Fisher R E, Scott J A, Palmer E L
Department of Radiology, Massachusetts General Hospital, Boston 02114, USA.
Radiology. 1996 Mar;198(3):699-706. doi: 10.1148/radiology.198.3.8628857.
To optimize the performance of artificial neural networks in the prediction of pulmonary embolism from ventilation-perfusion (V-P) scans.
Neural networks were constructed with a set of V-P scan criteria that included sharpness and completeness of perfusion defects and involved quantification of abnormalities by using a continuous numeric scale. Several network parameters were systematically varied. Networks were trained with 150 cases and tested with 30 different cases. Findings were compared with those of pulmonary angiography.
Networks capable of performing as well as experienced nuclear medicine physicians could be constructed with few V-P scan features. A brief training period was optimal (50-100 iterations). Further training diminished network performance.
Effective neural networks can be constructed by using a limited number of unconventional V-P scan features. Several parameters can be adjusted to optimize performance.
优化人工神经网络在根据通气灌注（V-P）扫描预测肺栓塞方面的性能。
利用一组V-P扫描标准构建神经网络，这些标准包括灌注缺损的清晰度和完整性，并通过连续数字量表对异常情况进行量化。系统地改变了几个网络参数。用150例病例对网络进行训练，并用30例不同病例进行测试。将结果与肺血管造影结果进行比较。
利用很少的V-P扫描特征就能构建出性能与经验丰富的核医学医师相当的网络。较短的训练期是最佳的（50 - 100次迭代）。进一步训练会降低网络性能。
通过使用有限数量的非常规V-P扫描特征可以构建有效的神经网络。可以调整几个参数以优化性能。
