Machine Learning Model for Chest Radiographs: Using Local Data to Enhance Performance

Abstract

PURPOSE: To develop and assess the performance of a machine learning model which screens chest radiographs for 14 labels, and to determine whether fine-tuning the model on local data improves its performance. Generalizability at different institutions has been an obstacle to machine learning model implementation. We hypothesized that the performance of a model trained on an open-source dataset will improve at our local institution after being fine-tuned on local data. METHODS: In this retrospective, institutional review board approved study, an ensemble of neural networks was trained on open-source datasets of chest radiographs for the detection of 14 labels. This model was then fine-tuned using 4510 local radiograph studies, using radiologists' reports as the gold standard to evaluate model performance. Both the open-source and fine-tuned models' accuracy were tested on 802 local radiographs. Receiver-operator characteristic curves were calculated, and statistical analysis was completed using DeLong's method and Wilcoxon signed-rank test. RESULTS: < .01). CONCLUSIONS: A machine learning model able to accurately detect 14 labels simultaneously on chest radiographs was developed using open-source data, and its performance was improved after fine-tuning on local site data. This simple method of fine-tuning existing models on local data could improve the generalizability of existing models across different institutions to further improve their local performance.

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