{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T15:59:47Z","timestamp":1791907187526,"version":"4.3.4"},"reference-count":30,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2024,12,21]],"date-time":"2024-12-21T00:00:00Z","timestamp":1734739200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Background: To validate the automated quantification of cardiac chamber volumes and myocardial mass on non-contrast chest CT using cardiac MR (CMR) as a reference. Methods: We retrospectively included 53 consecutive patients who received non-contrast chest CT and CMR within three weeks. A deep learning model created cardiac segmentations on axial soft-tissue reconstructions from CT, covering all four cardiac chambers and the left ventricular myocardium. Segmentations on CMR cine short-axis and long-axis images served as a reference. Standard estimates of diagnostic accuracy were calculated for ventricular volumes at end-diastole and end-systole (LVEDV, LVESV, RVEDV, RVESV), left ventricular mass (LVM), and atrial volumes (LA, RA) at ventricular end-diastole. A qualitative assessment noted segmentation issues. Results: The deep learning model generated CT measurements for 52 of the 53 patients (98%). Based on CMR measurements, the average LVEDV was 166 \u00b1 64 mL, RVEDV was 144 \u00b1 51 mL, and LVM was 115 \u00b1 39 g. The CT measurements correlated well with CMR measurements for LVEDV, LVESV, and LVM (ICC = 0.85, ICC = 0.84, and ICC = 0.91; all p &lt; 0.001) and RVEDV and RVESV (ICC = 0.79 and ICC= 0.78; both p &lt; 0.001), and moderately well with LA and RA (ICC = 0.74 and ICC = 0.61; both p &lt; 0.001). Absolute agreements likewise favored LVEDV, LVM, and RVEDV. ECG-gating did not relevantly influence the results. The CT results correctly identified 7\/15 LV and 1\/1 RV as dilated (one and six false positives, respectively). Major qualitative issues were found in three cases (6%). Conclusions: Automated cardiac chamber volume and myocardial mass quantification on non-contrast chest CT produced viable measurements in this retrospective sample. Relevance Statement: An automated cardiac assessment on non-contrast chest CT provides quantitative morphological data on the heart, enabling a preliminary organ evaluation that aids in incidentally identifying at-risk patients who may benefit from a more targeted diagnostic workup.<\/jats:p>","DOI":"10.3390\/diagnostics14242884","type":"journal-article","created":{"date-parts":[[2024,12,23]],"date-time":"2024-12-23T05:58:27Z","timestamp":1734933507000},"page":"2884","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Fully Automated Assessment of Cardiac Chamber Volumes and Myocardial Mass on Non-Contrast Chest CT with a Deep Learning Model: Validation Against Cardiac MR"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1138-2978","authenticated-orcid":false,"given":"Ramona","family":"Schmitt","sequence":"first","affiliation":[{"name":"Department of Cardiology and Angiology, University Heart Center Freiburg\u2014Bad Krozingen, Medical Center\u2014University of Freiburg, Faculty of Medicine, University of Freiburg, S\u00fcdring 15, 79189 Bad Krozingen, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1576-1481","authenticated-orcid":false,"given":"Christopher L.","family":"Schlett","sequence":"additional","affiliation":[{"name":"Department of Diagnostic and Interventional Radiology, Medical Center\u2014University of Freiburg, Faculty of Medicine, University of Freiburg, Hugstetter Str. 55, 79106 Freiburg im Breisgau, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4528-1507","authenticated-orcid":false,"given":"Jonathan I.","family":"Sperl","sequence":"additional","affiliation":[{"name":"Siemens Healthineers, Siemensstr. 