Horizontal Strabismus Surgical Planning Using Multi-output Regressors
Abstract
Strabismus is an ophthalmologic pathology that affects about 4% of the population, that may cause irreversible sensorial damage to vision. The treatment of more severe cases requires surgical intervention. The planning in such operations is complex and requires, besides vast knowledge on the subject, expertise from the specialist doctor. And so, the presented paper makes use of multi-output regressors and Support Vector Regression machines (SVR) to indicate the strabismus surgical planning. In our most precise method, of the three evaluated, it was obtained MAE of 0.798 millimeters and RMSE of 1.259 millimeters in the indication of the horizontal strabismus surgical planning.
References
Almeida, J. D. S. d., Silva, A. C., Teixeira, J. A. M., Paiva, A. C., and Gattass, M. (2015). Surgical planning for horizontal strabismus using support vector regression. Computers in biology and medicine, 63:178–186.
Borchani, H., Varando, G., Bielza, C., and Larrañaga, P. (2015). A survey on multi-output regression. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 5(5):216–233.
Diaz, J. P. and Dias, C. S. (2000). Strabismus. Butterworth Heinemann,Woburn, Massachusets, EUA.
Mills, M. D., Coats, D. K., Donahue, S. P., and Wheeler, D. T. (2004). Strabismus surgery for adults: a report by the american academy of ophthalmology. Ophthalmology, 111(6):1255–1262.
NOEL, L., BLOOM, J., CLARKE, W., and BAWAZEER, A. (1997). Retinal perforation in strabismus surgery. Journal of pediatric ophthalmology and strabismus, 34(2):115–117.
Noorden, G. V. and Campos, E. (2001). Binocular vision and ocular motility: theory and management of strabismus. Mosby Inc.
Queiroz, F. C. M. (2010). Análise de componentes principais na meta-análise para obtenção de equações de predição de valores energéticos de alimentos para aves. Master’s thesis, Universidade Federal de Lavras.
Read, J., Pfahringer, B., Holmes, G., and Frank, E. (2011). Classifier chains for multi-label classification. Machine learning, 85(3):333.
Schutte, S., Polling, J. R., van der Helm, F. C. T., and Simonsz, H. J. (2008). Human error in strabismus surgery: quantification with a sensitivity analysis. Graefe’s Archive for Clinical and Experimental Ophthalmology, 247(3):399.
Spyromitros-Xioufis, E., Tsoumakas, G., Groves, W., and Vlahavas, I. (2012). Multi-label classification methods for multi-target regression. arXiv preprint arXiv:1211.6581.
Vapnik, V. (1998). Statistical learning theory. 1998, volume 3. Wiley, New York.
Vapnik, V., Golowich, S. E., Smola, A., et al. (1997). Support vector method for function approximation, regression estimation, and signal processing. Advances in neural information processing systems, pages 281–287.
Witten, I. and Frank, E. (2005). Data mining: practical machine learning tools and techniques. Morgan Kaufmann, San Francisco, USA.
Ziaei, H., Katibeh, M., Mohammadi, S., Mirzaei, M., Moein, H.-R., Kheiri, B., Taghaddos, S., and Rajavi, Z. (2016). The impact of congenital strabismus surgery on quality of life in children. Journal of Ophthalmic & Vision Research, 11(2):188.
Borchani, H., Varando, G., Bielza, C., and Larrañaga, P. (2015). A survey on multi-output regression. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 5(5):216–233.
Diaz, J. P. and Dias, C. S. (2000). Strabismus. Butterworth Heinemann,Woburn, Massachusets, EUA.
Mills, M. D., Coats, D. K., Donahue, S. P., and Wheeler, D. T. (2004). Strabismus surgery for adults: a report by the american academy of ophthalmology. Ophthalmology, 111(6):1255–1262.
NOEL, L., BLOOM, J., CLARKE, W., and BAWAZEER, A. (1997). Retinal perforation in strabismus surgery. Journal of pediatric ophthalmology and strabismus, 34(2):115–117.
Noorden, G. V. and Campos, E. (2001). Binocular vision and ocular motility: theory and management of strabismus. Mosby Inc.
Queiroz, F. C. M. (2010). Análise de componentes principais na meta-análise para obtenção de equações de predição de valores energéticos de alimentos para aves. Master’s thesis, Universidade Federal de Lavras.
Read, J., Pfahringer, B., Holmes, G., and Frank, E. (2011). Classifier chains for multi-label classification. Machine learning, 85(3):333.
Schutte, S., Polling, J. R., van der Helm, F. C. T., and Simonsz, H. J. (2008). Human error in strabismus surgery: quantification with a sensitivity analysis. Graefe’s Archive for Clinical and Experimental Ophthalmology, 247(3):399.
Spyromitros-Xioufis, E., Tsoumakas, G., Groves, W., and Vlahavas, I. (2012). Multi-label classification methods for multi-target regression. arXiv preprint arXiv:1211.6581.
Vapnik, V. (1998). Statistical learning theory. 1998, volume 3. Wiley, New York.
Vapnik, V., Golowich, S. E., Smola, A., et al. (1997). Support vector method for function approximation, regression estimation, and signal processing. Advances in neural information processing systems, pages 281–287.
Witten, I. and Frank, E. (2005). Data mining: practical machine learning tools and techniques. Morgan Kaufmann, San Francisco, USA.
Ziaei, H., Katibeh, M., Mohammadi, S., Mirzaei, M., Moein, H.-R., Kheiri, B., Taghaddos, S., and Rajavi, Z. (2016). The impact of congenital strabismus surgery on quality of life in children. Journal of Ophthalmic & Vision Research, 11(2):188.
Published
2017-07-02
How to Cite
SILVA, Thalles Alencar; DE ALMEIDA, João Dallyson Sousa; TEIXEIRA, Jorge Antonio Meireles; BRAZ JUNIOR, Geraldo.
Horizontal Strabismus Surgical Planning Using Multi-output Regressors. In: BRAZILIAN SYMPOSIUM ON COMPUTING APPLIED TO HEALTH (SBCAS), 17. , 2017, São Paulo.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2017
.
p. 1987-1996.
ISSN 2763-8952.
DOI: https://doi.org/10.5753/sbcas.2017.3714.
