article · PLOS Digital Health
This study evaluated the implementation costs and cost-effectiveness of using ultraportable chest X-ray (CXR) with artificial intelligence (AI) for active tuberculosis (TB) case finding in Nigeria. Researchers screened individuals aged 15 and older for TB symptoms and offered AI-enabled CXR interpretation. Sputum tests were conducted based on symptoms or AI abnormality scores. The analysis compared five screening scenarios, calculating total costs, cost per confirmed case, and incremental cost-effectiveness ratios. Findings indicate that combining CXR and AI with symptom screening detects more TB cases at a lower cost per case compared to traditional symptom-based screening alone, proving to be a cost-effective approach.
This research provides crucial evidence for optimising tuberculosis detection strategies in resource-limited settings. By demonstrating the cost-effectiveness of integrating AI-enabled chest X-rays, it can guide public health programmes in Nigeria and similar regions to allocate resources more efficiently, leading to earlier diagnosis and improved disease control.
This research directly supports the adoption of AI-enabled ultraportable CXR technology by national and regional tuberculosis programmes and public health organisations. The findings provide a strong economic justification for investing in and implementing these tools for active case finding, particularly in settings with high TB burdens. This is applied research, with clear recommendations for near-market implementation by health authorities.
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Availability of ultraportable chest x-ray (CXR) and advancements in artificial intelligence (AI)-enabled CXR interpretation are promising developments in tuberculosis (TB) active case finding (ACF) but costing and cost-effectiveness analyses are limited. We provide implementation cost and cost-effectiveness estimates of different screening algorithms using symptoms, CXR and AI in Nigeria. People 15 years and older were screened for TB symptoms and offered a CXR with AI-enabled interpretation using qXR v3 (Qure.ai) at lung health camps. Sputum samples were tested on Xpert MTB/RIF for individuals reporting symptoms or with qXR abnormality scores ≥0.30. We conducted a retrospective costing using a combination of top-down and bottom-up approaches while utilizing itemized expense data from a health system perspective. We estimated costs in five screening scenarios: abnormality score ≥0.30 and ≥0.50; cough ≥ 2 weeks; any symptom; abnormality score ≥0.30 or any symptom. We calculated total implementation costs, cost per bacteriologically-confirmed case detected, and assessed cost-effectiveness using incremental cost-effectiveness ratio (ICER) as additional cost per additional case. Overall, 3205 people with presumptive TB were identified, 1021 were tested, and 85 people with bacteriologically-confirmed TB were detected. Abnormality ≥ 0.30 or any symptom (US$65704) had the highest costs while cough ≥ 2 weeks was the lowest (US$40740). The cost per case was US$1198 for cough ≥ 2 weeks, and lowest for any symptom (US$635). Compared to baseline strategy of cough ≥ 2 weeks, the ICER for any symptom was US$191 per additional case detected and US$ 2096 for Abnormality ≥0.30 OR any symptom algorithm. Using CXR and AI had lower cost per case detected than any symptom screening criteria when asymptomatic TB was higher than 30% of all bacteriologically-confirmed TB detected. Compared to traditional symptom screening, using CXR and AI in combination with symptoms detects more cases at lower cost per case detected and is cost-effective. TB programs should explore adoption of CXR and AI for screening in ACF.
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DOI: 10.1371/journal.pdig.0000894
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