Acute pulmonary embolism: artificial neural network approach for diagnosis.
OBJECTIVE
To investigate use of an artificial neural network (ANN) as a computer-aided diagnostic (CAD) tool for predicting pulmonary embolism (PE) from ventilation-perfusion lung scans and chest radiographs.


METHODS
The data base consisted of cases extracted from the collaborative study of the Prospective Investigation of Pulmonary Embolism Diagnosis (PIOPED). Initially, scan findings from 1,064 patients (383 with PE, 681 without PE) were used to train and test the network by using the "jackknife" method. Then, a receiver-operating-characteristic analysis was applied to compare the performance of the network with that of the physicians involved in the PIOPED study.


RESULTS
The ANN significantly outperformed the physicians involved in the PIOPED study (two-tailed P value = .01).


CONCLUSIONS
The findings suggest that an ANN can form the basis of a CAD system to assist physicians with the diagnosis of PE.
Tourassi
G D
GD
Department of Radiology, Duke University Medical Center, Durham, NC 27710.
Floyd
C E
CE
Sostman
H D
HD
Coleman
R E
RE
D000208:Acute Disease; D000465:Algorithms; D000792:Angiography; D003199:Computer Systems; D016208:Databases, Factual; D003936:Diagnosis, Computer-Assisted; D006801:Humans; D016571:Neural Networks, Computer; D011655:Pulmonary Embolism / Q000175:diagnosis* / Q000000981:diagnostic imaging / Q000503:physiopathology; D012372:ROC Curve; D013902:Radiography, Thoracic; D011877:Radionuclide Imaging; D014692:Ventilation-Perfusion Ratio