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

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.

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Journal
PLOS Global Public Health
Published
2025-06-18
DOI
https://doi.org/10.1371/journal.pgph.0003955
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1
Primary Topic
COVID-19 diagnosis using AI
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article
Field-Weighted Citation Impact
1.36
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article

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

Monde Muyoyeta, Mary Kagujje, Paul Somwe, Minyoi Mubita Maimbolwa et al.
1 citations
PLOS Global Public Health
COVID-19 diagnosis using AI
1.36
article

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

Monde Muyoyeta, Mary Kagujje, Paul Somwe, Minyoi Mubita Maimbolwa, Kanema Chiyenu, Mwansa Lumpa
article en
1 citations

Abstract

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.

PLOS Global Public HealthVol. 5(6)
Centre for Infectious Disease Research in Zambia (ZM)
Good health and well-being
Openalex Percentile: Top 19%
COVID-19 diagnosis using AI
1.36
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