Improving Chest X-Ray Classification by RNN-based Patient Monitoring
This work addresses the need for more accurate automated chest X-ray assessment in clinical settings, but it is incremental as it builds on existing CNN-based methods by adding patient history.
The study tackled the problem of improving chest X-ray classification by incorporating patient history information, such as previous images and diagnoses, and showed that a model trained with this additional data significantly outperformed one without it.
Chest X-Ray imaging is one of the most common radiological tools for detection of various pathologies related to the chest area and lung function. In a clinical setting, automated assessment of chest radiographs has the potential of assisting physicians in their decision making process and optimize clinical workflows, for example by prioritizing emergency patients. Most work analyzing the potential of machine learning models to classify chest X-ray images focuses on vision methods processing and predicting pathologies for one image at a time. However, many patients undergo such a procedure multiple times during course of a treatment or during a single hospital stay. The patient history, that is previous images and especially the corresponding diagnosis contain useful information that can aid a classification system in its prediction. In this study, we analyze how information about diagnosis can improve CNN-based image classification models by constructing a novel dataset from the well studied CheXpert dataset of chest X-rays. We show that a model trained on additional patient history information outperforms a model trained without the information by a significant margin. We provide code to replicate the dataset creation and model training.