The Early Experience of Real-World use of Radiology Artificial Intelligence in HA Service

Background

There has been a lot of hype around the application of artificial intelligence (AI) in medical imaging. The number of publications on this subject has exploded lately. However, the vast majority of published studies were about development, validation and postulation of benefit of AI systems. Examples of actual usage of such systems are still scanty. We here share our early experience of actual usage of an AI system to triage non-urgent chest x rays.

Aim

The chest x-ray is the most commonly performed radiology investigation. Its sheer volume precludes reporting of all of them by radiologist in HA. And for exams reported, report turnaround time has much room for improvement. We aim to apply AI to triage non-urgent CXRs so that those exams requiring early attention could be reported earlier. 

Methodology

We used convolutional neural network based algorithms and leveraged a public dataset of CXRs to kickstart creation of a prediction model for CXRs. This model was applied to classify non-urgent CXR exams requested by general out-patient clinics into high or low risk such that priority reporting by radiologists can be done for high risk exams. We have performed retrospective virtual simulation of this AI-enabled priority reporting workflow followed by prospective pilot of such workflow in Hong Kong West Cluster.

Results

Both retrospective and prospective results showed that AI-enabled priority reporting workflow resulted in dramatic reduction in average report turnaround time for CXR exams that required early attention. This was achieved without additional radiologist manpower.

Conclusion

Employing AI to prioritize non-urgent CXRs translated into shorter report turnaround time for exams that required early attention.

Authors:

VSH Chan¹, BXH Fang¹, ITH Chen², ESL Yau², VKL Lau², EYW Yeung², PTM Lui², JCH Wan², AYH Tong², MCY Cheng², JCM Poon², WWT Chan², NT Cheung², YC Wong³, DML Tse¹, WWM Lam¹, TPW Lam¹

¹ Department of Radiology, Queen Mary Hospital, Hong Kong

² Information Technology and Health Informatics Division, Hospital Authority Head Office

³ Department of Radiology and Nuclear Medicine, Tuen Mun Hospital