Machine learning "red dot": open-source, cloud, deep convolutional neural networks in chest radiograph binary normality classification. Issue 9 (September 2018) - Yates, E.J.; Yates, L.C.; Harvey, H.
Abstract : Aim: To develop a machine learning-based model for the binary classification of chest radiography abnormalities, to serve as a retrospective tool in guiding clinician reporting prioritisation. Materials and methods: The open-source machine learning library, Tensorflow, was used to retrain a final layer of the deep convolutional neural network, Inception, to perform binary normality classification on two, anonymised, public image datasets. Re-training was performed on 47, 644 images using commodity hardware, with validation testing on 5, 505 previously unseen radiographs. Confusion matrix analysis was performed to derive diagnostic utility metrics. Results: A final model accuracy of 94.6% (95% confidence interval [CI]: 94.3–94.7%) based on an unseen testing subset ( n =5, 505) was obtained, yielding a sensitivity of 94.6% (95% CI: 94.4–94.7%) and a specificity of 93.4% (95% CI: 87.2–96.9%) with a positive predictive value (PPV) of 99.8% (95% CI: 99.7–99.9%) and area under the curve (AUC) of 0.98 (95% CI: 0.97–0.99). Conclusion: This study demonstrates the application of a machine learning-based approach to classify chest radiographs as normal or abnormal. Its application to real-world datasets may be warranted in optimising clinician workload. Highlights: The radiographer 'red dot' is a longstanding method of flagging abnormal radiographs. Deep machine learning has previously been applied to clinical image interpretation. Red dot model training on ∼50000 chest x-rays developed a binary normality classifier. Application to real-world datasets may triage abnormal images for formal reporting.