial adipose tissue assessments from CT calcium scoring scan, Journal of Cardiovascular Computed Tomography, 19(2): 224-231, 2025.
J. Lee, et al., Prediction of obstructive coronary artery disease using coronary calcification and epicardial adipose tissue assessments from CT calcium scoring scan, Journal of Cardiovascular Computed Tomography, 19(2): 224-231, 2025.
6.
6.
J. Lee, et al., Detection of arterial remodeling using epicardial adipose tissue assessment from CT calcium scoring scan, Frontiers in Cardiovascular Medicine, 12: 1543816, 2025.
J. Lee, et al., Detection of arterial remodeling using epicardial adipose tissue assessment from CT calcium scoring scan, Frontiers in Cardiovascular Medicine, 12: 1543816, 2025.
7.
7.
J. Kim, et al., Improving coronary artery segmentation with self-supervised learning and automated pericoronary adipose tissue segmentation: a multi-institutional coronary CT angiography study, Journal of Medical Imaging, 12(1): 016002, 2025.
J. Kim, et al., Improving coronary artery segmentation with self-supervised learning and automated pericoronary adipose tissue segmentation: a multi-institutional coronary CT angiography study, Journal of Medical Imaging, 12(1): 016002, 2025.
8.
8.
H. Wu, et al., Cardiac CT perfusion imaging of pericoronary adipose tissue (PCAT) highlighting potential lconfounds in CTA analysis, Journal of Clinical Medicine, 14(3): 769, 2025.
H. Wu, et al., Cardiac CT perfusion imaging of pericoronary adipose tissue (PCAT) highlighting potential lconfounds in CTA analysis, Journal of Clinical Medicine, 14(3): 769, 2025.
9.
9.
Y. Song, et al., Pericoronary adipose tissue (PCAT) feature analysis in CT calcium score images with comparison to coronary CTA, Journal of Medical Imaging, 12(1): 014503, 2025.
Y. Song, et al., Pericoronary adipose tissue (PCAT) feature analysis in CT calcium score images with comparison to coronary CTA, Journal of Medical Imaging, 12(1): 014503, 2025.
10.
10.
T. Hu, et al., Artificial intelligence prediction of cardiovascular events using opportunistic epicardial adipose tissue assessments from computed tomography calcium score, JACC: Advances, 3(9), 101188, 2024.
T. Hu, et al., Artificial intelligence prediction of cardiovascular events using opportunistic epicardial adipose tissue assessments from computed tomography calcium score, JACC: Advances, 3(9), 101188, 2024.
11.
J. Lee, et al., Plaque characteristics derived from intravascular optical coherence tomography that predict major adverse cardiovascular events, Bioengineering, 11(8), 843, 2024.
J. Lee, et al., Plaque characteristics derived from intravascular optical coherence tomography that predict major adverse cardiovascular events, Bioengineering, 11(8), 843, 2024.
12.
12.
A. Hoori, et al., Enhancing cardiovascular risk prediction through AI-enabled calcium-omics, Nature – Scientific Reports, 14, 11134, 2024.
A. Hoori, et al., Enhancing cardiovascular risk prediction through AI-enabled calcium-omics, Nature – Scientific Reports, 14, 11134, 2024.
13.
13.
J. Lee, et al., Deep learning segmentation of fibrous cap in intravascular optical coherence tomography images, Nature – Scientific Reports, 14(1), 4393, 2024.
J. Lee, et al., Deep learning segmentation of fibrous cap in intravascular optical coherence tomography images, Nature – Scientific Reports, 14(1), 4393, 2024.
14.
14.
P. Singh, et al., Leveraging calcium score CT radiomics for heart failure risk prediction, Nature – Scientific Reports, 14(1), 26898, 2024.
P. Singh, et al., Leveraging calcium score CT radiomics for heart failure risk prediction, Nature – Scientific Reports, 14(1), 26898, 2024.
15.
15.
Y. Gharaibeh, et al., Prediction of stent under-expansion in calcified coronary arteries using machine learning on intravascular optical coherence tomography images, Nature – Scientific Reports, 13(1), 18110, 2023.
