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首页> 外文会议>Biomedical Circuits and Systems Conference, 2009. BioCAS 2009 >Decision-making models compatible with digital associative processor for orthodontic treatment planning
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Decision-making models compatible with digital associative processor for orthodontic treatment planning

机译：与数字联合处理器兼容的正畸治疗计划决策模型

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In this paper, decision-making models for orthodontic tooth extraction in treatment planning that are fully compatible with the digital associative processor has been developed. The architecture of the models was designed according to the specification of the digital associative processor. Feature variables were extracted from the pre-treatment clinical records of orthodontic patients and projected in the feature vector space by the 8-bit nonlinear transformation functions newly developed based on the expertise knowledge. Additionally, the fiducial treatments were defined by actual treatments described in the medical chart and judgments of three orthodontists having more than eight years of clinical experiences for each case. The sets of feature vectors and their corresponding fiducial treatments were employed as templates in the models. The N-neighboring search in the model templates was performed using weighed Manhattan distance as a dissimilarity measure to predict the optimum treatments for an input case. The hardware-friendly decision-making models for orthodontic tooth extraction were successfully developed and it was found that they were applicable in a clinical use.
机译：在本文中，开发了与数字关联处理器完全兼容的治疗计划中正畸牙齿拔除的决策模型。模型的体系结构是根据数字关联处理器的规范设计的。从正畸患者的治疗前临床记录中提取特征变量，并通过基于专业知识的新开发的8位非线性变换函数将特征变量投影到特征向量空间中。此外，基准治疗是根据医学图表中描述的实际治疗方法和对三名具有八年以上临床经验的正畸医生的判断来确定的。特征向量集及其对应的基准处理被用作模型中的模板。使用加权的曼哈顿距离作为相异性度量来执行模型模板中的N相邻搜索，以预测输入案例的最佳处理方式。成功开发了硬件友好的正畸拔牙决策模型，并发现它们可用于临床。

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