Acute pulmonary embolism: artificial neural network approach for diagnosis.
PURPOSE: 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. MATERIALS AND 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). CONCLUSION: The findings suggest that an ANN can form the basis of a CAD system to assist physicians with the diagnosis of PE.
Duke Scholars
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Related Subject Headings
- Ventilation-Perfusion Ratio
- Radionuclide Imaging
- Radiography, Thoracic
- ROC Curve
- Pulmonary Embolism
- Nuclear Medicine & Medical Imaging
- Neural Networks, Computer
- Humans
- Diagnosis, Computer-Assisted
- Databases, Factual
Citation
Published In
DOI
ISSN
Publication Date
Volume
Issue
Start / End Page
Location
Related Subject Headings
- Ventilation-Perfusion Ratio
- Radionuclide Imaging
- Radiography, Thoracic
- ROC Curve
- Pulmonary Embolism
- Nuclear Medicine & Medical Imaging
- Neural Networks, Computer
- Humans
- Diagnosis, Computer-Assisted
- Databases, Factual