Abstract:
:A neural network model was constructed to interpret ventilation-perfusion (V/Q) lung scans. This model was trained with data from 100 consecutive V/Q scans with pulmonary angiographic correlation. The network was constructed from 28 input parameters that described various standard V/Q findings, which were fed into a single hidden layer that contained 10-20 nodes. The network output indicated the percentage probability of pulmonary embolism for each set of findings on V/Q scans. This network was then used to classify 28 new scans; the resultant classifications were compared with the rankings of an experienced observer who read the scans without knowledge of the correlative angiographic data. The network with 15 hidden nodes outperformed the experienced observer in prediction of the likelihood of pulmonary embolism in the 28-case test set (P = .039). The neural network has several advantages over current algorithms for interpretation of V/Q scans, including the ability to synthesize many variables into a single conclusion and to learn, or modify itself, at exposure to additional data.
journal_name
Radiologyjournal_title
Radiologyauthors
Scott JA,Palmer ELdoi
10.1148/radiology.186.3.8430170subject
Has Abstractpub_date
1993-03-01 00:00:00pages
661-4issue
3eissn
0033-8419issn
1527-1315journal_volume
186pub_type
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