A FEED FORWARD NEURAL-NETWORK FOR CLASSIFICATION OF BULLS-EYE MYOCARDIAL PERFUSION IMAGES
A FEED FORWARD NEURAL-NETWORK FOR CLASSIFICATION OF BULLS-EYE MYOCARDIAL PERFUSION IMAGES
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DOI：
10.1007/bf00838939
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发表时间：
1995-02-01
期刊：
影响因子：
--
通讯作者：
AMRO, AA
中科院分区：
文献类型：
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作者：
HAMILTON, D;RILEY, PJ;AMRO, AA
心肌灌注单光子发射断层扫描研究中低灌注区的识别可以通过原始计数、病变范围和病变严重程度的靶心表示来辅助，后两者是通过将原始靶心数据与正常数据库进行比较而产生的。一种目前正变得广泛流行并且特别适合于模式识别的人工智能技术是人工神经网络技术。我们已经研究了前馈模式的能力，从靶心预测病变存在的能力与正常的数据库比较。对410例接受常规铊-201心肌灌注显像的男性患者的模拟数据和真实的负荷-静息数据进行了研究。通过计算受试者工作特征曲线下的面积来量化训练的神经网络预测病变存在的能力。未预分类患者数据的数字高达0.96，对应于92%的准确性。结果表明，神经网络可以准确地分类模式从靶心心肌灌注图像和检测低灌注区的存在，而不需要与正常的数据库进行比较。初步工作表明，该技术可用于研究心肌灌注模式及其与临床参数的相关性。
Identification of hypoperfused areas in myocardial perfusion single-photon emission tomography studies can be aided by bull's-eye representation of raw counts, lesion extent and lesion severity, the latter two being produced by comparison of the raw bull's-eye data with a normal data base. An artificial intelligence technique which is presently becoming widely popular and which is particularly suitable for pattern recognition is that of artificial neural network. We have studied the ability of feed forward patterns from bull's-eye capability to predict lesion presence comparison with a normal data base. Studies were undertaken on both simulation data and on real stress-rest data obtained from 410 male patients undergoing routine thallium-201 myocardial perfusion scintigraphy. The ability of trained neural networks to predict lesion presence was quantified by calculating the areas under receiver operating characteristic curves. Figures as high as 0.96 for non-preclassified patient data were obtained, corresponding to an accuracy of 92%. The results demonstrate that neural networks can accurately classify patterns from bull's-eye myocardial perfusion images and detect the presence of hypoperfused areas without the need for comparison with a normal data base. Preliminary work suggests that this technique could be used to study perfusion patterns in the myocardium and their correlation with clinical parameters.