Diagnostic performance of a computer-aided system for tuberculosis screening in two Philippine cities

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.

Should IgM/IgG rapid test kit be used in the diagnosis of COVID-19?

Key Findings@# Current evidence does NOT support use of IgM/IgG rapid test kits for the definitive diagnosis of COVID-19 in currently symptomatic patients. • The present standard for diagnosis of COVID-19 is through qualitative detection of COVID-19 virus nucleic acid via reverse transcription polymerase chain reaction (RT-PCR). • Due to long turnaround times and complicated logistical operations, a rapid and simple field test alternative is needed to diagnose and screen patients. • An alternative to the direct detection and measurement of viral load (RT-PCR) is the qualitative detection of specific antibodies to COVID-19. ELISA (discussed in a separate rapid review) and lateral flow immunoassay (LFIA) IgM/IgG rapid test kits are two currently available, qualitative, antibody tests for COVID-19. • Two low quality clinical trials showed that there is insufficient evidence to support the use of IgM/IgG rapid test kits for the definitive diagnosis of COVID-19. Diagnostic accuracy varies greatly depending on the timing of the test. The test performed very poorly during the early phase of the disease (i.e., less than eight days from onset of symptoms). • Existing guidelines do not recommend serologic antibody tests for the diagnosis of COVID-19 in currently symptomatic patients.