Y. Gharaibeh, et al., Prediction of stent under-expansion in calcified coronary arteries using machine learning on intravascular optical coherence tomography images, Nature – Scientific Reports, 13(1), 18110, 2023.
16.
16.
J. Kim, et al., Pericoronary adipose tissue radiomics from coronary CT angiography identifies vulnerable plaques characteristics in intravascular OCT, Bioengineering, 10(3), 360, 2023.
J. Kim, et al., Pericoronary adipose tissue radiomics from coronary CT angiography identifies vulnerable plaques characteristics in intravascular OCT, Bioengineering, 10(3), 360, 2023.
17.
17.
J. Lee, et al., OCTOPUS – optical coherence tomography plaque and stent analysis software, Heliyon, 9, e13396, 2023.
J. Lee, et al., OCTOPUS – optical coherence tomography plaque and stent analysis software, Heliyon, 9, e13396, 2023.
18.
18.
J. Lee, et al., Neoatherosclerosis prediction using plaque markers in intravascular optical coherence tomography images, Frontiers in Cardiovascular Medicine, 9:1079046, 2022.
J. Lee, et al., Neoatherosclerosis prediction using plaque markers in intravascular optical coherence tomography images, Frontiers in Cardiovascular Medicine, 9:1079046, 2022.
19.
19.
J. Lee, et al., Automated analysis of fibrous cap in intravascular optical coherence tomography images of coronary arteries, Nature – Scientific Reports, 12, 21454, 2022.
J. Lee, et al., Automated analysis of fibrous cap in intravascular optical coherence tomography images of coronary arteries, Nature – Scientific Reports, 12, 21454, 2022.
20.
20.
J. Lee, et al., Automated segmentation of microvessels in intravascular OCT images using deep learning, Bioengineering, 9, 648, 2022.
J. Lee, et al., Automated segmentation of microvessels in intravascular OCT images using deep learning, Bioengineering, 9, 648, 2022.
21.
21.
L. A. P. Dallan, et al., Assessment of post-dilatation strategies for optimal stent expansion in calcified coronary lesions: ex vivo analysis with optical coherence tomography, Cardiovascular Revascularization Medicine, 43:62-70, 2022.
L. A. P. Dallan, et al., Assessment of post-dilatation strategies for optimal stent expansion in calcified coronary lesions: ex vivo analysis with optical coherence tomography, Cardiovascular Revascularization Medicine, 43:62-70, 2022.
22.
22.
A. Hoori, et al., Deep learning segmentation and quantification method for assessing epicardial adipose tissue in CT calcium score scans, Nature – Scientific Reports, 12, 2276, 2022.
A. Hoori, et al., Deep learning segmentation and quantification method for assessing epicardial adipose tissue in CT calcium score scans, Nature – Scientific Reports, 12, 2276, 2022.
23.
23.
C. Kolluru, et al., Learning with fewer images via image clustering: application to intravascular OCT image segmentation, IEEE Access, 9, 37273-37280, 2021.
C. Kolluru, et al., Learning with fewer images via image clustering: application to intravascular OCT image segmentation, IEEE Access, 9, 37273-37280, 2021.
24.
24.
J. Lee, et al., Segmentation of coronary calcified plaque in intravascular OCT images using a two-step deep learning approach, IEEE Access, 8, 225581-225593, 2020.
J. Lee, et al., Segmentation of coronary calcified plaque in intravascular OCT images using a two-step deep learning approach, IEEE Access, 8, 225581-225593, 2020.
25.
25.
J. Lee, et al., Fully automated plaque characterization in intravascular OCT images using hybrid convolutional and lumen morphology features, Nature - Scientific Reports, 10(1), 2596, 2020.
J. Lee, et al., Fully automated plaque characterization in intravascular OCT images using hybrid convolutional and lumen morphology features, Nature - Scientific Reports, 10(1), 2596, 2020.
26.
26.
H. Lu, et al., Application and evaluation of highly automated software for comprehensive stent analysis in intravascular optical coherence tomography, Nature - Scientific Reports, 10, 2150, 2020.
H. Lu, et al., Application and evaluation of highly automated software for comprehensive stent analysis in intravascular optical coherence tomography, Nature - Scientific Reports, 10, 2150, 2020.