Automated Classification of Left Ventricular Hypertrophy on Cardiac MRI
Left ventricular hypertrophy is an independent predictor of coronary artery disease, stroke, and heart failure. Our aim was to detect LVH cardiac magnetic resonance (CMR) scans with automatic methods. We developed an ensemble model based on a three-dimensional version of ResNet. The input of the net...
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| Format: | Article |
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MDPI AG
2022-04-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/9/4151 |
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| author | Adam Budai Ferenc Imre Suhai Kristof Csorba Zsofia Dohy Liliana Szabo Bela Merkely Hajnalka Vago |
| author_facet | Adam Budai Ferenc Imre Suhai Kristof Csorba Zsofia Dohy Liliana Szabo Bela Merkely Hajnalka Vago |
| author_sort | Adam Budai |
| collection | DOAJ |
| description | Left ventricular hypertrophy is an independent predictor of coronary artery disease, stroke, and heart failure. Our aim was to detect LVH cardiac magnetic resonance (CMR) scans with automatic methods. We developed an ensemble model based on a three-dimensional version of ResNet. The input of the network included short-axis and long-axis images. We also introduced a standardization methodology to unify the input images for noise reduction. The output of the network is the decision whether the patient has hypertrophy or not. We included 428 patients (mean age: 49 ± 18 years, 262 males) with LVH (346 hypertrophic cardiomyopathy, 45 cardiac amyloidosis, 11 Anderson–Fabry disease, 16 endomyocardial fibrosis, 10 aortic stenosis). Our control group consisted of 234 healthy subjects (mean age: 35 ± 15 years; 126 males) without any known cardiovascular diseases. The developed machine-learning-based model achieved a 92% F1-score and 97% recall on the hold-out dataset, which is comparable to the medical experts. Experiments showed that the standardization method was able to significantly boost the performance of the algorithm. The algorithm could improve the diagnostic accuracy, and it could open a new door to AI applications in CMR. |
| first_indexed | 2024-03-10T04:22:44Z |
| format | Article |
| id | doaj.art-4a2f93e2bd4e4e48b8e6947b3f9ffb12 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T04:22:44Z |
| publishDate | 2022-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-4a2f93e2bd4e4e48b8e6947b3f9ffb122023-11-23T07:44:57ZengMDPI AGApplied Sciences2076-34172022-04-01129415110.3390/app12094151Automated Classification of Left Ventricular Hypertrophy on Cardiac MRIAdam Budai0Ferenc Imre Suhai1Kristof Csorba2Zsofia Dohy3Liliana Szabo4Bela Merkely5Hajnalka Vago6Department of Automation and Applied Informatics, Faculty of Electrical Engineering and Informatics, Budapest University of Technology and Economics, H-1111 Budapest, HungaryHeart and Vascular Center, Semmelweis University, H-1122 Budapest, HungaryDepartment of Automation and Applied Informatics, Faculty of Electrical Engineering and Informatics, Budapest University of Technology and Economics, H-1111 Budapest, HungaryHeart and Vascular Center, Semmelweis University, H-1122 Budapest, HungaryHeart and Vascular Center, Semmelweis University, H-1122 Budapest, HungaryHeart and Vascular Center, Semmelweis University, H-1122 Budapest, HungaryHeart and Vascular Center, Semmelweis University, H-1122 Budapest, HungaryLeft ventricular hypertrophy is an independent predictor of coronary artery disease, stroke, and heart failure. Our aim was to detect LVH cardiac magnetic resonance (CMR) scans with automatic methods. We developed an ensemble model based on a three-dimensional version of ResNet. The input of the network included short-axis and long-axis images. We also introduced a standardization methodology to unify the input images for noise reduction. The output of the network is the decision whether the patient has hypertrophy or not. We included 428 patients (mean age: 49 ± 18 years, 262 males) with LVH (346 hypertrophic cardiomyopathy, 45 cardiac amyloidosis, 11 Anderson–Fabry disease, 16 endomyocardial fibrosis, 10 aortic stenosis). Our control group consisted of 234 healthy subjects (mean age: 35 ± 15 years; 126 males) without any known cardiovascular diseases. The developed machine-learning-based model achieved a 92% F1-score and 97% recall on the hold-out dataset, which is comparable to the medical experts. Experiments showed that the standardization method was able to significantly boost the performance of the algorithm. The algorithm could improve the diagnostic accuracy, and it could open a new door to AI applications in CMR.https://www.mdpi.com/2076-3417/12/9/4151classificationleft ventricular hypertrophyCMRmachine learning |
| spellingShingle | Adam Budai Ferenc Imre Suhai Kristof Csorba Zsofia Dohy Liliana Szabo Bela Merkely Hajnalka Vago Automated Classification of Left Ventricular Hypertrophy on Cardiac MRI Applied Sciences classification left ventricular hypertrophy CMR machine learning |
| title | Automated Classification of Left Ventricular Hypertrophy on Cardiac MRI |
| title_full | Automated Classification of Left Ventricular Hypertrophy on Cardiac MRI |
| title_fullStr | Automated Classification of Left Ventricular Hypertrophy on Cardiac MRI |
| title_full_unstemmed | Automated Classification of Left Ventricular Hypertrophy on Cardiac MRI |
| title_short | Automated Classification of Left Ventricular Hypertrophy on Cardiac MRI |
| title_sort | automated classification of left ventricular hypertrophy on cardiac mri |
| topic | classification left ventricular hypertrophy CMR machine learning |
| url | https://www.mdpi.com/2076-3417/12/9/4151 |
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