Applying these findings in the real world
The AI system produces a number between 0 and 1 that indicates the risk of TB. For the system to be useful in a real-world setting, there needs to be agreement about what risk level indicates that patients should be recommended for additional testing. Calibrating this threshold can be time-consuming and expensive because administrators can only come to this number after running the system on hundreds of patients, testing these patients, and analyzing the results.
Based on the performance of our model, our research suggests that any clinic could start from this default threshold and be confident that the model will perform similarly to radiologists, making it easier to deploy this technology. From there, clinics can adjust the threshold based on local needs and resources. For example, regions with fewer resources may use a higher cut-off point to reduce the number of follow-up tests needed.