Neural networks in ventilation-perfusion imaging.
OBJECTIVE
To optimize the performance of artificial neural networks in the prediction of pulmonary embolism from ventilation-perfusion (V-P) scans.


METHODS
Neural networks were constructed with a set of V-P scan criteria that included sharpness and completeness of perfusion defects and involved quantification of abnormalities by using a continuous numeric scale. Several network parameters were systematically varied. Networks were trained with 150 cases and tested with 30 different cases. Findings were compared with those of pulmonary angiography.


RESULTS
Networks capable of performing as well as experienced nuclear medicine physicians could be constructed with few V-P scan features. A brief training period was optimal (50-100 iterations). Further training diminished network performance.


CONCLUSIONS
Effective neural networks can be constructed by using a limited number of unconventional V-P scan features. Several parameters can be adjusted to optimize performance.
Fisher
R E
RE
Department of Radiology, Massachusetts General Hospital, Boston 02114, USA.
Scott
J A
JA
Palmer
E L
EL
D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000792:Angiography; D006801:Humans; D008168:Lung / Q000098:blood supply / Q000000981:diagnostic imaging*; D008875:Middle Aged; D016571:Neural Networks, Computer; D011655:Pulmonary Embolism / Q000000981:diagnostic imaging*; D011877:Radionuclide Imaging; D014692:Ventilation-Perfusion Ratio; D014980:Xenon Radioisotopes
D014980:Xenon Radioisotopes