Objective: To assess whether computed tomography (CT) conversion across different scan parameters and manufacturers using a routable generative adversarial network (RouteGAN) can improve the accuracy and variability in quantifying interstitial lung disease (ILD) using a deep learning-based automated software. Materials and Methods: This study included patients with ILD who underwent thin-section CT. Unmatched CT images obtained using scanners from four manufacturers (vendors A-D), standard- or low-radiation doses, and sharp or medium kernels were classified into groups 1–7 according to acquisition conditions. CT images in groups 2–7 were converted into the target CT sty le (Group 1: vendor A, standard dose, and sharp kernel) using a RouteGAN. ILD was quantified on original and converted CT images using a deep learning-based software (Aview, Coreline Soft). The accuracy of quantification was analyzed using the dice similarity coefficient (DSC) and pixel-wise overlap accuracy metrics against manual quantification by a radiologist. Five radiologists evaluated quantification accuracy using a 10-point visual scoring system. Results: Three hundred and fifty CT slices from 150 patients (mean age: 67.6 ± 10.7 years; 56 females) were included. The overlap accuracies for quantifying total abnormalities in groups 2–7 improved after CT conversion (original vs. converted: 0.63vs. 0.68 for DSC, 0.66 vs. 0.70 for pixel-wise recall, and 0.68 vs. 0.73 for pixel-wise precision; P < 0.002 for all). The DSCs of fibrosis score, honeycombing, and reticulation significantly increased after CT conversion (0.32 vs. 0.64, 0.19 vs. 0.47, and 0.23 vs. 0.54, P < 0.002 for all), whereas those of ground-glass opacity, consolidation, and emphysema did not change significantly or decreased slightly. The radiologists’ scores were significantly higher (P < 0.001) and less variable on converted CT. Conclusion CT conversion using a RouteGAN can improve the accuracy and variability of CT images obtained using different scan parameters and manufacturers in deep learning-based quantification of ILD.

BACKGROUND: Mounting evidence implicates an association between ambient air pollution and impaired reproductive potential of human. Our study aimed to assess the association between air pollution and ovarian reserve in young, infertile women. METHODS: Our study included 2276 Korean women who attended a single fertility center in 2016-2018. Women's exposure to air pollution was assessed using concentrations of particulate matter (PM RESULTS: The mean age was 36.6 ± 4.2 years and AMH level was 3.3 ± 3.1 ng/mL in the study population. Average AMH ratio was 0.8 ± 0.7 and low AMH was observed in 10.3% of women (n=235). The average concentration of six air pollutants was not different between the normal ovarian reserve and low AMH groups for all averaging periods. In multivariable models, an interquartile range (IQR)-increase in 1 month-average PM CONCLUSIONS In a cohort of infertile Korean women, there was a suggestive evidence of the negative association between ambient PM concentration and ovarian reserve, highlighting the potential adverse impact of air pollution on women's fertility.
Adult ; Air Pollutants/adverse effects* ; Air Pollution/adverse effects* ; Environmental Exposure/adverse effects* ; Female ; Humans ; Infertility, Female/etiology* ; Ovarian Reserve/physiology* ; Republic of Korea