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RADIOGRAPHIC DIFFERENTIAL DIAGNOSIS IN DENTAL REGION USING EXPERT SYSTEM

使用专家系统对牙科区域进行放射线鉴别诊断

基本信息

批准号：
08650470
负责人：
KODERA Yoshie
金额：
$ 1.41万
依托单位：
KINKI UNIVERSITY
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (C)
财政年份：
1996
资助国家：
日本
起止时间：
1996 至 1997
项目状态：
已结题

来源：
https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-08650470/
关键词：
Expert system Neural network Statistical analysis Logistic regression computer-aided diagnostic system Dental x-ray image Radiographic differential diagnosis 歯科X線画像

项目摘要

Radiologists performed radiographic differential diagnosis from qualitative features in radiogram. For this kind of diagnosis, computer-aided diagnostic (CAD) system is watched as one of an engineering approach. Dentigerous cyst, odontogenic keratocyst and ameloblastoma in maxillofacial region occur frequently, but their recuperations are different. Then it is necessary to make an exact differential diagnosis, although their symptoms are similar to one another. As a pilot study, we investigated statistical methods using a logistic regression and neural network. Under these studies, Kodera presented as "Differential diagnosis by neural network and statistical analysis in oral radiology" in the 12th Annual Meeting Japanese Association of Medical Physics in July 1995. And, Ohtani presented as "Radiographic differential diagnosis among dentigerous cyst, odontogenic keratocyst and ameloblatoma using statistical methods and neural network" in the 36th Japanese Society for Oral and Maxillofacial Radiology Annual meeting in September 1995. Based on these results, in 1996, we constructed the system and accumulated data. Kodera presented as "Radiographic differential diagnosis in dental region using expert system" in the 37th Japanese Society for Oral and Maxillofacial Radiology Annual meeting in September 1996. Finally, we evaluated clinically the developed system in Hiroshima University, namely dentists evaluated and confirmed the effectiveness of these methods. The method proposed in this study was applied to the other area in dental images.

放射科医生根据放射线照片的定性特征进行放射线鉴别诊断。对于这种诊断，计算机辅助诊断（CAD）系统被视为一种工程方法。颌面部含牙囊肿、牙源性角化囊肿、成釉细胞瘤等发病率较高，但恢复情况不同。尽管它们的症状相似，但还是有必要做出准确的鉴别诊断。作为一项试点研究，我们使用逻辑回归和神经网络研究了统计方法。根据这些研究，Kodera在1995年7月第12届日本医学物理学会年会上发表了“口腔放射学中的神经网络和统计分析的鉴别诊断”。Ohtani在第36届日本口腔颌面放射学会年会上发表了“使用统计方法和神经网络对含牙囊肿、牙源性角化囊肿和成釉细胞瘤进行放射线鉴别诊断”。 1995年9月召开了会议。基于这些成果，我们于1996年构建了系统并积累了数据。 1996年9月，Kodera在第37届日本口腔颌面放射学会年会上发表了“使用专家系统在牙科区域进行放射线鉴别诊断”的报告。最后，我们对广岛大学开发的系统进行了临床评估，即牙医评估并确认了这些方法的有效性。本研究提出的方法应用于牙科图像的其他区域。