Deep Neural Network Augments Performance of Junior Residents in Diagnosing COVID-19 Pneumonia on Chest Radiographs

Deep Neural Network Augments Performance of Junior Residents in Diagnosing COVID-19 Pneumonia on Chest Radiographs

Diagnostics

10.3390/diagnostics13081397

https://doi.org/10.3390/diagnostics13081397

Yangqin Feng, Jordan Sim Zheng Ting, Xinxing Xu, Chew Bee Kun, Edward Ong Tien En, Hendra Irawan Tan Wee Jun, Yonghan Ting, Xiaofeng Lei, Wen-Xiang Chen, Yan Wang, Shaohua Li, Yingnan Cui, Zizhou Wang, Liangli Zhen, Yong Liu, Rick Siow Mong Goh, Cher Heng Tan

General Medicine

12 April 2023

Feng, Y., Sim Zheng Ting, J., Xu, X., Bee Kun, C., Ong Tien En, E., Irawan Tan Wee Jun, H., Ting, Y., Lei, X., Chen, W.-X., Wang, Y., Li, S., Cui, Y., Wang, Z., Zhen, L., Liu, Y., Siow Mong Goh, R., & Tan, C. H. (2023). Deep Neural Network Augments Performance of Junior Residents in Diagnosing COVID-19 Pneumonia on Chest Radiographs. Diagnostics, 13(8), 1397. https://doi.org/10.3390/diagnostics13081397

Chest X-rays (CXRs) are essential in the preliminary radiographic assessment of patients affected by COVID-19. Junior residents, as the first point-of-contact in the diagnostic process, are expected to interpret these CXRs accurately. We aimed to assess the effectiveness of a deep neural network in distinguishing COVID-19 from other types of pneumonia, and to determine its potential contribution to improving the diagnostic precision of less experienced residents. A total of 5051 CXRs were utilized to develop and assess an artificial intelligence (AI) model capable of performing three-class classification, namely non-pneumonia, non-COVID-19 pneumonia, and COVID-19 pneumonia. Additionally, an external dataset comprising 500 distinct CXRs was examined by three junior residents with differing levels of training. The CXRs were evaluated both with and without AI assistance. The AI model demonstrated impressive performance, with an Area under the ROC Curve (AUC) of 0.9518 on the internal test set and 0.8594 on the external test set, which improves the AUC score of the current state-of-the-art algorithms by 1.25% and 4.26%, respectively. When assisted by the AI model, the performance of the junior residents improved in a manner that was inversely proportional to their level of training. Among the three junior residents, two showed significant improvement with the assistance of AI. This research highlights the novel development of an AI model for three-class CXR classification and its potential to augment junior residents’ diagnostic accuracy, with validation on external data to demonstrate real-world applicability. In practical use, the AI model effectively supported junior residents in interpreting CXRs, boosting their confidence in diagnosis. While the AI model improved junior residents’ performance, a decline in performance was observed on the external test compared to the internal test set. This suggests a domain shift between the patient dataset and the external dataset, highlighting the need for future research on test-time training domain adaptation to address this issue.

Attribution 4.0 International (CC BY 4.0)

This research / project is supported by the Agency for Science, Technology and Research - Advanced Manufacturing and Engineering Programmatic Funding Scheme
Grant Reference no. : A20H4b0141

https://oar.a-star.edu.sg/communities-collections/articles/22555

2075-4418

Institute of High Performance Computing 

https://doi.org/10.3390/diagnostics13081397