Deep learning model for the quantification of lung abnormalities in ILD patients

Lung fibrosis quantification from CT scans is prone to large inter and intra observer variability and its correlation with PFT is essential in the definition of disease progression. There is the need for a reliable and reproducible tool for abnormalities quantification. For this reason, a deep learning abnormalities quantification model was used to explore the correlation with PFT in ILD patients. The abnormalities segmentation model is based on 2D U-Net combined with Res Next as encoder and deep supervision and was trained on axial unenhanced chest CT scans of 199 COVID-19 patients and externally validated on 50 COVID-19 patients. Whole lungs were segmented using RadiomiX toolbox. Validation of the quantification performance was explored in a cohort of 20 ILD patients. The model performed the automatic segmentation of all abnormalities and calculate the ratio on the total lung volume ((abnormalities volume/whole lungs volume) * 100). This value is then correlated with the Forced Vital Capacity (FVC) and Diffusion Lung Capacity for carbon monoxide (DLCO) for each patient with Pearson correlation coefficient (rho). The deep learning segmentation algorithm achieved good performances (mean DSC 0.6 +/- 0.1) on the external test set. The percentage volume of disease region correlated with FVC and DLCO were the rho = -0.70402, -0.58133, respectively (P <. 001 for all). The developed algorithm performed similarly to radiologists for disease-extent contouring, which correlated with pulmonary function to assess CT images from patients with ILD. This automatic quantification tool could help in the prognosis and diagnosis of ILDs, based on the lung abnormalities extent.

Structural Health Monitoring for Architectural Heritage: Case Studies in Central Italy

Structural health monitoring (SHM) has been recognized as a useful tool for experimentally assessing the structural behavior of historical buildings over time. If monitoring is performed continuously and for a long time, it allows to evaluate variations in the building’s dynamic response to external factors. The main goal is to estimate the dynamic response of the monitored buildings to daily stresses produced by environmental and anthropogenic factors (variations in ambient temperature and humidity, wind velocity, vibrations produced by vehicular traffic or other anthropogenic noise sources including visitors, service staff, etc.) to distinguish ordinary fluctuations in the buildings’ response from other anomalous behavior. Continuous monitoring also makes it possible to assess the impact of extraordinary events such as extreme weather events, earthquakes, excavations, cultural events involving many people nearby the monitored buildings. Some examples from the authors’ many monitoring campaigns on monuments located in different urban environments are presented. In particular, the effect on one of the monitored buildings of the drastic reduction of seismic noise during the SarsCov2 pandemic lockdown is investigated. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.