Performance of a machine-learning algorithm for fully automatic LGE scar quantification in the large multi-national derivate registry
| العنوان: | Performance of a machine-learning algorithm for fully automatic LGE scar quantification in the large multi-national derivate registry |
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| المؤلفون: | Pier Giorgio Masci, Jürg Schwitter, Andrea Igoren Guaricci, Anna Giulia Pavon, Gerard Crelier, Thomas Joyce, F Ghanbari, Sebastian Kozerke, G Pantone |
| المصدر: | European Heart Journal - Cardiovascular Imaging. 22 |
| بيانات النشر: | Oxford University Press (OUP), 2021. |
| سنة النشر: | 2021 |
| مصطلحات موضوعية: | Short axis, Multi national, business.industry, Fully automatic, Medicine, Radiology, Nuclear Medicine and imaging, General Medicine, Data mining, Cardiology and Cardiovascular Medicine, business, computer.software_genre, computer, Post myocardial infarction |
| الوصف: | Funding Acknowledgements Type of funding sources: Other. Main funding source(s): J. Schwitter receives research support by “ Bayer Schweiz AG “. C.N.C. received grant by Siemens. Gianluca Pontone received institutional fees by General Electric, Bracco, Heartflow, Medtronic, and Bayer. U.J.S received grand by Astellas, Bayer, General Electric. This work was supported by Italian Ministry of Health, Rome, Italy (RC 2017 R659/17-CCM698). This work was supported by Gyrotools, Zurich, Switzerland. Background Late Gadolinium enhancement (LGE) scar quantification is generally recognized as an accurate and reproducible technique, but it is observer-dependent and time consuming. Machine learning (ML) potentially offers to solve this problem. Purpose to develop and validate a ML-algorithm to allow for scar quantification thereby fully avoiding observer variability, and to apply this algorithm to the prospective international multicentre Derivate cohort. Method The Derivate Registry collected heart failure patients with LV ejection fraction 2SD to Results In the validation and test data sets, both not used for training, the dense scar GT was 20.8 ± 9.6% and 21.9 ± 13.3% of LV mass, respectively. The TTA-network yielded the best results with small biases vs GT (-2.2 ± 6.1%, p Conclusions In the large Derivate cohort from 20 centres, performance of the presented ML-algorithm to quantify dense and non-dense scar fully automatically is comparable to that of experienced humans with small bias and acceptable 95%-CI. Such a tool could facilitate scar quantification in clinical routine as it eliminates human observer variability and can handle large data sets. |
| تدمد: | 2047-2412 2047-2404 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_________::e0eb1a6df48ed79c708a71cb824a2c1f https://doi.org/10.1093/ehjci/jeab090.023 |
| حقوق: | OPEN |
| رقم الأكسشن: | edsair.doi...........e0eb1a6df48ed79c708a71cb824a2c1f |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 20472412 20472404 |
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