Devising a deep neural network based mammography phantom image filtering algorithm using images obtained under mAs and kVp control

We study whether deep neural network based algorithm can filter out mammography phantom images that will pass or fail. With 543 phantom images generated from a mammography unit, we created VGG16 based phantom shape scoring models (multi-and binary-class classifiers). Using these models we designed filtering algorithms that can filter failed or passed phantom images. 61 phantom images obtained from two different medical institutions were used for external validation. The performances of the scoring models show an F1-score of 0.69 (95% confidence interval (CI) 0.65, 0.72) for multi-class classifiers and an F1-score of 0.93 (95% CI 0.92, 0.95) and area under the receiver operating characteristic curve of 0.97 (95% CI 0.96, 0.98) for binary-class classifiers. A total of 42 of the 61 phantom images (69%) were filtered by the filtering algorithms without further need for assessment from a human observer. This study demonstrated the potential to reduce the human workload from mammographic phantom interpretation using the deep neural network based algorithm.

Automated summary

This study investigates the use of a deep neural network based algorithm to filter mammography phantom images that will pass or fail. Based on 543 phantom images from a mammography unit, VGG16 based phantom shape scoring models were created, and filtering algorithms were designed to filter out failed or passed phantom images. External validation using 61 phantom images from two different medical institutions showed that the scoring models achieved F1-scores of 0.69 (95% confidence interval 0.65, 0.72) for multi-class classifiers and 0.93 (95% CI 0.92, 0.95) for binary-class classifiers, with an area under the receiver operating characteristic curve of 0.97 (95% CI 0.96, 0.98). Out of the 61 phantom images, 42 (69%) were filtered by the algorithms without the need for further assessment by a human observer. This study demonstrates the potential of using deep neural network based algorithms to reduce the human workload in mammographic phantom interpretation.