Abstract
The physical appearance of the nostril is important in the objective assessment of a cleft-lip patient while an objective quantitative evaluation is necessary to improve the result of the surgical procedure. The use of Kendall’s coefficient of concordance (W) to identify consistency between several raters is proposed in this paper. Linear regression method was then compared with the Neural Network method to find out which is better in determining the consistency of data. The feature factors were extracted from a digital image of the nostril taking into consideration symmetry as the basis. Statistical and Neural Network methods were utilized to process and analyze the deformity assessment data. Two groups of raters were chosen to evaluate the deformity of the cleft lip/ cleft nose based on photos shown to them. The angles and distance were measured with respect to the symmetrical aspect and the elementary reference score and factors were obtained through statistical analysis. Linear regression equations describing the relationship between the selected factors and the elementary score were formulated in order to obtain a more reliable reference data. The target data was pre-processed to achieve a more consistent and stable performance. A Neural Network was used to predict the evaluation score and it performed better than the linear regression method under certain conditions. The proposed method can give an objective evaluation to help surgeons evaluate their performance after a surgical procedure and find out if there is a need for further procedures to be done with lesser computational requirement over other existing three-dimensional algorithms.
Similar content being viewed by others
References
Tobiasen, J. M. and Hiebert, J. M., “Facial Impairment Scales for Clefts,” Plastic and Reconstructive Surgery, Vol. 93, No. 1, pp. 42–43, 1994.
Salazar, A., Daza, S. G., Sánchez, L., Prieto, F., Castellanos, G. and Quintero, C., “Feature Extraction & Lips Posture Detection Oriented to the Treatment of CLP Children,” Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Vol. 1, pp. 5747–5750, 2006.
Takeshita, A., Nakajuma, T., Kaneko, T., Yasawa, M. and Tamada, I., “Surgical Creation of a Cupid Bow Using W-Plasty in Patients After Cleft Lip Surgery,” British Journal of Plastic Surgery, Vol. 56, No. 4, pp. 375–379, 2003.
Wong, G. B., Burvin, R. and Mulliken, J. B., “Resorbable Internal Splint: An Adjunct to Primary Correction of Unilateral Cleft Lip-Nasal Deformity,” Plastic and Reconstructive Surgery, Vol. 110, No. 2, pp. 385–391, 2002.
Cobley, T. D., Orlando, A., Page, K. and Mercer, N. S., “Modification of the Koken Nasal Splint,” Cleft Palate Craniofacial Journal, Vol. 37, No. 2, pp. 125–126, 2000.
Lo, L. J., Wong, F. H., Mardini, S., Chen, Y. R. and Noordhoff, M. S., “Assessment of Bilateral Cleft Lip Nose Deformity: A Comparison of Results as Judged by Cleft Surgeons and Laypersons,” Plastic and Reconstructive Surgery, Vol. 110, No. 3, pp. 733–738, 2002.
Asher-McDade, C., Roberts, C., Shaw, W. C. and Gallager, C., “Development of a Method for Rating Nasolabial Appearance in Patients with Clefts of the Lip and Palate,” Cleft Palate-Craniofacial Journal, Vol. 28, No. 4, pp. 385–390, Discussion 390–391, 1991.
Anastassov, Y. and Chipkov, C., “Analysis of Nasal and Labial Deformities in Cleft Lip, Alveolus and Palate by a New Rating Scale: Preliminary Report,” Journal of Cranio-Maxillofacial Surgery, Vol. 31, No. 5, pp. 299–303, 2003.
Kim, D. W., Kim, J. T., Hong, H. K., Nam, K. C. and Park, J. H., “Statistical Evaluation of the Cleft Lip Nose Deformity Image,” Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Vol. 1, pp. 3840–3842, 2006.
Kim, S. C., Nam, K. C., Kim, J. T., Hong, H. K., Cha, E. J. and Kim, D. W., “Quantitative Evaluation of Nose Deformity of Cleft Lips Using a Neural Network,” Journal of the Institute of Electronics Engineers of Korea, Vol. 43, No. 3, pp. 67–77, 2006.
Legendre, P., “Species Associations: The Kendall Coefficient of Concordance Revisited,” Journal of Agricultural, Biological, and Environmental Statistics, Vol. 10, No. 2, pp. 226–245, 2005.
Foster, J. J., “Data analysis-Using SPSS for Windows,” SAGE Publications, 2001.
Kim, S. W., Lee, T. Y., Kim, M. H., Bae, J. Y. and Lee, S. Y., “Flame Diagnosis Using Image Processing Technique,” International Journal of the Korean Society of Precision Engineering, Vol. 3, No. 2, pp. 33–44, 2002.
Kim, G. H., “Evaluation of Pre-estimation Model to the Inprocess Surface Roughness for Grinding Operations,” International Journal of the Korean Society of Precision Engineering, Vol. 3, No. 4, pp. 24–30, 2002.
Choi, J. C., Kim, Y. H. and Park, J. H., “Optimal Reheating Condition of Semi-solid Material in Semisolid Forging by Neural Network,” International Journal of the Korean Society of Precision Engineering, Vol. 4, No. 2, pp. 49–56, 2003.
Lee, J. S., “Automated Structural Design System Using Fuzzy Theory and Neural Network,” International Journal of the Korean Society of Precision Engineering, Vol. 3, No. 1, pp. 43–48, 2002.
Lee, J. W. and Lee, G. K., “Adaptive Postural Control for Trans-Femoral Prostheses Based on Neural Networks and EMG Signals,” Int. J. Precis. Eng. Manuf., Vol. 6, No. 3, pp. 37–44, 2005.
Hagan, M. T., Demuth, H. B. and Beale, M. H., “Neural Network Design,” PWS Publishing, 1996.
Vafaeesefat, A., “Optimum Creep Feed Grinding Process Conditions for Rene 80 Supper Alloy Using Neural Network,” Int. J. Precis. Eng. Manuf., Vol. 10, No. 3, pp. 5–11, 2009.
Yamada, T., Mori, Y., Minami, K., Mishima, K. and Sugahara, T., “Computer Aided Three-Dimensional Analysis of Nostril Forms: Application in Normal and Operated Cleft Lip Patients,” Journal of Cranio-Maxillofacial Surgery, Vol. 27, No. 6, pp. 345–353, 1999.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Xi, W., Vista, F., Kim, D.W. et al. Assessing the deformity of cleft lip nose based on neural network. Int. J. Precis. Eng. Manuf. 11, 473–482 (2010). https://doi.org/10.1007/s12541-010-0056-6
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12541-010-0056-6