Artificial neural network for diagnosis of acute pulmonary embolism: effect of case and observer selection

Abstract

Purpose: To compare the diagnostic performance of an artificial neural network (ANN) with that of physicians in patients with suspected pulmonary embolism (PE).

Materials and methods: 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.

Results: 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.

Conclusion: 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.