Assessing the deformity of cleft lip nose based on neural network

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.

A Systematic Review of Artificial Intelligence Applications in Plastic Surgery: Looking to the Future

“…Most papers (n = 37, 85%) reported an accuracy of the AI model used, although three of these were inadequate and potentially biased. 29 , 31 , 63 …”

“…AI was primarily used to aid in diagnosis and surgical planning (n = 9) 25,26,29,30,44,51–54 and to aid in the prediction (n = 12) 29,31,35,37,40,41,55–59 or objective evaluation of outcomes (n = 10) 28,27,47,50,56,60–63 within all subfields of plastic and reconstructive surgery. ( See figure, Supplemental Digital Content 5 , http://links.lww.com/PRSGO/C214.)…”

“…( See figure, Supplemental Digital Content 5 , http://links.lww.com/PRSGO/C214.) AI was frequently used to predict indications for surgical reconstruction, 17,24,25,30,31,33,43,57,62,64 the likelihood of clinical and technical outcomes (blood loss, 65 swelling‚ 50 response to antibiotics, 40 wound healing, 35,57,66 surgical site infection‚ 62 flap failure‚ 55 and overall survival 64 ), anatomical landmarks for surgical planning (skeletal profiles, 30,31,37 midfacial plane, 29 and perforators 52 ), and measurements of qualitative postoperative success (improvements in aesthetics, 28,29,49,53,56,60 form, 61,63 and function 32,47,54,67 ). The most popular subspecialty was aesthetic and breast surgery (n = 12, 27%), 25–28,47,49,52,55,56,60,61,68 followed by craniofacial surgery (n = 10, 23%), 29–32,37,41,51,54,67,68 nonbreast microsurgery (n = 6, 14%), 39,42,44,45,47,62 burn surgery (n = 5, 11%), 33,40,57,58,64 general plastic surgery (n = 4, 9%), 17,34,48,69 oral and maxillofacial surgery (n = 3, 7%), 43,50,65 wound care (n = 2, 5%), 35,59 and hand surgery (n = 2, 5%).…”

“…The most popular subspecialty was aesthetic and breast surgery (n = 12, 27%), 25–28,47,49,52,55,56,60,61,68 followed by craniofacial surgery (n = 10, 23%), 29–32,37,41,51,54,67,68 nonbreast microsurgery (n = 6, 14%), 39,42,44,45,47,62 burn surgery (n = 5, 11%), 33,40,57,58,64 general plastic surgery (n = 4, 9%), 17,34,48,69 oral and maxillofacial surgery (n = 3, 7%), 43,50,65 wound care (n = 2, 5%), 35,59 and hand surgery (n = 2, 5%). 36,46 AI models were most often in study phases 0 (n = 19) 29–31,33,37–39,41–46,57,61–64,68 and 1 (n = 12, 27%); only one article (2%) investigated clinical therapeutic efficacy (phase 3) (Fig. 2).…”

Remote activation and confidence factor setting of ARToolKit with data association for tracking multiple markers

“…The specified system features were analyzed and implemented through System Design, Development & Deployment Using Rapid by-Customer Demand with Business Principles Approach (RBCDwBPA) [12] which has features similar to AGILE [13]. RBCDwBPA is the formalized and more detailed version of the Rapid, Non-Formal and By-Customer-Demand Approach [14] that was successfully utilized in [15].…”