Related Experiment Video
Updated: Aug 18, 2026
Quantitative Mapping of Specific Ventilation in the Human Lung using Proton Magnetic Resonance Imaging and Oxygen as a Contrast Agent
Published on: June 5, 2019
Neural networks in ventilation-perfusion imaging. Part II. Effects of interpretive variability
J A Scott1, R E Fisher, E L Palmer
1Department of Radiology, Massachusetts General Hospital, Boston 02114, USA.
Purpose:
To evaluate the usefulness of a neural network developed by one physician and used by another.
Materials And Methods:
Intra- and interobserver variability were analyzed in image categorization of ventilation-perfusion (V-P) scans. This information was used to estimate network performance when it was used by a physician who did not train the network.
Results:
Network training was optimized by using input parameters that demonstrated both individually high correlations with pulmonary embolism and good reproducibility in multiple interpretations.
Conclusion:
Potential variability exists in the performance of a network when it is supplied with input data by different physicians. The clinical usefulness of a network depends heavily on the similarity of interpretive styles between the network trainer and the user.
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