InferRead DR Chest

Efficacy

Retrospective cohort study (OCEBM 3) testing DL model for identifying fresh vertebral compression fractures (VCFs) with MRI as reference standard. 1877 VCFs in 1099 patients; 824 for training and 275 for testing.

The ensemble model identified fresh and old VCFs, reaching an AUC of 0.80 (95% confidence interval [CI], 0.77–0.83), an accuracy of 74% (95% CI, 72–77%), a sensitivity of 80% (95% CI, 77–83%), and a specificity of 68% (95% CI, 63–72%). Lateral (AUC, 0.83) views exhibited better performance than anteroposterior views (AUC, 0.77)Comparison of 5 DL models, including InferRead DR, against 3 radiologists and WHO's Target Product Profile (TPP) triage of tests (>90sens and >70% spec) in detecting TB in patients in Bangladesh. 23,954 CXRs included.

All five AI algorithms significantly outperformed the radiologists. The areas under the receiver operating characteristic curve were 90·81% (95% CI 90·33–91·29) for qXR, 90·34% (89·81–90·87) for CAD4TB, 88·61% (88·03–89·20) for Lunit INSIGHT CXR, 84·90% (84·27–85·54) for InferRead DR, and 84·89% (84·26–85·53) for JF CXR-1. Only qXR (74·3% specificity [95% CI 73·3–74·9]) and CAD4TB (72·9% specificity [72·3–73·5]) met the TPP at 90% sensitivity. All five AI algorithms reduced the number of Xpert tests required by 50% while maintaining a sensitivity above 90%. All AI algorithms performed worse among older age groups (>60 years) and people with a history of tuberculosis