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首页> 外文会议>Engineering in Medicine and Biology Society, 2000. Proceedings of the 22nd Annual International Conference of the IEEE >Diagnostic reasoning by classification in upper digestive tract endoscopy
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Diagnostic reasoning by classification in upper digestive tract endoscopy

机译：上消化道内镜中的分类诊断诊断

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The purpose of the authors' project is to build an atlas of indexed endoscopical lesions that could be used in computer-assisted diagnostics as referring data. The analysis of both endoscopists' experience and computerized record format developed by professional shows that the diagnostic reasoning in digestive endoscopy uses a scene-object approach. The objects correspond to the endoscopical findings and the medical context of examination, and the scene to the endoscopical diagnosis. Based on expert assessment, a computer-assisted diagnosis system is described using a classification reasoning approach. The classes of endoscopical findings and diagnoses are listed and the primitive characteristics of objects are extracted. Each class content is defined in an intensive way, and symbolic distances between descriptors and classes leads to an a priori description of classes. Symbolic distances are transformed by heuristic in numeric measures that allow estimation of the membership value of an observed case to a defined class. A simulation test with 50 randomized objects by class demonstrates a good classification of endoscopical findings. The correct class is the unique Response in 71% of the tested objects, the first of multiple Responses in 23% and one of the multiple responses in 6%. Four descriptors are of major importance in the classification algorithm: anatomic location, shape, color and relief. A computerized application is currently developed to archive endoscopical cases according to the classification method.
机译：作者项目的目的是建立索引的内窥镜病变地图集，该地图集可在计算机辅助诊断中用作参考数据。对内镜医师的经验和专业人士开发的计算机记录格式的分析表明，消化内窥镜的诊断推理使用场景对象方法。这些对象对应于内窥镜检查结果和检查的医学背景，以及对应于内窥镜检查诊断的场景。基于专家评估，使用分类推理方法描述了计算机辅助诊断系统。列出了内窥镜检查结果和诊断的类别，并提取了对象的原始特征。每个类的内容都经过严格的定义，描述符和类之间的符号距离导致对类的先验描述。符号距离通过启发式方法在数字量度中进行转换，从而可以将观察到的案例的隶属度值估计为已定义的类别。按类别对50个随机对象进行的模拟测试证明了内窥镜检查结果的良好分类。正确的类别是71％的测试对象中的唯一响应，23％的多个响应中的第一个以及6％的多个响应中的一个。在分类算法中，四个描述符是最重要的：解剖位置，形状，颜色和浮雕。当前，根据分类方法，开发了计算机化的应用程序以对内窥镜病例进行存档。

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