Deep learning in chest radiography: Detection of findings and presence of change

For chest radiographs, DL algorithms have found success in the evaluation of abnormalities such as lung nodules, pulmonary tuberculosis, cystic fibrosis, pneumoconiosis, and location of peripherally inserted central catheters. Chest radiography represents the most commonly performed radiological test for a multitude of non-emergent and emergent clinical indications. . The area under the curve (AUC) for DL and test radiologists ranged between 0.837–0.929 and 0.693–0.923, respectively. DL had lowest AUC (0.758) for assessing changes in pulmonary opacities over follow up CXR. DL algorithm can aid in interpretation of CXR findings and their stability over follow up CXR. However, in its present version, it is unlikely to replace radiologists due to its limited specificity for categorizing specific findings.

Performance of a Chest Radiography AI Algorithm for Detection of Missed or Mislabeled Findings: A Multicenter Study

The study found that the qXR has high sensitivity, specificity, and accuracy for detecting all missed and mislabeled CXR findings, including pulmonary nodules, consolidation, pleural effusions, pneumothorax, and rib fractures. The CXR AI has the potential to reduce the frequency of errors in detection and side-labeling of radiographic findings.