신경망 인공지능 의사결정 모델을 이용한 발치 진단의 새로운 방법 제안

학위논문 (박사)-- 서울대학교 대학원 : 치의학대학원 치의과학과 치과교정학전공, 2016. 2. 김태우.Introduction: The diagnosis of extractions in the orthodontic treatment is important and difficult, because that decision has tendency to be based on the practitioners experiences. The purpose of this study was to construct an artificial intelligent expert system for the diagnosis of extraction using neural network machine learning (NNML) and to evaluate performance of this model. Methods: The subjects consisted of 156 patients in total. Input data consisted of 12 cephalometric variables and additional six indices. Output data consisted of three bits to divide extraction patterns. Four NNML models for the diagnosis of extractions were constructed using backpropagation algorithm, and were evaluated. Results: The success rates of the models showed 93% for the diagnosis of extraction versus non-extraction, and showed 84% for the detailed diagnosis of the extraction patterns. Conclusions: This study suggests that artificial intelligent expert systems using neural network machine learning could be useful in orthodontics. Improving performance was achieved by the components such as proper selection of the input data, appropriate organization of the modeling, and preferable generalization.I. Introduction 1 II. Review of Literature 3 III. Material and Methods 9 IV. Results 13 V. Discussion 15 VI. Conclusion 19 VII. References 30Docto

An Investigation into Neuromorphic ICs using Memristor-CMOS Hybrid Circuits

The memristance of a memristor depends on the amount of charge flowing through it and when current stops flowing through it, it remembers the state. Thus, memristors are extremely suited for implementation of memory units. Memristors find great application in neuromorphic circuits as it is possible to couple memory and processing, compared to traditional Von-Neumann digital architectures where memory and processing are separate. Neural networks have a layered structure where information passes from one layer to another and each of these layers have the possibility of a high degree of parallelism. CMOS-Memristor based neural network accelerators provide a method of speeding up neural networks by making use of this parallelism and analog computation. In this project we have conducted an initial investigation into the current state of the art implementation of memristor based programming circuits. Various memristor programming circuits and basic neuromorphic circuits have been simulated. The next phase of our project revolved around designing basic building blocks which can be used to design neural networks. A memristor bridge based synaptic weighting block, a operational transconductor based summing block were initially designed. We then designed activation function blocks which are used to introduce controlled non-linearity. Blocks for a basic rectified linear unit and a novel implementation for tan-hyperbolic function have been proposed. An artificial neural network has been designed using these blocks to validate and test their performance. We have also used these fundamental blocks to design basic layers of Convolutional Neural Networks. Convolutional Neural Networks are heavily used in image processing applications. The core convolutional block has been designed and it has been used as an image processing kernel to test its performance.Comment: Bachelor's thesi

Cephalometric Variables Prediction from Lateral Photographs Between Different Skeletal Patterns Using Regression Artificial Neural Networks

Objective: This study aimed to design an artificial neural network for the prediction of cephalometric variables via a lateral photograph in skeletal Class I, II, and III patterns.Methods: A total of 94 patients were recruited for this prospective study, with an age range of 15-20 years (41 boys and 53 girls) seeking orthodontic treatment. According to cephalometric analysis, using AutoCAD 21.0, they were allocated into three groups. Thirty with skeletal Class I (14 boys and 16 girls), 34 with skeletal Class II (14 boys and 20 girls), and 30 with skeletal Class III malocclusion (13 boys and 17 girls) according to SNA, SNB, and ANB angles measured from cephalometric radiographs. The study includes (1) finding the correlation of the skeletal measurements between lateral profile photographs and cephalometric radiographs for the recruited patients and (2) designing a specific artificial neural networks for the assessment of skeletal factors via lateral photographs, these artificial neural networks are trained and tested with the total of 94 standard lateral cephalograms.Results: This novel Network provided models of regression that can forecast the cephalometric variables through analogous photographic measurements with excellent predictive power R = 0.99 and limited estimation error for each malocclusion (Class I, II, and III).Conclusion: This study suggests that artificial intelligence would be useful as an accurate method in orthodontics for the prediction of cephalometric variables and its performance was achieved by several factors such as proper selection of the input data, preferable generalization, and organization