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Aditya Killekar

Showing results (21-30 of 27) with videos related to

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Journal of Medical Imaging (Bellingham, Wash.)|September 12, 2022
Rapid quantification of COVID-19 pneumonia burden from computed tomography with convolutional long short-term memory networksAditya Killekar, Kajetan Grodecki, Andrew Lin, et al.
Medrxiv : the Preprint Server for Health Sciences|August 12, 2024
AI-based volumetric six-tissue body composition quantification from CT cardiac attenuation scans enhances mortality prediction: multicenter studyJirong Yi, Anna M Michalowska, Aakash Shanbhag, et al.
The Lancet. Digital Health|May 17, 2025
AI-based volumetric six-tissue body composition quantification from CT cardiac attenuation scans for mortality prediction: a multicentre studyJirong Yi, Anna M Marcinkiewicz, Aakash Shanbhag, et al.
The British Journal of Radiology|June 13, 2023
Artificial intelligence-assisted quantification of COVID-19 pneumonia burden from computed tomography improves prediction of adverse outcomes over visual scoring systemsKajetan Grodecki, Aditya Killekar, Judit Simon, et al.
The Lancet. Digital Health|March 26, 2022
Deep learning-enabled coronary CT angiography for plaque and stenosis quantification and cardiac risk prediction: an international multicentre studyAndrew Lin, Nipun Manral, Priscilla McElhinney, et al.
Medrxiv : the Preprint Server for Health Sciences|December 3, 2025
Ejection fraction quantification from ungated chest CT by AIJianhang Zhou, Jacek Kwieciński, Aakash Shanbhag, et al.
Circulation. Cardiovascular Imaging|October 15, 2024
Patient-Specific Myocardial Infarction Risk Thresholds From AI-Enabled Coronary Plaque AnalysisRobert J H Miller, Nipun Manral, Andrew Lin, et al.
Pageof 3

Showing results (21-30 of 27) with videos related to

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Pageof 3
You have reached the last page of results.This site can display upto 27 results.
Journal of Medical Imaging (Bellingham, Wash.)|September 12, 2022
Rapid quantification of COVID-19 pneumonia burden from computed tomography with convolutional long short-term memory networksAditya Killekar, Kajetan Grodecki, Andrew Lin, et al.
Medrxiv : the Preprint Server for Health Sciences|August 12, 2024
AI-based volumetric six-tissue body composition quantification from CT cardiac attenuation scans enhances mortality prediction: multicenter studyJirong Yi, Anna M Michalowska, Aakash Shanbhag, et al.
The Lancet. Digital Health|May 17, 2025
AI-based volumetric six-tissue body composition quantification from CT cardiac attenuation scans for mortality prediction: a multicentre studyJirong Yi, Anna M Marcinkiewicz, Aakash Shanbhag, et al.
The British Journal of Radiology|June 13, 2023
Artificial intelligence-assisted quantification of COVID-19 pneumonia burden from computed tomography improves prediction of adverse outcomes over visual scoring systemsKajetan Grodecki, Aditya Killekar, Judit Simon, et al.
The Lancet. Digital Health|March 26, 2022
Deep learning-enabled coronary CT angiography for plaque and stenosis quantification and cardiac risk prediction: an international multicentre studyAndrew Lin, Nipun Manral, Priscilla McElhinney, et al.
Medrxiv : the Preprint Server for Health Sciences|December 3, 2025
Ejection fraction quantification from ungated chest CT by AIJianhang Zhou, Jacek Kwieciński, Aakash Shanbhag, et al.
Circulation. Cardiovascular Imaging|October 15, 2024
Patient-Specific Myocardial Infarction Risk Thresholds From AI-Enabled Coronary Plaque AnalysisRobert J H Miller, Nipun Manral, Andrew Lin, et al.
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