1, 91301 Forchheim, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Saikiran","family":"Rapaka","sequence":"additional","affiliation":[{"name":"Siemens Healthineers, 755 College Rd E, Princeton, NJ 08540, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5948-3655","authenticated-orcid":false,"given":"Athira J.","family":"Jacob","sequence":"additional","affiliation":[{"name":"Siemens Healthineers, 755 College Rd E, Princeton, NJ 08540, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0430-7184","authenticated-orcid":false,"given":"Manuel","family":"Hein","sequence":"additional","affiliation":[{"name":"Department of Cardiology and Angiology, University Heart Center Freiburg\u2014Bad Krozingen, Medical Center\u2014University of Freiburg, Faculty of Medicine, University of Freiburg, S\u00fcdring 15, 79189 Bad Krozingen, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9826-2372","authenticated-orcid":false,"given":"Muhammad Taha","family":"Hagar","sequence":"additional","affiliation":[{"name":"Department of Diagnostic and Interventional Radiology, Medical Center\u2014University of Freiburg, Faculty of Medicine, University of Freiburg, Hugstetter Str. 55, 79106 Freiburg im Breisgau, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Philipp","family":"Ruile","sequence":"additional","affiliation":[{"name":"Department of Cardiology and Angiology, University Heart Center Freiburg\u2014Bad Krozingen, Medical Center\u2014University of Freiburg, Faculty of Medicine, University of Freiburg, S\u00fcdring 15, 79189 Bad Krozingen, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dirk","family":"Westermann","sequence":"additional","affiliation":[{"name":"Department of Cardiology and Angiology, University Heart Center Freiburg\u2014Bad Krozingen, Medical Center\u2014University of Freiburg, Faculty of Medicine, University of Freiburg, S\u00fcdring 15, 79189 Bad Krozingen, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6314-1829","authenticated-orcid":false,"given":"Martin","family":"Soschynski","sequence":"additional","affiliation":[{"name":"Department of Diagnostic and Interventional Radiology, Medical Center\u2014University of Freiburg, Faculty of Medicine, University of Freiburg, Hugstetter Str. 55, 79106 Freiburg im Breisgau, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fabian","family":"Bamberg","sequence":"additional","affiliation":[{"name":"Department of Diagnostic and Interventional Radiology, Medical Center\u2014University of Freiburg, Faculty of Medicine, University of Freiburg, Hugstetter Str. 55, 79106 Freiburg im Breisgau, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9675-803X","authenticated-orcid":false,"given":"Christopher","family":"Schuppert","sequence":"additional","affiliation":[{"name":"Department of Diagnostic and Interventional Radiology, Medical Center\u2014University of Freiburg, Faculty of Medicine, University of Freiburg, Hugstetter Str. 55, 79106 Freiburg im Breisgau, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,12,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"e139","DOI":"10.1161\/CIR.0000000000000757","article-title":"Heart Disease and Stroke Statistics-2020 Update: A Report from the American Heart Association","volume":"141","author":"Virani","year":"2020","journal-title":"Circulation"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1789","DOI":"10.1016\/S0140-6736(18)32279-7","article-title":"Global, regional, and national incidence, prevalence, and years lived with disability for 354 diseases and injuries for 195 countries and territories, 1990\u20132017: A systematic analysis for the Global Burden of Disease Study 2017","volume":"392","author":"Disease","year":"2018","journal-title":"Lancet"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"572","DOI":"10.1016\/S0140-6736(17)32520-5","article-title":"Temporal trends and patterns in heart failure incidence: A population-based study of 4 million individuals","volume":"391","author":"Conrad","year":"2018","journal-title":"Lancet"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"242","DOI":"10.1002\/ejhf.483","article-title":"Epidemiology of heart failure: The prevalence of heart failure and ventricular dysfunction in older adults over time. A systematic review","volume":"18","author":"Hoes","year":"2016","journal-title":"Eur. J. Heart Fail."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1614","DOI":"10.1016\/j.ehj.2004.06.038","article-title":"Quantifying the heart failure epidemic: Prevalence, incidence rate, lifetime risk and prognosis of heart failure The Rotterdam Study","volume":"25","author":"Bleumink","year":"2004","journal-title":"Eur. Heart J."