Bridging the analog divide: A comparison of printed X-ray films and digital images when using computer-aided detection software for tuberculosis screening

Computer-aided detection (CAD) software provides scalable, standardized
chest X-ray (CXR) interpretation, helping address the global shortage of
radiologists and inter-reader variability. Printed X-ray films remain
common in many low-resource settings, yet most CAD software can only
process Digital Imaging and Communications in Medicine (DICOM)
files. Genki software (DeepTek, India) is one of the few World
Health Organization (WHO)-recommended CAD software capable of interpreting
both DICOM files and photographs of printed X-ray films (Joint
Photographic Experts Group [JPEG] files), but its performance using JPEG
files has not been independently evaluated. We evaluated Genki software
using a test library of 1,466 CXR images of adults collected during mobile
CXR screening for tuberculosis (TB) in Ho Chi Minh City, Viet Nam. Each
test library participant’s TB status was determined using a composite
reference standard, based on radiological findings and Xpert MTB/RIF Ultra
testing. Each CXR image was blindly re-read by 10 human readers and
processed by Genki software using both DICOM and JPEG files. Genki
software performance was evaluated using median abnormality scores, area
under the receiver operating characteristic curves (AUC), and
sensitivity/specificity comparisons at different abnormality score
thresholds. Genki software abnormality scores were significantly higher
when using JPEG files, but this did not translate into significant
differences in AUCs between the file types (DICOM AUC=0.94 vs JPEG
AUC=0.92, p=0.190). When abnormality score thresholds were calibrated to
match average human reader sensitivity (79.0%), Genki achieved
significantly higher specificity with both DICOM (95.2% vs 84.8%,
p<0.001) and JPEG (92.1% vs 94.8%, p<0.001) files. When the
software’s abnormality score thresholds were calibrated to achieve 90%
sensitivity, Genki maintained high specificity with both DICOM (89.3%) and
JPEG (81.1%) file types, exceeding the minimum Target Product Profile
(TPP) criteria for a community-based TB referral test. Genki software
performs comparably when interpreting DICOM files and photographs of
printed X-ray films, outperforming human readers and meeting TPP criteria
with both file types. This capability enhances its usability in
resource-limited settings where digital infrastructure is lacking,
supporting its broader deployment for TB screening. Further research is
needed to assess real-world implementation and performance in diverse
populations and clinical environments.