Xiaoqiu Xie1, Lin Wang, Aming Wang. 1. Department of Orthodontics, Stomatology Hospital Affiliated with Medical School, Nanjing University, Nanjing, PR China.
Abstract
OBJECTIVE: To construct a decision-making expert system (ES) for the orthodontic treatment of patients between 11 and 15 years old to determine whether extraction is needed by using artificial neural networks (ANN). Specifically, we will uncover the factors that affect this decision-making process. METHODS: A total of 200 subjects were chosen; among them, 120 were accepted for extraction treatments, and 80 were chosen for nonextraction treatments. For each case, 23 indices were selected. A 23-13-1 Back Propagation (BP) ANN model was constructed, and the data for 180 patients were aggregated to constitute the training set. Data for the other 20 patients were used as the testing set. RESULTS: When data from the 180 patients that had been trained were tested, the result was 100%, as expected. The untrained data from 20 patients in the testing set were 80% correct (ie, 16 cases were forecasted successfully). In the meantime, the relative contributions of the 23 input indices to the final output index (extraction/nonextraction) were calculated. "Anterior teeth uncovered by incompetent lips" and "IMPA (L1-MP)" were the two indices that gave the biggest contributions sequentially; the index of FMA (FH-MP) gave the smallest contribution. CONCLUSIONS: (1) The constructed artificial neural network in this study was effective, with 80% accuracy, in determining whether extraction or nonextraction treatment was best for malocclusion patients between 11 and 15 years old; (2) when the clinician is predicting whether an orthodontic treatment requires extraction, the indices "anterior teeth uncovered by incompetent lips" and "IMPA (L1-MP)" should be taken into consideration first.
OBJECTIVE: To construct a decision-making expert system (ES) for the orthodontic treatment of patients between 11 and 15 years old to determine whether extraction is needed by using artificial neural networks (ANN). Specifically, we will uncover the factors that affect this decision-making process. METHODS: A total of 200 subjects were chosen; among them, 120 were accepted for extraction treatments, and 80 were chosen for nonextraction treatments. For each case, 23 indices were selected. A 23-13-1 Back Propagation (BP) ANN model was constructed, and the data for 180 patients were aggregated to constitute the training set. Data for the other 20 patients were used as the testing set. RESULTS: When data from the 180 patients that had been trained were tested, the result was 100%, as expected. The untrained data from 20 patients in the testing set were 80% correct (ie, 16 cases were forecasted successfully). In the meantime, the relative contributions of the 23 input indices to the final output index (extraction/nonextraction) were calculated. "Anterior teeth uncovered by incompetent lips" and "IMPA (L1-MP)" were the two indices that gave the biggest contributions sequentially; the index of FMA (FH-MP) gave the smallest contribution. CONCLUSIONS: (1) The constructed artificial neural network in this study was effective, with 80% accuracy, in determining whether extraction or nonextraction treatment was best for malocclusion patients between 11 and 15 years old; (2) when the clinician is predicting whether an orthodontic treatment requires extraction, the indices "anterior teeth uncovered by incompetent lips" and "IMPA (L1-MP)" should be taken into consideration first.
Authors: Manal H Hamdan; Lyudmila Tuzova; André Mol; Peter Z Tawil; Dmitry Tuzoff; Donald A Tyndall Journal: Dentomaxillofac Radiol Date: 2022-09-12 Impact factor: 3.525
Authors: Yashodhan M Bichu; Ismaeel Hansa; Aditi Y Bichu; Pratik Premjani; Carlos Flores-Mir; Nikhilesh R Vaid Journal: Prog Orthod Date: 2021-07-05 Impact factor: 2.750