Abstract
Quantitative evaluation of right ventricular (RV) volumes is of paramount importance in many cardiovascular conditions and is best performed by cardiovascular magnetic resonance imaging (CMR). However, CMR scanners are scarce, costly, and lack portability. Two-dimensional transthoracic echocardiography (2DE) allows for widely available, low cost and bedside evaluation of RV size and function. 2DE-based quantitative RV analysis is nevertheless restricted by the lack of accurate models of the complex RV shape. In this paper, we propose to calculate the RV end-diastolic (ED) and end-systolic (ES) volume by using an attention-based deep learning (DL) model on tabular data. Morphological measurements (areas) from eight standardized 2DE views are used as input to the regression model along with age, cardiac phase and gender information. The proposed architecture comprises a feature tokenizer module to transform all features (categorical and numerical) to embeddings, before applying a stack of Transformer layers. Our pipeline is trained and tested on 50 ED and 50 ES RV volumes (100 in total). The predicted volumes are compared to reference CMR values. Our method achieved impressive performance (R\(^2\)=0.975) on this relatively small-scale dataset, while it outperformed other state-of-the-art methods. The RV function evaluation using tabular Transformers shows promise. This work questions the superiority of tree-based ensemble models over DL-based solutions for tabular data in the context of functional imaging of the heart. Our pipeline is also appealing as it may allow building multi-modal cardiovascular frameworks, where only one part of the data is tabular, and other parts include images and text data.
T. A. Bohoran and P. N. Kampaktsis—Authors contributed equally.
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Data Availability Statement
The link to the GitHub repository containing the code of the image analysis pipeline is: https://github.com/tuanaqeelbohoran/RV-Volume-Prediction.git.
References
Haddad, F., Hunt, S., Rosenthal, D., Murphy, D.: Right ventricular function in cardiovascular disease, part I: anatomy, physiology, aging, and functional assessment of the right ventricle. Circulation 117, 1436–1448 (2008)
Grothues, F., Moon, J., Bellenger, N., Smith, G., Klein, H., Pennell, D.: Interstudy reproducibility of right ventricular volumes, function, and mass with cardiovascular magnetic resonance. Am. Heart J. 147, 218–223 (2004)
Keenan, N., et al.: Regional variation in cardiovascular magnetic resonance service delivery across the UK. Heart 107, 1974–1979 (2021)
Treibel, T., et al.: United Kingdom standards for non-invasive cardiac imaging: recommendations from the Imaging Council of the British Cardiovascular Society. Heart 108, e7–e7 (2022)
Marsh, H.: English hospitals in urgent need of more scanners and staff to deal with backlog. https://www.theguardian.com/society/2020/jul/14/english-nhs-hospitals-in-urgent-need-of-more-scanners-and-staff-to-deal-with-backlog,0. Accessed 10 Feb 2023
Tilling, L., González Gómez, A., Gros Otero, J., Becher, H.: Performing a comprehensive echocardiogram study: audit of adherence to the British Society of echocardiography minimum dataset guidelines. Br. J. Cardiol. 15, 151–154 (2008)
Jenkins, C., Chan, J., Bricknell, K., Strudwick, M., Marwick, T.: Reproducibility of right ventricular volumes and ejection fraction using real-time three-dimensional echocardiography: comparison with cardiac MRI. Chest 131, 1844–1851 (2007)
Fernández-GolfÃn, C., Zamorano, J.: Three-dimensional echocardiography and right ventricular function: the beauty and the beast? Circ. Cardiovasc. Imaging 10, e006099 (2017)
Gorishniy, Y., Rubachev, I., Khrulkov, V., Babenko, A.: Revisiting deep learning models for tabular data. NeurIPS 34, 18932–18943 (2021)
Chen, T., Guestrin, C.: XGBoost. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2016)
Prokhorenkova, L., Gusev, G., Vorobev, A., Dorogush, A., Gulin, A.: CatBoost: unbiased boosting with categorical features. NeurIPS 31 (2018)
Huang, X., Khetan, A., Cvitkovic, M., Karnin, Z.: TabTransformer: Tabular Data Modeling Using Contextual Embeddings (2020)
Kochav, J., et al.: Novel echocardiographic algorithm for right ventricular mass quantification: cardiovascular magnetic resonance and clinical prognosis validation. J. Am. Soc. Echocardiogr. 34, 839-850.e1 (2021)
Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)
Wang, Q., et al.: Learning deep transformer models for machine translation. arXiv preprint arXiv:1906.01787 (2019)
Funding
Tuan Aqeel Bohoran is funded by the European Union’s Horizon 2020 reasearch and innovation programme under the Marie Sklodowska-Curie grant agreement No 801604.
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Bohoran, T.A. et al. (2023). Right Ventricular Volume Prediction by Feature Tokenizer Transformer-Based Regression of 2D Echocardiography Small-Scale Tabular Data. In: Bernard, O., Clarysse, P., Duchateau, N., Ohayon, J., Viallon, M. (eds) Functional Imaging and Modeling of the Heart. FIMH 2023. Lecture Notes in Computer Science, vol 13958. Springer, Cham. https://doi.org/10.1007/978-3-031-35302-4_30
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DOI: https://doi.org/10.1007/978-3-031-35302-4_30
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