Automated interpretation of planar thallium-201-dipyridamole stress-redistribution scintigrams using artificial neural networks.
Automated interpretation of planar thallium-201-dipyridamole stress-redistribution scintigrams using artificial neural networks.
复制标题
使用人工神经网络自动解释平面铊-201-双嘧达莫应力重新分布闪烁图。
DOI：
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发表时间：
1994
影响因子：
9.3
通讯作者：
H. Sochor
中科院分区：
文献类型：
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作者：
Gerold Porenta;Georg Dorffner;Stephan Kundrat;Paolo Petta;Johanna Duit;H. Sochor
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为了开发平面心脏 201Tl 双嘧达莫应力/再分布闪烁图的自动图像解释系统，作者使用了人工神经网络，将节段性心肌铊摄取模式与对重大冠状动脉疾病的存在、严重程度和定位的诊断评估联系起来。
方法
使用分段铊分析结果和 159 例专家读数或 81 名患者亚组的冠状动脉造影结果来训练和评估人工神经网络。
结果
根据接受者操作特征分析，以 90% 的特异性检测显着冠状动脉疾病的灵敏度与血管造影相比为 51%，与人类专家相比为 72%。对于疾病的严重程度和定位，分析中包括分配给左前降支 (LAD) 动脉的血管床和左回旋动脉和右冠状动脉共同包围的区域 (CX/RCA) 的两个血管区域。
结论
人工神经网络可能有助于开发 201Tl 灌注闪烁图的基于计算机的自动化图像解释系统。然而，利用大型训练数据集似乎是实现足够诊断性能的先决条件。
UNLABELLED
To develop an automated image interpretation system of planar cardiac 201Tl dipyridamole stress/redistribution scintigrams, the authors used artificial neural networks that associate patterns of segmental myocardial thallium uptake with a diagnostic assessment about the presence, severity and localization of significant coronary artery disease.
METHODS
Artificial neural networks were trained and evaluated using the results from segmental thallium analysis and either expert readings in 159 cases or coronary angiography in a subgroup of 81 patients.
RESULTS
Based on receiver operating characteristics analysis, the sensitivity for the detection of significant coronary artery disease at a specificity of 90% was 51% compared with angiography and 72% compared with the human expert. For severity and localization of disease, two vascular territories assigned to the vascular bed of the left anterior descending (LAD) artery and to the territory subtended by the left circumflex artery and the right coronary artery together (CX/RCA) were included in the analysis.
CONCLUSION
Artificial neural networks may be useful to develop automated computer-based image interpretation systems of 201Tl perfusion scintigrams. However, utilization of large training datasets appears to be a prerequisite to achieve adequate diagnostic performance.