Thin-section CT (TSCT) is currently the most sensitive imaging modality for detecting bronchiectasis. However, conventional TSCT or HRCT may overlook subtle lung involvement such as alveolar and interstitial changes. Artificial Intelligence (AI)-based analysis offers the potential to identify novel information on lung parenchymal involvement that is not easily detectable with traditional imaging techniques.
View Article and Find Full Text PDF: Pulmonary tuberculosis (TB) remains a major public health issue in India, with high incidence and mortality. The current literature on post-TB sequelae functional defects focuses heavily on spirometry, with conflicting obstruction vs. restriction data, lacks advanced statistical analysis, and has insufficient data on diffusion limitation and functional impairment.
View Article and Find Full Text PDFIntroduction: Major methodological issues with the existing algorithm (WBreath) used for the analysis of speed-of-sound-based infant sulfur hexafluoride (SF) multiple-breath washout (MBW) measurements lead to implausible results and complicate the comparison between different age groups and centers.
Methods: We developed OASIS-a novel algorithm to analyze speed-of-sound-based infant SF MBW measurements. This algorithm uses known context of the measurements to replace the dependence of WBreath on model input parameters.
Int J Tuberc Lung Dis
June 2024