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1016\/j.jcct.2021.12.005","article-title":"Deep learning model to quantify left atrium volume on routine non-contrast chest CT and predict adverse outcomes","volume":"16","author":"Aquino","year":"2022","journal-title":"J. Cardiovasc. Comput. Tomogr."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"3798","DOI":"10.1088\/1361-6560\/aa63cb","article-title":"Automatic segmentation and quantification of the cardiac structures from non-contrast-enhanced cardiac CT scans","volume":"62","author":"Shahzad","year":"2017","journal-title":"Phys. Med. Biol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1699","DOI":"10.1007\/s11548-021-02468-0","article-title":"Spectral augmentation for heart chambers segmentation on conventional contrasted and unenhanced CT scans: An in-depth study","volume":"16","author":"Lartaud","year":"2021","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"336","DOI":"10.1016\/j.jcct.2023.08.001","article-title":"AI-based, automated chamber volumetry from gated, non-contrast CT","volume":"17","author":"Jacob","year":"2023","journal-title":"J. Cardiovasc. Comput. Tomogr."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1321","DOI":"10.1093\/ehjci\/jez232","article-title":"European Association of Cardiovascular Imaging expert consensus paper: A comprehensive review of cardiovascular magnetic resonance normal values of cardiac chamber size and aortic root in adults and recommendations for grading severity","volume":"20","author":"Petersen","year":"2019","journal-title":"Eur. Heart J. Cardiovasc. Imaging"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1098","DOI":"10.1056\/NEJM198710223171717","article-title":"Simplified calculation of body-surface area","volume":"317","author":"Mosteller","year":"1987","journal-title":"N. Engl. J. Med."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1016\/j.jcm.2016.02.012","article-title":"A Guideline of Selecting and Reporting Intraclass Correlation Coefficients for Reliability Research","volume":"15","author":"Koo","year":"2016","journal-title":"J. Chiropr. Med."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1016\/j.ejrad.2009.01.035","article-title":"Feasibility of automatic assessment of four-chamber cardiac function with MDCT: Initial clinical application and validation","volume":"74","author":"Abadi","year":"2010","journal-title":"Eur. J. Radiol."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"5222","DOI":"10.1007\/s00330-022-08663-0","article-title":"Diagnostic accuracy of automated 3D volumetry of cardiac chambers by CT pulmonary angiography for identification of pulmonary hypertension due to left heart disease","volume":"32","author":"Melzig","year":"2022","journal-title":"Eur. Radiol."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Bruns, S., Wolterink, J.M., van den Boogert, T.P.W., Runge, J.H., Bouma, B.J., Henriques, J.P., Baan, J., Viergever, M.A., Planken, R.N., and Isgum, I. (2022). Deep learning-based whole-heart segmentation in 4D contrast-enhanced cardiac CT. Comput. Biol. Med., 142.","DOI":"10.1016\/j.compbiomed.2021.105191"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Sharkey, M.J., Taylor, J.C., Alabed, S., Dwivedi, K., Karunasaagarar, K., Johns, C.S., Rajaram, S., Garg, P., Alkhanfar, D., and Metherall, P. (2022). Fully automatic cardiac four chamber and great vessel segmentation on CT pulmonary angiography using deep learning. Front. Cardiovasc. Med., 9.","DOI":"10.3389\/fcvm.2022.983859"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1080\/0284186X.2021.1967445","article-title":"Atlas-based auto-segmentation for delineating the heart and cardiac substructures in breast cancer radiation therapy","volume":"61","author":"Milo","year":"2022","journal-title":"Acta Oncol."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Finnegan, R., Dowling, J., Koh, E.S., Tang, S., Otton, J., Delaney, G., Batumalai, V., Luo, C., Atluri, P., and Satchithanandha, A. (2019). Feasibility of multi-atlas cardiac segmentation from thoracic planning CT in a probabilistic framework. Phys. Med. Biol., 64.","DOI":"10.1088\/1361-6560\/ab0ea6"},{"key":"ref_19","first-page":"659","article-title":"Heart Chamber Segmentation from CT Using Convolutional Neural Networks","volume":"10578","author":"Dormer","year":"2018","journal-title":"Proc. SPIE Int. Soc. Opt. Eng."