Diagnostic performance of a computer-aided system for tuberculosis screening in two Philippine cities
https://doi.org/10.47895/amp.vi0.8950
- Author:
Gabrielle P. Flores
1
,
2
;
Reiner Lorenzo J. Tamayo
2
,
3
;
Robert Neil F. Leong
2
,
4
,
5
;
Christian Sergio M. Biglaen
2
,
6
;
Kathleen Nicole T. Uy
2
,
3
,
4
;
Renee Rose O. Maglente
2
,
7
;
Marlex Jorome M. Nugui
2
;
Jason V. Alacap
2
,
4
,
8
Author Information
1. Royal Free London NHS Trust, London, United Kingdom
2. Innovations for Community Health, Inc., Mandaluyong City, Philippines
3. Department of Health Policy and Administration, College of Public Health, University of the Philippines Manila, Manila, Philippines
4. metaHealth Insights and Innovations, Inc., Malabon City, Philippines
5. Mathematics and Statistics Department, De La Salle University, Manila, Philippines
6. Department of Epidemiology and Biostatistics, College of Public Health, University of the Philippines Manila, Manila, Philippines
7. University of the Philippines Manila, Manila, Philippines
8. Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland, USA
- Publication Type:Journal Article
- MeSH:
Tuberculosis;
Diagnostic Imaging;
Deep Learning
- From:
Acta Medica Philippina
2024;58(Early Access 2024):1-8
- CountryPhilippines
- Language:English
-
Abstract:
Background and Objectives:The Philippines faces challenges in the screening of tuberculosis (TB), one of them being the shortage in the health workforce who are skilled and allowed to screen TB. Deep learning neural networks (DLNNs) have shown potential in the TB screening process utilizing chest radiographs (CXRs). However, local studies on AIbased TB screening are limited. This study evaluated qXR3.0 technology's diagnostic performance for TB screening in Filipino adults aged 15 and older. Specifically, we evaluated the specificity and sensitivity of qXR3.0 compared to radiologists' impressions and determined whether it meets the World Health Organization (WHO) standards.
Methods:A prospective cohort design was used to perform a study on comparing screening and diagnostic accuracies of qXR3.0 and two radiologist gradings in accordance with the Standards for Reporting Diagnostic Accuracy (STARD). Subjects from two clinics in Metro Manila which had qXR 3.0 seeking consultation at the time of study were invited to participate to have CXRs and sputum collected. Radiologists' and qXR3.0 readings and impressions were compared with respect to the reference standard Xpert MTB/RiF assay. Diagnostic accuracy measures were calculated.
Results:With 82 participants, qXR3.0 demonstrated 100% sensitivity and 72.7% specificity with respect to the
reference standard. There was a strong agreement between qXR3.0 and radiologists' readings as exhibited by
the 0.7895 (between qXR 3.0 and CXRs read by at least one radiologist), 0.9362 (qXR 3.0 and CXRs read by both
radiologists), and 0.9403 (qXR 3.0 and CXRs read as not suggestive of TB by at least one radiologist) concordance indices.
Conclusions:qXR3.0 demonstrated high sensitivity to identify presence of TB among patients, and meets the WHO standard of at least 70% specificity for detecting true TB infection. This shows an immense potential for the tool to supplement the shortage of radiologists for TB screening in the country. Future research directions may consider larger sample sizes to confirm these findings and explore the economic value of mainstream adoption of qXR 3.0 for TB screening.
- Full text:8950-Other-151516-1-10-20240325.pdf
