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How Well Can Radiologists Using Neural Network Software Diagnose Pulmonary Embolism?

¹ All authors: Department of Radiology, Division of Nuclear Medicine, Massachusetts General Hospital and Harvard Medical School, Fruit St., Boston, MA 02114.

View larger version (32K): [in a new window]   Fig. 1. —Bar graph shows positive and negative predictive values for three human observers as well as those for network ANNMAX in predicting presence or absence of emboli in either lung. Performance of networks is comparable with that of human observers. ANNMAX = highest likelihood of embolism in right or left lung produced by networks trained separately on the two lungs, black bars = positive predictive value, white bars = negative predictive value.

View larger version (33K): [in a new window]   Fig. 2. —Bar graph shows positive and negative predictive values for three human observers as well as those for network ANNRSEG in predicting presence or absence of emboli in right lung. Performance of network is similar to that of human observers. ANNRSEG = networks trained on right-lung data using only cases without emboli or with acute segmental emboli, black bars = positive predictive value, white bars = negative predictive value.

View larger version (34K): [in a new window]   Fig. 3. —Bar graph shows positive and negative predictive values for three human observers as well as those for network ANNLSEG in predicting presence or absence of emboli in left lung. Positive predictive values in left lung are generally lower than are those in right lung for both human observers and network, although network and observer performance is comparable overall. ANNLSEG = networks trained on left-lung data using only cases without emboli or with acute segmental emboli, black bars = positive predictive value, white bars = negative predictive value.

View larger version (19K): [in a new window]   Fig. 4. —Graph shows receiver operating characteristic curves for three human observers and ANNMAX in detecting acute segmental emboli in either lung. ANNMAX = highest likelihood of embolism in left or right lung produced by networks trained separately on the two lungs; observer 1 = dotted line, observer 2 = dashed line, observer 3 = thin solid line, ANNMAX = thick solid line.

View larger version (15K): [in a new window]   Fig. 5. —Graph shows receiver operating characteristic curves for three human observers and ANNRSEG in detecting acute segmental emboli in right lung. ANNRSEG = networks trained on right-lung data using only cases without emboli or with acute segmental emboli; observer 1 = dotted line, observer 2 = dashed line, observer 3 = thin solid line, ANNRSEG = thick solid line.

View larger version (19K): [in a new window]   Fig. 6. —Graph shows receiver operating characteristic curves for three human observers and ANNLSEG in detecting acute segmental emboli in left lung. ANNLSEG = networks trained on left-lung data using only cases without emboli or with acute segmental emboli; observer 1 = dotted line, observer 2 = dashed line, observer 3 = thin solid line, ANNLSEG = thick solid line.

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