Tourassi G D, Floyd C E, Sostman H D, Coleman R E
Department of Radiology, Duke University Medical Center, Durham, NC 27710.
Radiology. 1995 Mar;194(3):889-93. doi: 10.1148/radiology.194.3.7862997.
To compare the diagnostic performance of an artificial neural network (ANN) with that of physicians in patients with suspected pulmonary embolism (PE).
An ANN was developed to predict PE by using findings from ventilation-perfusion lung scans and chest radiographs. First, the network was evaluated on 1,064 cases from the Prospective Investigation of Pulmonary Embolism Diagnosis (PIOPED) study that had a definitive angiographic outcome. An upper and lower bound of its diagnostic performance was provided depending on case difficulty. Then, the network was tested on 104 patients with suspected PE in whom pulmonary angiography was essential for diagnosis. The diagnostic performance of the ANN was compared with that of (a) two nuclear medicine physicians who read the scans for the needs of this study and (b) the nuclear medicine physicians who originally read the scans. The effects of case and observer selection on performance were addressed.
The ANN outperformed the physicians when they used the PIOPED criteria for categoric assessment, and it performed as well as the two study physicians on the basis of their probability assessments.
The ANN can detect or exclude PE in a highly selected group of difficult cases with a consistency equivalent to that of very experienced physicians.
比较人工神经网络（ANN）与医生对疑似肺栓塞（PE）患者的诊断性能。
开发了一种人工神经网络，通过通气-灌注肺扫描和胸部X光片的结果来预测肺栓塞。首先，该网络在1064例来自肺栓塞诊断前瞻性研究（PIOPED）且血管造影结果明确的病例上进行评估。根据病例难度提供了其诊断性能的上限和下限。然后，该网络在104例疑似肺栓塞患者身上进行测试，这些患者中肺血管造影对于诊断至关重要。将人工神经网络的诊断性能与以下两者进行比较：（a）两名为本研究需求解读扫描结果的核医学医生，以及（b）最初解读扫描结果的核医学医生。探讨了病例和观察者选择对性能的影响。
当医生使用PIOPED标准进行分类评估时，人工神经网络的表现优于医生；而基于概率评估时，其表现与两名参与研究的医生相当。
在一组经过高度筛选的疑难病例中，人工神经网络能够检测或排除肺栓塞，其一致性与经验丰富的医生相当。
