To evaluate the performance of a commercially available deep learning-based AI algorithm on pediatric chest X-rays (CXRs).
3,319 frontal (PA and AP) CXRs of patients’ aged 6 to 18 years were pulled from PACS and anonymised at a tertiary care pediatric hospital in Brazil. Labels (normal, abnormal) were ascertained from the radiology reports. The data was loaded on to CARPL AI Research platform (CARING Research, India) for AI inference and validation-related statistical analysis. The algorithm under test was QXR Version 3.0 (Qure.ai, India). The algorithmic output consisted of three categories – “normal”, “abnormal” and “to be read”. The “to be read” scans,
which refer to cases where the scans are meant to be read by a radiologist directly, were excluded from calculation of summary statistics. False negative scans were re-read by a specialized pediatric radiologist with 6 years of experience.
Out of the 3,319 cases, 1,802 were labeled as “to be read” and excluded from analysis. On the remaining 1,517 cases the algorithm gave a sensitivity of 91% and specificity of 96%. The 38 false negatives were reviewed and only 9 truly missed findings existed out of which 7 cases had consolidation, 1 had atelectasis and 1 had vascular engorgement.
Figure 1
Our independent evaluation provides evidence of AI’s ability to accurately read and triage normal pediatric CXRs thereby saving significant time and effort on part of radiologists.
Most AI algorithms are trained on adult data and hence have poor performance on pediatric cases where lack of trained radiologists is a constant problem, especially in the developing and underdeveloped world.
