AI-CAD enhances pulmonary TB detection and yield in active case finding.

IJTLD open Pub Date : 2025-10-10 eCollection Date: 2025-10-01 DOI:10.5588/ijtldopen.25.0088

A Frederick, R Kubendiran, T Neelagandan, K K Shankar, A Ojha, R Pant, S Pardeshi, T Gupte, A Kharat

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Abstract

Background: India accounts for 27% of global TB incidence and bears the highest TB burden worldwide. This study evaluates the performance of an AI-assisted computer-aided detection (AI-CAD) solution in a community-based, active case-finding TB screening programme conducted in Tamil Nadu, India. It also provides a comparative analysis of AI-assisted screening and conventional screening methods.

Methods: Community-based TB screening was carried out using mobile diagnostic units equipped with digital X-ray machines. The performance of the AI-CAD solution was evaluated by calculating area under the receiver operating characteristic (AUROC) curve, sensitivity, and specificity. Additionally, data from five districts that used conventional screening methods were analysed for comparative analysis against AI-assisted screening.

Results: AI-CAD exceeded the World Health Organization (WHO)-recommended minimum target product profile (TPP) with a sensitivity of 0.93 (95% confidence interval [CI]: 0.88, 0.97) and a specificity of 0.83 (95% CI: 0.82, 0.83). AI interpretation was significantly associated with positive TB diagnosis (odds ratio: 58.95, P < 0.0001). AI-assisted screening led to a 2.09-fold increase in TB diagnoses (P < 0.05) and a 2.86-fold higher sputum positivity rate (P < 0.05) compared with the conventional screening approach.

Conclusion: The AI-CAD met and exceeded the WHO's minimal TPP for TB detection. The higher sputum-positive yield reinforces AI-CAD's potential in large-scale TB screening programmes.

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AI-CAD提高了肺结核的检出率和主动病例发现率。

背景：印度占全球结核病发病率的27%，是世界上结核病负担最高的国家。本研究评估了人工智能辅助计算机辅助检测（AI-CAD）解决方案在印度泰米尔纳德邦开展的以社区为基础的主动病例发现结核病筛查规划中的表现。它还提供了人工智能辅助筛查和常规筛查方法的比较分析。方法：采用配备数字x光机的移动诊断单元开展社区结核病筛查。通过计算受试者工作特征（AUROC）曲线下的面积、灵敏度和特异性来评估AI-CAD解决方案的性能。此外，还分析了使用传统筛查方法的五个地区的数据，以便与人工智能辅助筛查进行比较分析。结果：AI-CAD超过了世界卫生组织（WHO）推荐的最低目标产品谱（TPP），灵敏度为0.93(95%可信区间[CI]: 0.88, 0.97)，特异性为0.83 （95% CI: 0.82, 0.83）。AI解释与结核阳性诊断显著相关（优势比：58.95,P < 0.0001）。与常规筛查方法相比，人工智能辅助筛查导致结核病诊断率提高2.09倍（P < 0.05），痰阳性率提高2.86倍（P < 0.05）。结论：AI-CAD达到并超过了WHO对结核检测的最低TPP标准。较高的痰阳性率增强了AI-CAD在大规模结核病筛查规划中的潜力。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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IJTLD open

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