Tourassi G D, Floyd C E, Coleman R E
Department of Radiology, Duke University Medical Center, Durham, NC, USA.
Acad Radiol. 1996 Dec;3(12):1012-8. doi: 10.1016/s1076-6332(96)80035-3.
The authors improved the noninvasive diagnosis of acute pulmonary embolism (PE) by studying the clinical and chest radiographic findings of patients suspected of having PE and correlating those findings with the physicians' clinical impression.
A stepwise linear discriminant algorithm was developed on the basis of 1,064 patients from the Prospective Investigation of Pulmonary Embolism Diagnosis (PIOPED) study to select clinical and chest radiographic findings with the highest diagnostic power in patients suspected of having PE. Subsequently, a linear classifier and a nonlinear artificial neural network were developed to help diagnose PE on the basis of the reduced number of findings.
Both classifiers produced a statistically significant improvement (Az = 0.77 +/- 0.02) in the clinical performance of the PIOPED physicians (Az = 0.72 +/- 0.02). Results are also presented separately for groups of patients classified on the basis of the difficulty level of their ventilation-perfusion lung scans.
Two computer-aided diagnostic tools were developed to assist physicians in the assessment of the pretest likelihood of PE by using an optimally reduced number of findings.
作者通过研究疑似急性肺栓塞（PE）患者的临床及胸部X线表现，并将这些表现与医生的临床判断相关联，改进了急性肺栓塞的无创诊断方法。
基于肺栓塞诊断前瞻性研究（PIOPED）中的1064例患者，开发了一种逐步线性判别算法，以选择疑似PE患者中诊断能力最强的临床及胸部X线表现。随后，基于减少的表现数量，开发了线性分类器和非线性人工神经网络以辅助诊断PE。
两个分类器在PIOPED医生的临床诊断表现（Az = 0.72±0.02）上均产生了具有统计学意义的改善（Az = 0.77±0.02）。还根据通气-灌注肺扫描的难度水平对患者组分别给出了结果。
开发了两种计算机辅助诊断工具，通过使用最优减少的表现数量来协助医生评估PE的检查前可能性。
