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

The study was conducted to determine the immunisation status of children under 2 years of age, assess the determinants of non-immunisation and evaluate the association between immunisation status and various sociodemographic factors.

A cross-sectional questionnaire-based study was conducted at the outpatient department of Khyber Teaching Hospital, Peshawar, over a period of 4 months. A convenience sampling technique was used and the sample size was calculated using the WHO sample size formula, resulting in n=363. A semistructured questionnaire was used to measure the primary outcome, which was the immunisation status of children under 2 years of age. Immunisation data were verified through vaccination cards to ensure accuracy and minimise recall bias. The questionnaire also collected information on sociodemographic factors, including maternal education, place of residence and household income. Statistical analysis was done at the end of the study using SPSS (V.25).

According to the vaccination card, it was found that 43.3% of the children were fully immunised, 32.5% of the children were partially immunised and the percentage of unimmunised children was 24.2.

We found that the immunisation status of children was not satisfactory. Sustained efforts are required to achieve universal coverage of immunisation. Significant interventions are required, especially in areas that are more rural and less educated.