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"110061","DOI":"10.1016\/j.radonc.2023.110061","article-title":"Deep learning-based automatic segmentation of cardiac substructures for lung cancers","volume":"191","author":"Chen","year":"2024","journal-title":"Radiother. Oncol."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"101249","DOI":"10.1016\/j.jacadv.2024.101249","article-title":"AI-Derived Left Ventricular Mass From Noncontrast Cardiac CT: Correlation With Contrast CT Angiography and CMR","volume":"3","author":"Han","year":"2024","journal-title":"JACC Adv."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"e220133","DOI":"10.1148\/ryct.220133","article-title":"Comparison of Biplane Area-Length Method and 3D Volume Quantification by Using Cardiac MRI for Assessment of Left Atrial Volume in Atrial Fibrillation","volume":"5","author":"Maroun","year":"2023","journal-title":"Radiol. Cardiothorac. Imaging"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Kuusisto, J.K., Poyhonen, P.A.K., Pirinen, J., Lehmonen, L.J., Raty, H.P., Martinez-Majander, N., Putaala, J., Sinisalo, J., and Jarvinen, V. (2021). Revisiting left atrial volumetry by magnetic resonance imaging: The role of atrial shape and 3D angle between left ventricular and left atrial axis. BMC Med. Imaging, 21.","DOI":"10.1186\/s12880-021-00701-5"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"867","DOI":"10.1093\/ehjci\/jeac011","article-title":"Imaging assessment of the right atrium: Anatomy and function","volume":"23","author":"Lang","year":"2022","journal-title":"Eur. Heart J. Cardiovasc. Imaging"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"309","DOI":"10.1038\/s41746-024-01308-0","article-title":"Artificial intelligence applied to coronary artery calcium scans (AI-CAC) significantly improves cardiovascular events prediction","volume":"7","author":"Naghavi","year":"2024","journal-title":"NPJ Digit. Med."},{"key":"ref_26","first-page":"101300","article-title":"AI-Enabled CT Cardiac Chamber Volumetry Predicts Atrial Fibrillation and Stroke Comparable to MRI","volume":"3","author":"Naghavi","year":"2024","journal-title":"JACC: Advances"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"678","DOI":"10.1016\/j.jchf.2018.03.006","article-title":"Temporal Trends in the Incidence of and Mortality Associated With Heart Failure With Preserved and Reduced Ejection Fraction","volume":"6","author":"Tsao","year":"2018","journal-title":"JACC Heart Fail."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"2747","DOI":"10.1038\/s41467-024-46977-3","article-title":"Predicting mortality from AI cardiac volumes mass and coronary calcium on chest computed tomography","volume":"15","author":"Miller","year":"2024","journal-title":"Nat. Commun."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1756","DOI":"10.1093\/eurheartj\/ehy453","article-title":"Cardio-Oncology Services: Rationale, organization, and implementation","volume":"40","author":"Lancellotti","year":"2019","journal-title":"Eur. Heart J."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"4229","DOI":"10.1093\/eurheartj\/ehac244","article-title":"2022 ESC Guidelines on cardio-oncology developed in collaboration with the European Hematology Association (EHA), the European Society for Therapeutic Radiology and Oncology (ESTRO) and the International Cardio-Oncology Society (IC-OS)","volume":"43","author":"Lyon","year":"2022","journal-title":"Eur. Heart J."}],"container-title":["Diagnostics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-4418\/14\/24\/2884\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:57:45Z","timestamp":1760115465000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-4418\/14\/24\/2884"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,21]]},"references-count":30,"journal-issue":{"issue":"24","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["diagnostics14242884"],"URL":"https:\/\/doi.org\/10.3390\/diagnostics14242884","relation":{},"ISSN":["2075-4418"],"issn-type":[{"value":"2075-4418","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,21]]}}}