Li Haisheng, Lai Long, Chen Li, Lu Cheng, Cai Qiang
School of Computer and Information Engineering, Beijing Technology and Business University, Beijing 100048, China.
Peking University School and Hospital of Stomatology, Beijing 100081, China.
Comput Math Methods Med. 2015;2015:816719. doi: 10.1155/2015/816719. Epub 2015 Mar 22.
Although the use of computer color matching can reduce the influence of subjective factors by technicians, matching the color of a natural tooth with a ceramic restoration is still one of the most challenging topics in esthetic prosthodontics. Back propagation neural network (BPNN) has already been introduced into the computer color matching in dentistry, but it has disadvantages such as unstable and low accuracy. In our study, we adopt genetic algorithm (GA) to optimize the initial weights and threshold values in BPNN for improving the matching precision. To our knowledge, we firstly combine the BPNN with GA in computer color matching in dentistry. Extensive experiments demonstrate that the proposed method improves the precision and prediction robustness of the color matching in restorative dentistry.
尽管使用计算机颜色匹配可以减少技术人员主观因素的影响，但使陶瓷修复体与天然牙齿颜色相匹配仍然是口腔美学修复中最具挑战性的课题之一。反向传播神经网络（BPNN）已被引入牙科计算机颜色匹配中，但存在稳定性差和精度低等缺点。在我们的研究中，我们采用遗传算法（GA）来优化BPNN中的初始权重和阈值，以提高匹配精度。据我们所知，我们首次将BPNN与GA相结合用于牙科计算机颜色匹配。大量实验表明，该方法提高了修复牙科中颜色匹配的精度和预测鲁棒性。
