COVID-19 screening in low resource settings using artificial intelligence for chest radiographs and point-of-care blood tests.

Artificial intelligence (AI) systems for detection of COVID-19 using chest X-Ray (CXR) imaging and point-of-care blood tests were applied to data from four low resource African settings. The performance of these systems to detect COVID-19 using various input data was analysed and compared with antigen-based rapid diagnostic tests. Participants were tested using the gold standard of RT-PCR test (nasopharyngeal swab) to determine whether they were infected with SARS-CoV-2. A total of 3737 (260 RT-PCR positive) participants were included. In our cohort, AI for CXR images was a poor predictor of COVID-19 (AUC = 0.60), since the majority of positive cases had mild symptoms and no visible pneumonia in the lungs. AI systems using differential white blood cell counts (WBC), or a combination of WBC and C-Reactive Protein (CRP) both achieved an AUC of 0.74 with a suggested optimal cut-off point at 83% sensitivity and 63% specificity. The antigen-RDT tests in this trial obtained 65% sensitivity at 98% specificity. This study is the first to validate AI tools for COVID-19 detection in an African setting. It demonstrates that screening for COVID-19 using AI with point-of-care blood tests is feasible and can operate at a higher sensitivity level than antigen testing.

Incidental radiological findings during clinical tuberculosis screening in Lesotho and South Africa: a case series.

BACKGROUND: Chest X-ray offers high sensitivity and acceptable specificity as a tuberculosis screening tool, but in areas with a high burden of tuberculosis, there is often a lack of radiological expertise to interpret chest X-ray. Computer-aided detection systems based on artificial intelligence are therefore increasingly used to screen for tuberculosis-related abnormalities on digital chest radiographies. The CAD4TB software has previously been shown to demonstrate high sensitivity for chest X-ray tuberculosis-related abnormalities, but it is not yet calibrated for the detection of non-tuberculosis abnormalities. When screening for tuberculosis, users of computer-aided detection need to be aware that other chest pathologies are likely to be as prevalent as, or more prevalent than, active tuberculosis. However, non--tuberculosis chest X-ray abnormalities detected during chest X-ray screening for tuberculosis remain poorly characterized in the sub-Saharan African setting, with only minimal literature. CASE PRESENTATION: In this case series, we report on four cases with non-tuberculosis abnormalities detected on CXR in TB TRIAGE + ACCURACY (ClinicalTrials.gov Identifier: NCT04666311), a study in adult presumptive tuberculosis cases at health facilities in Lesotho and South Africa to determine the diagnostic accuracy of two potential tuberculosis triage tests: computer-aided detection (CAD4TB v7, Delft, the Netherlands) and C-reactive protein (Alere Afinion, USA). The four Black African participants presented with the following chest X-ray abnormalities: a 59-year-old woman with pulmonary arteriovenous malformation, a 28-year-old man with pneumothorax, a 20-year-old man with massive bronchiectasis, and a 47-year-old woman with aspergilloma. CONCLUSIONS: Solely using chest X-ray computer-aided detection systems based on artificial intelligence as a tuberculosis screening strategy in sub-Saharan Africa comes with benefits, but also risks. Due to the limitation of CAD4TB for non-tuberculosis-abnormality identification, the computer-aided detection software may miss significant chest X-ray abnormalities that require treatment, as exemplified in our four cases. Increased data collection, characterization of non-tuberculosis anomalies and research on the implications of these diseases for individuals and health systems in sub-Saharan Africa is needed to help improve existing artificial intelligence software programs and their use in countries with high tuberculosis burden.

Feasibility of implementing the advanced HIV disease care package as part of community-based HIV/TB activities: a mixed-methods study protocol.

INTRODUCTION: Although the advanced HIV disease (AHD) care package reduces morbidity and mortality in people with AHD (defined in people living with HIV as WHO stage 3 or 4, CD4 count <200 cells/µL or age <5 years), it is barely implemented in many countries. A novel point-of-care CD4 test rapidly identifies AHD. We evaluate the feasibility of implementing the AHD care package as part of community-based HIV/tuberculosis services. METHODS AND ANALYSIS: This two-phased study is guided by the Medical Research Council framework for evaluation of complex interventions. Stage 1 is a stakeholder consultation to define tools and indicators to assess feasibility of the AHD care package. Stage 2 is the implementation of the AHD care package during a facility-based tuberculosis diagnostic accuracy study in high-burden HIV/tuberculosis settings. Consenting adults with tuberculosis symptoms in two sites in Lesotho and South Africa are eligible for inclusion. HIV-positive participants are included in the feasibility study and are offered a CD4 test, a tuberculosis-lipoarabinomannan assay and those with CD4 count of ≤200 cells/µL a cryptococcal antigen lateral flow assay. Participants are referred for clinical management following national guidelines. The evaluation includes group discussions, participant observation (qualitative strand) and a semistructured questionnaire to assess acceptability among implementers. The quantitative strand also evaluates process compliance (process rating and process cascade) and early outcomes (vital and treatment status after twelve weeks). Thematic content analysis, descriptive statistics and data triangulation will be performed. ETHICS AND DISSEMINATION: The National Health Research and Ethics Committee, Lesotho, the Human Sciences Research Council Research Ethics Committee and Provincial Department of Health, South Africa and the Ethikkommission Nordwest- und Zentralschweiz, Switzerland, approved the protocol. Dissemination will happen locally and internationally at scientific conferences and in peer-reviewed journals. TRIAL REGISTRATION NUMBER: NCT04666311.