Background: The composite physiologic index (CPI) was developed to estimate the extent of interstitial lung disease (ILD) in idiopathic pulmonary fibrosis (IPF) patients based on pulmonary function tests (PFTs). The CALIPER-revised version of the CPI (CALIPER-CPI) was also developed to estimate the volume fraction of ILD measured by CALIPER, an automated quantitative CT postprocessing software. Recently, artificial intelligence-based quantitative CT image analysis software (AIQCT), which can be used to quantify the bronchial volume separately from the ILD volume, was developed and validated in IPF.
View Article and Find Full Text PDFBackground: Interstitial lung abnormalities (ILAs) on CT may affect the clinical outcomes in patients with chronic obstructive pulmonary disease (COPD), but their quantification remains unestablished. This study examined whether artificial intelligence (AI)-based segmentation could be applied to identify ILAs using two COPD cohorts.
Methods: ILAs were diagnosed visually based on the Fleischner Society definition.
A 52-year-old man had developed hearing loss since childhood, as well as recurrent foot ulcers and osteomyelitis since his forties. He presented with gait disturbance and dysarthria that had worsened over four years and a month, respectively. Neurological exams revealed cognitive impairment, proximal weakness of the lower extremities, generalized hyperrflexia, ataxia, sensory disturbances predominant in deep sensation, urinary retention, and gait instability.
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