Republic of Korea
United States of America
Our solution demonstrates remarkable concordance,
not just in cardiac CT scans but also in low-dose CT (LDCT) scans.
cardiac CT imaging study
Risk Classification Consistency
Drug Treatment Target Screening
Vonder M, Zheng S, Dorrius MD, van der Aalst CM, de Koning HJ, Yi J, Yu D, Gratama JWC, Kuijpers D, Oudkerk M. Deep Learning for Automatic Calcium Scoring in Population-Based Cardiovascular Screening. JACC Cardiovasc Imaging. 2022 Feb;15(2):366-367. doi: 10.1016/j.jcmg.2021.07.012. Epub 2021 Aug 18.
Aldana-Bitar J, Cho GW, Anderson L, Karlsberg DW, Manubolu VS, Verghese D, Hussein L, Budoff MJ, Karlsberg RP. Artificial intelligence using a deep learning versus expert computed tomography human reading in calcium score and coronary artery calcium data and reporting system classification. Coron Artery Dis. 2023 May 1. doi: 10.1097/MCA.0000000000001244. Epub ahead of print.
LowDose CT Imaging Study
Suh YJ, Kim C, Lee JG, Oh H, Kang H, Kim YH, Yang DH. Fully automatic coronary calcium scoring in non-ECG-gated low-dose chest CT: comparison with ECG-gated cardiac CT. Eur Radiol. 2023 eb;33(2):1254-1265. doi: 10.1007/s00330-022-09117-3. Epub 2022 Sep 13.
Refer to the following research papers
These contents represent summaries of scientific
publications and are unrelated to any form of advertising
The objective of this study is to validate an artificial intelligence (AI)–based fully automatic coronary artery calcium (CAC) scoring system on non-electrocardiogram (ECG)–gated low-dose chest computed tomography (LDCT) using multi-institutional datasets with manual CAC scoring as the reference standard.
The Coreline Soft Aview CAC, an AI-based automatic CAC scoring software to LDCT shows good to excellent reliability in CAC score and CAC severity categorization in multi-institutional datasets.
Young Joo Suh, Cherry Kim, June-Goo Lee, Hongmin Oh, Heejun Kang, Young-Hak Kim & Dong Hyun Yang. "Fully automatic coronary calcium scoring in non-ECG-gated low-dose chest CT: comparison with ECG-gated cardiac CT" European Radiology volume 33, pages1254–1265 (2023)
The objective of the current study was to evaluate the performance of deep learning–based software (CAC, Corelinesoft) for automatic coronary calcium scoring in a screening setting.
The deep learning–based software for automatic CAC scoring performed excellently in a population-based screening setting to determine risk categorization in asymptomatic participants.
Future deep learning software that is able to assign a limited number of uncertain cases for manual human feedback could improve the calcium scoring process and outperform (a panel of) experienced readers that solely use manual scoring.
Marleen Vonder PhD, Sunyi Zheng PhD, Monique D. Dorrius MD, PhD, Carlijn M. van der Aalst PhD, Harry J. de Koning MD, PhD, Jaeyoun Yi PhD, Donghoon Yu MSc, Jan Willem C. Gratama MD, PhD, Dirkjan Kuijpers MD, PhD, Matthijs Oudkerk MD, PhD "Deep Learning for Automatic Calcium Scoring in Population-Based Cardiovascular Screening" JACC: Cardiovascular Imaging Volume 15, Issue 2, February 2022, Pages 366-367