Introduction Lymphobronchial tuberculosis (TB) is common in children with primary TB and enlarged lymph nodes can cause airway compression of the large airways. If not treated correctly, airway compression can result in persistent and permanent parenchymal pathology, as well as irreversible lung destruction. Bronchoscopy was originally used to collect diagnostic samples; however, its role has evolved, and it is now used as an interventional tool in the diagnosis and management of complicated airway disease. Endoscopic treatment guidelines for children with TB are scarce. Areas covered The role of interventional bronchoscopy in the diagnosis and management of complicated pulmonary TB will be discussed. This review will provide practical insights into how and when to perform interventional procedures in children with complicated TB for both diagnostic and therapeutic purposes. This discussion incorporates current scientific evidence and refers to adult literature, as some of the interventions have only been done in adults but may have a role in children. Limitations and future perspectives will be examined. Expert opinion Pediatric pulmonary TB lends itself to endoscopic interventions as it is a disease with a good outcome if treated correctly. However, interventions must be limited to safeguard the parenchyma and prevent permanent damage.

Background: Artificial intelligence (AI) is transforming medical imaging, with large language models such as ChatGPT-4 emerging as potential tools for automated image interpretation. While AI-driven radiomics has shown promise in diagnostic imaging, the efficacy of ChatGPT-4 in liver ultrasound analysis remains largely unexamined. Objective: This study aimed to evaluate the capability of ChatGPT-4 in liver ultrasound radiomics, specifically its ability to differentiate fibrosis, steatosis, and normal liver tissue, compared with conventional image analysis software. Methods: Seventy grayscale ultrasound images from a preclinical liver disease model, including fibrosis (n=31), fatty liver (n=18), and normal liver (n=21), were analyzed. ChatGPT-4 extracted texture features, which were compared with those obtained using interactive data language (IDL), a traditional image analysis software. One-way ANOVA was used to identify statistically significant features differentiating liver conditions, and logistic regression models were used to assess diagnostic performance. Results: ChatGPT-4 extracted 9 key textural features—echo intensity, heterogeneity, skewness, kurtosis, contrast, homogeneity, dissimilarity, angular second momentum, and entropy—all of which significantly differed across liver conditions (P<.05). Among individual features, echo intensity achieved the highest F1-score (0.85). When combined, ChatGPT-4 attained 76% accuracy and 83% sensitivity in classifying liver disease. Receiver operating characteristic analysis demonstrated strong discriminatory performance, with area under the curve values of 0.75 for fibrosis, 0.87 for normal liver, and 0.97 for steatosis. Compared with IDL image analysis software, ChatGPT-4 exhibited slightly lower sensitivity (0.83 vs 0.89) but showed moderate correlation (r=0.68, P<.001) with IDL-derived features. However, it significantly outperformed IDL in processing efficiency, reducing analysis time by 40%, and highlighting its potential for high throughput radiomic analysis. Conclusions: Despite slightly lower sensitivity than IDL, ChatGPT-4 demonstrated high feasibility for ultrasound radiomics, offering faster processing, high-throughput analysis, and automated multi-image evaluation. These findings support its potential integration into AI-driven imaging workflows, with further refinements needed to enhance feature reproducibility and diagnostic accuracy.

Objective: Magnetic resonance imaging (MRI) can accurately quantify liver iron concentration (LIC), eliminating the need for an invasive liver biopsy. Currently, the most widely used relaxometry methods for iron quantification are R2 and R2*, which are based on T2 and T2* acquisition sequences, respectively. We compared the rate of change of LIC as measured by the R2-based, FDA-approved commercially available third-party software with the rate of change of LIC measured by in-house analysis using R2*-relaxometry-based MR imaging in patients undergoing follow-up MRI scans for liver iron estimation. Methods: We retrospectively included patients who had undergone serial MRIs for liver iron estimation. The MR studies were performed on a 1.5T scanner; standard multi-slice, multi-echo T2- and T2*-based sequences were acquired, and LIC was estimated. The comparison between the rate of change of LIC by R2 and R2* values was performed via correlation coefficients and Bland–Altman difference plots. Results: One hundred and eighty-nine MR abdomen studies for liver iron evaluation from 81 patients (male: 38; female: 43) were included in the study. Fifty-nine patients had two serial scans, eighteen patients had three serial scans, three patients had four serial scans, and one patient had five serial scans. The average time interval between the first and last scans for each patient was 13.3 months. The average rates of change of LIC via R2 and R2* methods were −0.0043 ± 0.0214 and −0.0047 ± 0.012 mg/g per month, respectively. There was no significant difference in the rate of change of LIC observed between the two methods. Linearity between the rate of change of LIC measured by R2 (LIC R2) and R2* (LIC R2*) was strong, showing a correlation coefficient of r = 0.72, p < 0.01. A Bland–Altman plot between the rate of change of the two methods showed that the majority of the plotted variables were between two standard deviations. Conclusion: There was no significant difference in the rate of change of LIC detected between the R2 method and the R2* method that uses a gradient echo (GRE) sequence acquired with breath-hold. Since R2* is relatively faster and less prone to motion artifacts, R2*-derived LIC is recommended for iron homeostasis follow-up in patients with liver iron overload.