Sensitivity and Specificity Evaluation of Deep Learning Models for Detection of Pneumoperitoneum on Chest Radiographs

Title:

Sensitivity and Specificity Evaluation of Deep Learning Models for Detection of Pneumoperitoneum on Chest Radiographs

Link:

https://arxiv.org/abs/2010.08872

Abstract:

Deep learning has great potential to assist with detecting and triaging critical findings such as pneumoperitoneum on medical images. To be clinically useful, the performance of this technology still needs to be validated for generalizability across different types of imaging systems. This retrospective study included 1,287 chest X-ray images of patients who underwent initial chest radiography at 13 different hospitals between 2011 and 2019. State-of-the-art deep learning models were trained on a subset of this dataset, and the automated classification performance was evaluated on the rest of the dataset by measuring the AUC, sensitivity, and specificity. All deep learning models performed well for identifying radiographs with pneumoperitoneum, while DenseNet161 achieved the highest AUC of 95.7%, Specificity of 89.9%, and Sensitivity of 91.6%. The DenseNet161 model was able to accurately classify radiographs from different imaging systems (Accuracy of 90.8%), while it was trained on images captured from a specific imaging system from a single institution. This result suggests the generalizability of our model for learning salient features in chest X-ray images to detect pneumoperitoneum, independent of the imaging system. If verified in clinical settings, this model could assist practitioners with the diagnosis and management of patients with this urgent condition.

Citation:

Manu Goyal, Judith Austin-Strohbehn, Sean J. Sun, Karen Rodriguez, Jessica M. Sin, Yvonne Y. Cheung, Saeed Hassanpour, “Sensitivity and Specificity Evaluation of Deep Learning Models for Detection of Pneumoperitoneum on Chest Radiographs”, International Conference on Artificial Intelligence in Medicine (AIME), 12721:307-317, 2021.