Eulerian Motion Magnification as an Imaging Biomarker for Acute Rheumatic Fever in Pediatric Echocardiography
Resumo
In acute rheumatic fever (ARF), valvular involvement is inflammatory and precedes the structural remodeling of chronic disease; as a result, descriptors built on leaflet geometry or kinematics often fail to separate ARF from controls in this early window. We ask whether the global cardiac motion of the exam, amplified by Eulerian Video Magnification (EVM), carries a signature of ARF in two-dimensional pediatric echocardiography. For each study, a temporal band-pass filter isolates the heartbeat-frequency component of intensity variation, and the energy of that motion is summarized into compact descriptors. Across 73 examinations (17 ARF, 56 controls), motion-energy descriptors were higher in ARF (p < 0.01; Cohen’s d up to 0.80) and, under leave-oneout cross-validation, separated the groups with an area under the ROC curve of 0.69 (95% CI 0.53–0.83; permutation p = 0.022). Direct leaflet kinematic descriptors did not separate the groups (AUC 0.42) under the same protocol. These preliminary results place amplified global motion as an objective, low-cost candidate marker for ARF screening in resource-limited settings, and they motivate validation on larger, multi-center cohorts.
Referências
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Kim, S., Jin, P., Song, S., Chen, C., Li, Y., Ren, H., Li, X., Liu, T., and Li, Q. (2025). EchoFM: Foundation model for generalizable echocardiogram analysis. IEEE Transactions on Medical Imaging.
Leitman, M., Lysyansky, P., Sidenko, S., et al. (2004). Two-dimensional strain—a novel software for real-time quantitative echocardiographic assessment of myocardial function. Journal of the American Society of Echocardiography, 17(10):1021–1029.
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Nunes, M. C. P., Sable, C., Nascimento, B. R., et al. (2016). Echocardiographic prevalence of rheumatic heart disease in brazilian schoolchildren: data from the provar study. International Journal of Cardiology, 219:439–445.
Oh, T.-H., Jaroensri, R., Kim, C., Elgharib, M., Durand, F., Freeman, W. T., and Matusik, W. (2018). Learning-based video motion magnification. In Proceedings of the European Conference on Computer Vision (ECCV), pages 633–648.
Oppenheim, A. V. and Schafer, R. W. (1999). Discrete-Time Signal Processing. Prentice Hall, 2 edition.
Ouyang, D., He, B., Ghorbani, A., et al. (2020). Video-based ai for beat-to-beat assessment of cardiac function. Nature, 580(7802):252–256.
Passos, L. S. A., Nunes, M. C. P., and Aikawa, E. (2021). Rheumatic heart valve disease pathophysiology and underlying mechanisms. Frontiers in Cardiovascular Medicine, 7:612716.
Ploutz, M., Lu, J. C., Scheel, J., et al. (2016). Handheld echocardiographic screening for rheumatic heart disease by non-experts. Heart, 102(1):35–39.
Reményi, B., Wilson, N., Steer, A., et al. (2012). World heart federation criteria for echocardiographic diagnosis of rheumatic heart disease—an evidence-based guideline. Nature Reviews Cardiology, 9(5):297–309.
Voigt, J.-U., Pedrizzetti, G., Lysyansky, P., et al. (2015). Definitions for a common standard for 2d speckle tracking echocardiography: consensus document of the eacvi/ase/industry task force. European Heart Journal – Cardiovascular Imaging, 16(1):1–11.
Wadhwa, N., Rubinstein, M., Durand, F., and Freeman, W. T. (2013). Phase-based video motion processing. ACM Transactions on Graphics (TOG), 32(4):1–10.
Wadhwa, N., Rubinstein, M., Durand, F., and Freeman, W. T. (2014). Riesz pyramids for fast phase-based video magnification. In IEEE International Conference on Computational Photography (ICCP), pages 1–10. IEEE.
Wang, A., Doan, T. T., Reddy, C., and Jone, P.-N. (2025). Artificial intelligence in fetal and pediatric echocardiography. Children, 12(1):14.
Watkins, D. A., Johnson, C. O., Colquhoun, S. M., et al. (2017). Global, regional, and national burden of rheumatic heart disease, 1990–2015. New England Journal of Medicine, 377(8):713–722.
Williamson, J., Reményi, B., Francis, J., Morris, P., and Whalley, G. (2024). Echocardio-graphic screening for rheumatic heart disease: a brief history and implications for the future. Heart, Lung and Circulation, 33(7):943–950.
Wu, H.-Y., Rubinstein, M., Shih, E., Guttag, J., Durand, F., and Freeman, W. (2012). Eulerian video magnification for revealing subtle changes in the world. ACM Transactions on Graphics (TOG), 31(4):1–8.
Zhang, Z., Wu, Q., Ding, S., Wang, X., and Ye, J. (2025). Echo-vision-fm: a pre-training and fine-tuning framework for echocardiogram video vision foundation model. Nature Communications, 16.
Zühlke, L., Engel, M. E., Karthikeyan, G., et al. (2015). Characteristics, complications, and gaps in evidence-based interventions in rheumatic heart disease: the global rheumatic heart disease registry (the remedy study). European Heart Journal, 36(18):1115–1122.
Bhorat, R., Harmon, D. M., Liu, K., Dugan, J., Ngo, C., Friedman, P. A., Bagula, H., Ntsekhe, M., and Attia, Z. I. (2026). Artificial intelligence-enhanced electrocardiogram for the detection of rheumatic heart disease. European Heart Journal – Digital Health, 7(4):ztag056.
