xrAI - Improving Quality and Efficiency in Chest Radiograph Interpretation by Radiologists
xrAI - Improving Quality and Efficiency in Chest Radiograph Interpretation by Radiologists
xrAI (pronounced "X-ray") serves as a clinical assistance tool for trained clinical professionals who are interpreting chest radiographs. The tool is designed as a quality control and adjunct, limited, clinical decision support tool, and does not replace the role of clinical professionals. It highlights areas on chest radiographs for review by an interpreting clinician.
The objective of this study is to utilize machine learning and artificial intelligence algorithms (xrAI) to improve the quality and efficiency in the interpretation of chest radiographs by radiologists.
The hypothesis is that the addition of xrAI's analysis will reduce inter-observer variability in the interpretation of chest radiographs and increase participants' sensitivity, recall, and accuracy in pulmonary abnormality screening.
To investigate the effect of xrAI for radiologists that interpret chest radiographs as part of their daily responsibilities, the investigators have designed a randomized control trial.
The pulmonary abnormalities detected by xrAI and included in the definition of abnormal are as follows: any linear scar or fibrosis, atelectasis, consolidation, abscess or cavity, nodule, pleural effusion, severe cases of emphysema and COPD (mild cases with hyperinflation but not significant emphysema are not flagged), and pneumothorax.
To assess the causal effect of xrAI the investigators randomly assign 10 to 14 radiologists to either treatment (x-ray images processed by xrAI) or control (no xrAI processing) groups. Participants will only review images once. Each participant will perform 500 radiograph interpretations in total.
Participants in the control group will be asked to interpret the same 500 images without xrAI's analysis.
To increase the precision of the estimate and better investigate potential differences between clinical professionals, investigators block randomize the assignment of treatment or control group.
To analyse the effect of xrAI, the investigators will estimate the average treatment effect (ATE) for each group by comparing the performance of the treatment and control groups using randomization-based inference (Green and Gerber, 2012).
Inclusion Criteria:
Exclusion Criteria: