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Takada 9 and Yagi 10 proposed a CBES that used a k-nearest neighbors ( k-NN) algorithm to perform classification in tooth-extraction decisions.
ORTHO EXTRACT SOFTWARE SOFTWARE
A software that combined RBES and CBES was proposed in Noroozi’s work 8, and the application of fuzzy logic made it more practical. The difficulty of using CBES lies in finding an exact case that matches the new case thus, some new cases have to be properly modified to be identified. CBESs acquire new knowledge by analyzing and taking in new cases, thereby acquiring better indexing features 7. To overcome the limitations of RBESs, case-based expert systems (CBESs) have been developed. RBESs use formulated rules to construct a decision tree but suffer from considerable knowledge lost in the rule determination. Rule-based expert systems (RBESs) were used to help orthodontic students and inexperienced practitioners with problem-solving and decision-making 7. Researchers have attempted to make orthodontic treatment planning procedures more objective by using some prediction methods. Comprehensive and deliberate evaluation of many factors makes treatment planning a complex process without any objective patterns, and heavily depends on the subjective judgment of the orthodontists. To achieve satisfactory orthodontic treatment effects, treatment planning must be carefully performed before the treatment process begins 6. The Health Policy Institute of the American Dental Association reported that 33% of young adults avoid smiling due to the condition of their mouth and teeth, and 82% of adults believe that the good appearance of the mouth and teeth can help them advance in life 5. An epidemiologic survey in America showed that 57% to 59% of each racial group has at least some degree of orthodontic treatment need 4. Malocclusion is a common disease that impairs occlusal function, increases the incidence of caries, causes psychological discomfort, endangers health and reduces the quality of life 1, 2, 3. These results indicate that the proposed method based on artificial neural networks can provide good guidance for orthodontic treatment planning for less-experienced orthodontists. For handling discrete input features with missing data, the average value method has a better complement performance than the k-nearest neighbors ( k-NN) method for handling continuous features with missing data, k-NN performs better than the other methods most of the time. The most important features for prediction of the neural networks are “crowding, upper arch” “ANB” and “curve of Spee”. The accuracies of the extraction patterns and anchorage patterns are 84.2% and 92.8%, respectively. The neural network models show an accuracy of 94.0% for extraction-nonextraction prediction, with an area under the curve (AUC) of 0.982, a sensitivity of 94.6%, and a specificity of 93.8%. The neural network can output the feasibilities of several applicable treatment plans, offering orthodontists flexibility in making decisions. Conclusion: In Malaysian females, significant changes were found in soft-tissue profile post-orthodontic treatment with corresponding simultaneous change in the underlying hard tissue.In this study, multilayer perceptron artificial neural networks are used to predict orthodontic treatment plans, including the determination of extraction-nonextraction, extraction patterns, and anchorage patterns. The Pearson’s correlation test showed that the lower lip contacts both the upper and lower incisors hence, the lower lip position would be impacted not only by the lower incisor retraction but also by the upper incisor retraction. Results: All linear and angular measurements of hard- and soft-tissue changes showed significant differences except L1 to A-pogonion (A-pog) angle ( P = 0.05), and the mean change has decreased to almost 1.0mm posttreatment. Data were analyzed using paired t-test to determine the difference between two means. Linear and angular measurements were made. Pre and posttreatment lateral cephalograms of patients were traced on a cephalometric tracing software CASSOS (Soft Enable Technology Limited, Hong Kong). The average of treatment time was 22 months. Materials and Methods: This is a retrospective study involving 24 Malaysian female patients age between 18 and 24 years treated with extraction of the upper and lower first premolars and bonded by 0.22 × 0.28′′ standard edgewise technique. Aim: To quantify the amount of soft-tissue changes in Malaysian female patients treated with the extraction of first four premolars.
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