Brown, K., Roshanitabrizi, P., Rwebembera, J., et al. (2024). Using artificial intelligence for rheumatic heart disease detection by echocardiography: focus on mitral regurgitation. Journal of the American Heart Association, 13(2):e031257.
Burt, P. J. and Adelson, E. H. (1983). The laplacian pyramid as a compact image code. IEEE Transactions on Communications, 31(4):532–540.
Gewitz, M. H., Baltimore, R. S., Tani, L. Y., et al. (2015). Revision of the jones criteria for the diagnosis of acute rheumatic fever in the era of doppler echocardiography: a scientific statement from the american heart association. Circulation, 131(20):1806–1818.
Kim, S., Jin, P., Song, S., Chen, C., Li, Y., Ren, H., Li, X., Liu, T., and Li, Q. (2025). EchoFM: Foundation model for generalizable echocardiogram analysis. IEEE Transactions on Medical Imaging.
Leitman, M., Lysyansky, P., Sidenko, S., et al. (2004). Two-dimensional strain—a novel software for real-time quantitative echocardiographic assessment of myocardial function. Journal of the American Society of Echocardiography, 17(10):1021–1029.
Martins, J. F. B. S., Nascimento, E. R., Nascimento, B. R., et al. (2021). Towards automatic diagnosis of rheumatic heart disease on echocardiographic exams through video-based deep learning. Journal of the American Medical Informatics Association, 28(9):1834–1842.
Nunes, M. C. P., Sable, C., Nascimento, B. R., et al. (2016). Echocardiographic prevalence of rheumatic heart disease in brazilian schoolchildren: data from the provar study. International Journal of Cardiology, 219:439–445.
Oh, T.-H., Jaroensri, R., Kim, C., Elgharib, M., Durand, F., Freeman, W. T., and Matusik, W. (2018). Learning-based video motion magnification. In Proceedings of the European Conference on Computer Vision (ECCV), pages 633–648.
Oppenheim, A. V. and Schafer, R. W. (1999). Discrete-Time Signal Processing. Prentice Hall, 2 edition.
Ouyang, D., He, B., Ghorbani, A., et al. (2020). Video-based ai for beat-to-beat assessment of cardiac function. Nature, 580(7802):252–256.
Passos, L. S. A., Nunes, M. C. P., and Aikawa, E. (2021). Rheumatic heart valve disease pathophysiology and underlying mechanisms. Frontiers in Cardiovascular Medicine, 7:612716.
Ploutz, M., Lu, J. C., Scheel, J., et al. (2016). Handheld echocardiographic screening for rheumatic heart disease by non-experts. Heart, 102(1):35–39.
Reményi, B., Wilson, N., Steer, A., et al. (2012). World heart federation criteria for echocardiographic diagnosis of rheumatic heart disease—an evidence-based guideline. Nature Reviews Cardiology, 9(5):297–309.
Voigt, J.-U., Pedrizzetti, G., Lysyansky, P., et al. (2015). Definitions for a common standard for 2d speckle tracking echocardiography: consensus document of the eacvi/ase/industry task force. European Heart Journal – Cardiovascular Imaging, 16(1):1–11.
Wadhwa, N., Rubinstein, M., Durand, F., and Freeman, W. T. (2013). Phase-based video motion processing. ACM Transactions on Graphics (TOG), 32(4):1–10.
Wadhwa, N., Rubinstein, M., Durand, F., and Freeman, W. T. (2014). Riesz pyramids for fast phase-based video magnification. In IEEE International Conference on Computational Photography (ICCP), pages 1–10. IEEE.
Wang, A., Doan, T. T., Reddy, C., and Jone, P.-N. (2025). Artificial intelligence in fetal and pediatric echocardiography. Children, 12(1):14.
Watkins, D. A., Johnson, C. O., Colquhoun, S. M., et al. (2017). Global, regional, and national burden of rheumatic heart disease, 1990–2015. New England Journal of Medicine, 377(8):713–722.
Williamson, J., Reményi, B., Francis, J., Morris, P., and Whalley, G. (2024). Echocardio-graphic screening for rheumatic heart disease: a brief history and implications for the future. Heart, Lung and Circulation, 33(7):943–950.
Wu, H.-Y., Rubinstein, M., Shih, E., Guttag, J., Durand, F., and Freeman, W. (2012). Eulerian video magnification for revealing subtle changes in the world. ACM Transactions on Graphics (TOG), 31(4):1–8.
Zhang, Z., Wu, Q., Ding, S., Wang, X., and Ye, J. (2025). Echo-vision-fm: a pre-training and fine-tuning framework for echocardiogram video vision foundation model. Nature Communications, 16.
Zühlke, L., Engel, M. E., Karthikeyan, G., et al. (2015). Characteristics, complications, and gaps in evidence-based interventions in rheumatic heart disease: the global rheumatic heart disease registry (the remedy study). European Heart Journal, 36(18):1115–1122.
Publicado
23/09/2026
Como Citar
SILVA, Naila Suele Mascarenhas; RODRIGUES, Carlos Alberto; PINTO, Airandes de Sousa; OLIVEIRA, Bráulio Muzzi Ribeiro de; MENDOZA, Renata Fonseca; NUNES, Maria do Carmo Pereira.
Eulerian Motion Magnification as an Imaging Biomarker for Acute Rheumatic Fever in Pediatric Echocardiography. In: ESCOLA REGIONAL DE COMPUTAÇÃO BAHIA, ALAGOAS E SERGIPE (ERBASE), 26. , 2026, Maragogi/AL.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2026
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p. 170-179.
DOI: https://doi.org/10.5753/erbase.2026.32170.
