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

## TL;DR

This study compares how well a tuberculosis detection software works with digital X-rays versus printed X-ray photos, finding it performs well in both formats.

## Contribution

The study is one of the first to independently evaluate Genki CAD software's performance using JPEG images of printed X-rays for TB screening.

## Key findings

- Genki software achieved high specificity with both DICOM and JPEG files when calibrated to match human reader sensitivity.
- The software met Target Product Profile criteria for high sensitivity and specificity using both file types.
- JPEG file performance was comparable to DICOM files in terms of AUC and diagnostic accuracy.

## Abstract

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 1466 CXR images from adults screened for tuberculosis (TB) in Ho Chi Minh City, Viet Nam. Each 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 84.8%, 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, meeting the minimum Target Product Profile (TPP) criteria for a high-sensitivity, high-specificity screening test.

Genki software performs comparably when interpreting DICOM and JPEG files, 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 feasibility and performance in diverse populations and clinical environments.

The online version contains supplementary material available at 10.1186/s44263-025-00237-8.