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Your search keyword '"Huang, Cathleen"' showing total 19 results
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19 results on '"Huang, Cathleen"'

4. Unsupervised learning to characterize patients with known coronary artery disease undergoing myocardial perfusion imaging

Author

Williams, Michelle C., Bednarski, Bryan P., Pieszko, Konrad, Miller, Robert J. H., Kwiecinski, Jacek, Shanbhag, Aakash, Liang, Joanna X., Huang, Cathleen, Sharir, Tali, Dorbala, Sharmila, Di Carli, Marcelo F., Einstein, Andrew J., Sinusas, Albert J., Miller, Edward J., Bateman, Timothy M., Fish, Mathews B., Ruddy, Terrence D., Acampa, Wanda, Hauser, M. Timothy, Kaufmann, Philipp A., Dey, Damini, Berman, Daniel S., and Slomka, Piotr J.

Published
2023
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6. Clinical phenotypes among patients with normal cardiac perfusion using unsupervised learning: a retrospective observational study

Author

Miller, Robert J.H., Bednarski, Bryan P., Pieszko, Konrad, Kwiecinski, Jacek, Williams, Michelle C., Shanbhag, Aakash, Liang, Joanna X., Huang, Cathleen, Sharir, Tali, Hauser, M. Timothy, Dorbala, Sharmila, Di Carli, Marcelo F., Fish, Mathews B., Ruddy, Terrence D., Bateman, Timothy M., Einstein, Andrew J., Kaufmann, Philipp A., Miller, Edward J., Sinusas, Albert J., Acampa, Wanda, Han, Donghee, Dey, Damini, Berman, Daniel S., and Slomka, Piotr J.

Published
2024
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8. Direct Risk Assessment From Myocardial Perfusion Imaging Using Explainable Deep Learning

Author

Singh, Ananya, Miller, Robert J.H., Otaki, Yuka, Kavanagh, Paul, Hauser, Michael T., Tzolos, Evangelos, Kwiecinski, Jacek, Van Kriekinge, Serge, Wei, Chih-Chun, Sharir, Tali, Einstein, Andrew J., Fish, Mathews B., Ruddy, Terrence D., Kaufmann, Philipp A., Sinusas, Albert J., Miller, Edward J., Bateman, Timothy M., Dorbala, Sharmila, Di Carli, Marcelo, Liang, Joanna X., Huang, Cathleen, Han, Donghee, Dey, Damini, Berman, Daniel S., and Slomka, Piotr J.

Published
2023
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11. Predictingmortality from AI cardiac volumes mass and coronary calcium on chest computed tomography.

Author

Miller, Robert J. H., Killekar, Aditya, Shanbhag, Aakash, Bednarski, Bryan, Michalowska, Anna M., Ruddy, Terrence D., Einstein, Andrew J., Newby, David E., Lemley, Mark, Pieszko, Konrad, Van Kriekinge, Serge D., Kavanagh, Paul B., Liang, Joanna X., Huang, Cathleen, Dey, Damini, Berman, Daniel S., and Slomka, Piotr J.

Abstract

Chest computed tomography is one of the most common diagnostic tests, with 15 million scans performed annually in the United States. Coronary calcium can be visualized on these scans, but other measures of cardiac risk such as atrial and ventricular volumes have classically required administration of contrast. Here we show that a fully automated pipeline, incorporating two artificial intelligence models, automatically quantifies coronary calcium, left atrial volume, left ventricular mass, and other cardiac chamber volumes in 29,687 patients from three cohorts. The model processes chamber volumes and coronary artery calcium with an end-to-end time of ~18 s, while failing to segment only 0.1% of cases. Coronary calcium, left atrial volume, and left ventricular mass index are independently associated with all-cause and cardiovascular mortality and significantly improve risk classification compared to identification of abnormalities by a radiologist. This automated approach can be integrated into clinical workflows to improve identification of abnormalities and risk stratification, allowing physicians to improve clinical decision-making. [ABSTRACT FROM AUTHOR]

Published
2024
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13. Correction to: Integration of coronary artery calcium scoring from CT attenuation scans by machine learning improves prediction of adverse cardiovascular events in patients undergoing SPECT/CT myocardial perfusion imaging.

Author

Feher, Attila, Pieszko, Konrad, Miller, Robert, Lemley, Mark, Shanbhag, Aakash, Huang, Cathleen, Miras, Leonidas, Liu, Yi-Hwa, Sinusas, Albert J., Miller, Edward J., and Slomka, Piotr J.

Abstract

N survival analysis patients with high CAC-ML score (> 0.091) had higher event rate when compared to patients with low CAC-ML score (CI 5.2, 95% CI 4.2-6.5, I P i <.001)." The positive and negative NRI were 0.06 (95% CI 0.02, 0.1) and - 0.01 (95% CI - 0.02, - 0.004), respectively"."Patients with high CAC-ML score (> 0.091, had higher event rate when compared to patients with low CAC-ML score (< 0.091, HR 5.2, 95% CI 4.2-6.5, I P i <.001) (Figure 4B).". [Extracted from the article]

Published
2023
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