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Evaluating InferVision’s Computer-Aided Detection (CAD) algorithm for Tuberculosis (TB) screening, Lusaka, Zambia

Dados Bibliográficos

ID19592828
AutoresPaul Somwe (0009-0002-4854-0180, Centre for Infectious Disease Research in Zambia, autor correspondente), Minyoi Maimbolwa, Minyoi M Maimbolwa (0000-0002-1770-0359, Centre for Infectious Disease Research in Zambia, autor correspondente), Kanema Chiyenu (Centre for Infectious Disease Research in Zambia, autor correspondente), Mwansa Lumpa (0009-0000-2306-6318, Centre for Infectious Disease Research in Zambia, autor correspondente), Mary Kagujje (0000-0003-4818-6548, Centre for Infectious Disease Research in Zambia, autor correspondente), Monde Muyoyeta (0000-0003-3609-5403, Centre for Infectious Disease Research in Zambia, autor correspondente)
EditoresEmily B Wong
Ano2025
Volume5
Fascículo6
Páginase0003955
Data de publicação2025-06-18
Peer ReviewedSim
Open AccessSim
TipoARTICLE
PeriódicoPLOS Global Public Health (JOURNAL)
Identificadores do periódicoISSN: 2767-3375 • E-ISSN: 2767-3375
EditoraPublic Library of Science (PLoS) (PUBLISHER)
DOI10.1371/journal.pgph.0003955
PMID40531944
OpenAlexW4411403309
IdiomaEN
Referências citadas13

The objective of this study was to evaluate the diagnostic performance of InferRead DR Chest for tuberculosis (TB) screening in a high HIV and TB burden setting. The study assessed the performance of InferRead DR Chest using anonymized chest X-ray images from an active TB case finding study in Lusaka, Zambia, for individuals aged 15 and older. The Xpert MTB/RIF or MTB culture was the composite reference standard. Performance was evaluated using the Area Under the Receiver Operating Characteristic Curve (AUC), and a binary classification point was selected where the sensitivity aligned with the WHO target product profile for TB screening tools. Of the 1,890 chest X-ray images that met the inclusion criteria, 91.5% of participants reported at least one TB symptom. The median age was 38 years (IQR: 29–47), and 1,186 (62.8%) were male. From the study sample, 449 participants (23.8%) reported a history of previous TB, and 704 (37.2%) were HIV positive. Among the analyzed images, 289 (15.3%) were classified as TB positive based on the composite reference standard test results. The overall area under the curve (AUC) was 0.81 (95% CI: 0.78–0.83). Among individuals with a history of previous TB and those who were HIV positive, the AUCs were 0.71 (95% CI: 0.63–0.79) and 0.77 (95% CI: 0.72–0.82), respectively. At a sensitivity of 90.3% (95% CI: 86.3%–93.5%), InferRead DR Chest achieved a specificity of 39.2% (95% CI: 36.8%–41.7%) at TB score cut point of 0.12. InferRead DR Chest had acceptable performance in our population. Additional training and piloting of InferRead DR Chest in this population is recommended

Algorithm · Area under the curve · Pathology · Receiver operating characteristic · Tuberculosis · COVID-19 diagnosis using AI · Infectious Diseases and Tuberculosis · Medicine · Tuberculosis Research and Epidemiology · Immunology · Internal Medicine

  • Accuracy of computer-aided chest X-ray in community-based tuberculosis screening

    Open Access•Brenda Mungai, Jane Rahedi Ong’ang’o et al.•PLOS Global Public Health•2022

  • Artificial intelligence-based computer aided detection (AI-CAD) in the fight against tuberculosis

    Open Access•Julien Onno, F A Khan et al.•Social Science & Medicine•2023

Velocidade de citaçãohistorical
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