Aurangzeb Brekhna, Robert Dennis, Baard Cynthia, Qureshi Abid Ali, Shaheen Aneela, Ambreen Atiqa, McFarlane David, Javed Humera, Bano Iqbal, Chiramal Justy Antony, Workman Lesley, Pillay Tanyia, Franckling-Smith Zoe, Mustafa Tehmina, Andronikou Savvas, Zar Heather J
Centre for International Health, Department of Global Public Health and Primary Care, University of Bergen, Bergen, Norway.
Clinical Research, Qure.ai Technologies Private Limited, Bangalore, India.
BMJ Open. 2025 Jul 28;15(7):e105881. doi: 10.1136/bmjopen-2025-105881.
Diagnosing pulmonary tuberculosis (PTB) in children is challenging owing to paucibacillary disease, non-specific symptoms and signs and challenges in microbiological confirmation. Chest X-ray (CXR) interpretation is fundamental for diagnosis and classifying disease as severe or non-severe. In adults with PTB, there is substantial evidence showing the usefulness of artificial intelligence (AI) in CXR interpretation, but very limited data exist in children.
A prospective two-stage study of children with presumed PTB in three sites (one in South Africa and two in Pakistan) will be conducted. In stage I, eligible children will be enrolled and comprehensively investigated for PTB. A CXR radiological reference standard (RRS) will be established by an expert panel of blinded radiologists. CXRs will be classified into those with findings consistent with PTB or not based on RRS. Cases will be classified as confirmed, unconfirmed or unlikely PTB according to National Institutes of Health definitions. Data from 300 confirmed and unconfirmed PTB cases and 250 unlikely PTB cases will be collected. An AI-CXR algorithm (qXR) will be used to process CXRs. The primary endpoint will be sensitivity and specificity of AI to detect confirmed and unconfirmed PTB cases (composite reference standard); a secondary endpoint will be evaluated for confirmed PTB cases (microbiological reference standard). In stage II, a multi-reader multi-case study using a cross-over design will be conducted with 16 readers and 350 CXRs to assess the usefulness of AI-assisted CXR interpretation for readers (clinicians and radiologists). The primary endpoint will be the difference in the area under the receiver operating characteristic curve of readers with and without AI assistance in correctly classifying CXRs as per RRS.
The study has been approved by a local institutional ethics committee at each site. Results will be published in academic journals and presented at conferences. Data will be made available as an open-source database.
PACTR202502517486411.
由于儿童肺结核(PTB)菌量少、症状和体征不具特异性以及微生物学确诊存在挑战,因此儿童PTB的诊断颇具难度。胸部X线(CXR)解读对于诊断以及将疾病分类为严重或非严重至关重要。在成人PTB患者中,有大量证据表明人工智能(AI)在CXR解读中有用,但儿童方面的数据非常有限。
将在三个地点(南非一个,巴基斯坦两个)对疑似PTB儿童进行一项前瞻性两阶段研究。在第一阶段,符合条件的儿童将被纳入并接受PTB的全面调查。由 blinded放射科医生专家小组建立CXR放射学参考标准(RRS)。根据RRS,CXR将被分类为具有与PTB一致的表现或不具有与PTB一致的表现。根据美国国立卫生研究院的定义,病例将被分类为确诊、未确诊或不太可能为PTB。将收集300例确诊和未确诊PTB病例以及250例不太可能为PTB病例的数据。将使用AI - CXR算法(qXR)处理CXR。主要终点将是AI检测确诊和未确诊PTB病例(综合参考标准)的敏感性和特异性;次要终点将针对确诊PTB病例(微生物学参考标准)进行评估。在第二阶段,将对16名读者和350张CXR进行一项采用交叉设计的多读者多病例研究,以评估AI辅助CXR解读对读者(临床医生和放射科医生)的有用性。主要终点将是在根据RRS将CXR正确分类时,有AI辅助和无AI辅助的读者的受试者操作特征曲线下面积的差异。
该研究已获得每个地点的当地机构伦理委员会的批准。结果将发表在学术期刊上并在会议上展示。数据将作为开源数据库提供。
PACTR202502517486411。