Optimizing Computer-Aided Detection for Tuberculosis (CAD4TB) Thresholds for Community TB Screening in Nigeria: Recalibrating Threshold with Local Data
Odume Bethrand
*
Knowledge Network for disease Control and Vigilance (KNCV), Nigeria and Department of Public Health, University of Port Harcourt School of Public Health, Rivers State, Nigeria.
Inumanye N. Ojule
Department of Community Medicine, University of Port Harcourt Teaching Hospital, Rivers State, Nigeria.
Emmanuel Etim Clement
Department of Public Health, University of Port Harcourt School of Public Health, Rivers State, Nigeria.
Daniel Okon
Department of Computer Science, University of Port Harcourt, Rivers State, Nigeria.
Best Ordinioha
Department of Public Health, University of Port Harcourt School of Public Health, Rivers State, Nigeria.
Elias Aniwada
Department of Community Medicine, College of Medicine, University of Nigeria Enugu Campus, Nigeria.
*Author to whom correspondence should be addressed.
Abstract
Background: Computer-aided detection (CAD) is a promising tool for tuberculosis (TB) screening, but its performance, particularly the optimal abnormality score thresholds, is highly context-specific. This study aimed to optimise the use of computer-aided detection (CAD4TB) software in tuberculosis (TB) screening by customising its threshold settings based on individual patient characteristics and reported symptoms.
Method: The study was a retrospective observational study utilizing secondary data from community-based TB screening using portable digital X-ray (PDX) with CAD4TB AI. A total of 18,529 adults were screened across various settings, with 16,047 participants having complete results on both CAD4TB and molecular tests (GeneXpert). Most participants were from the general population (35.0%), slums (25.3%), and hard-to-reach areas (16.5%). The mean CAD4TB score was 43.13 (range: 0–99.3). CAD4TB AI findings were validated against molecular results to determine diagnostic accuracy, sensitivity, specificity; PPV, and NPV were calculated, and ROC curves were generated to assess AI score thresholds and case detection yield by age and setting.
Findings: The mean age was 43.5 ± 18.4 years; 62.6% were males. The study found a high TB prevalence of 10.9%. The performance of CAD exhibited significant variation among individuals with a prior history of tuberculosis. The optimal threshold for this subgroup was a score of 48 (sensitivity 91.0%, specificity 41.9%, AUC 0.76), compared to a score of 36 for those without prior TB (sensitivity 90.6%, specificity 39.0%, AUC 0.81). This confirms that CAD accuracy is reduced among people previously treated for TB, likely due to post-TB radiological sequelae. While no single threshold met the WHO Target Product Profile (≥90% sensitivity and ≥70% specificity) for the entire population, the study demonstrated CAD's good overall accuracy (AUC >0.80) and its value as a high-sensitivity screening tool.
Conclusion: CAD is an effective tool for community-based TB screening in Nigeria. However, its implementation requires locally validated, context-specific thresholds rather than universal cut-offs. Programmes must adopt different thresholds for key subgroups, such as those with a previous history of TB, to balance case detection with cost-effectiveness. Continuous re-evaluation of thresholds with each software update is essential to maintain optimal performance and impact.
Keywords: Computer-Aided Detection (CAD), tuberculosis screening, TB thresholds, patient profiles