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

Journal of Medical Case Reports

Case Report

Alastair van Heerden¹  Shannon Bosman¹  Thandanani Madonsela¹  Jens Bremerich²  Naomi Glaser³  Lutgarde Lynen⁴  Keelin Murphy⁵  Irene Ayakaka⁶  Bulemba Katende⁶  Kamele Mashaete⁶  Klaus Reither⁷  Aita Signorell⁷ 

[1] Center for Community Based Research, Human Sciences Research Council, Pietermaritzburg, South Africa;Department of Radiology, Clinic of Radiology and Nuclear Medicine, University Hospital Basel, University of Basel, Basel, Switzerland;Faculty of Medicine, University of Zürich, Zurich, Switzerland;Department of Health Sciences and Medicine, University of Lucerne, Lucerne, Switzerland;Institute of Tropical Medicine Antwerp, Antwerp, Belgium;Radboud University Medical Center, Nijmegen, The Netherlands;SolidarMed, Partnerships for Health, Maseru, Lesotho;Swiss Tropical and Public Health Institute, Allschwil, Switzerland;University of Basel, Basel, Switzerland;

关键词: Case series;    Chest X-ray;    Non-TB abnormalities;    CAD4TB;    Sub-Saharan Africa;   

DOI  :  10.1186/s13256-023-04097-4

 received in 2023-05-02, accepted in 2023-07-21,  发布年份 2023

来源: Springer

【 摘 要 】

BackgroundChest 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 presentationIn 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.ConclusionsSolely 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.

【 授权许可】

CC BY   
© BioMed Central Ltd., part of Springer Nature 2023

【 图 表 】

Fig. 1