Role of an Automated Deep Learning Algorithm for Reliable Screening of Abnormality in Chest Radiographs: A Prospective Multicenter Quality Improvement Study

This study evaluated the effectiveness of an artificial intelligence (AI) chest X-ray screening tool called qXR in a large radiology network in India. The results showed that the AI system had good performance in detecting normal and abnormal chest X-rays, with a high negative predicted value (NPV) of 98.9%. The system was able to detect various abnormalities in chest X-rays with high accuracy, and the turnaround time decreased by 40.63%. This suggests that the qXR system can be a reliable and fast tool to assist radiologists in detecting abnormalities in chest X-rays and improve the radiological workflow.

Automated classification of Chest X-Rays as Normal/Abnormal using a high sensitivity deep learning algorithm

Majority of Chest X-rays (CXRs) performed globally are normal and radiologists spend significant time ruling out these scans. We present a Deep Learning (DL) model trained for the specific use of classifying CXRs into normal and abnormal, potentially reducing time and cost associated with reporting normal studies. qXr algorithm correctly classified 376 (87.44%) CXRs with a sensitivity of 97.19% (95% CI -94.54% to 98.78%) and specificity of 68.28% (95% CI -60.04% to 75.75%).
Conclusion: High-sensitivity DL algorithms can potentially be deployed for primary read of CXRs enabling radiologists to spend appropriate time on abnormal cases, saving time and thereby cost of reporting CXRs, especially in non-emergency situations. More in-depth prospective trials are required to ascertain the overall impact of such algorithms