How the First Year of the COVID-19 Pandemic Impacted Patients’ Hospital Admission and Care in the Vascular Surgery Divisions of the Southern Regions of the Italian Peninsula

Background: To investigate the effects of the COVID-19 lockdowns on the vasculopathic population. Methods: The Divisions of Vascular Surgery of the southern Italian peninsula joined this multicenter retrospective study. Each received a 13-point questionnaire investigating the hospitalization rate of vascular patients in the first 11 months of the COVID-19 pandemic and in the preceding 11 months. Results: 27 out of 29 Centers were enrolled. April-December 2020 (7092 patients) vs. 2019 (9161 patients): post-EVAR surveillance, hospitalization for Rutherford category 3 peripheral arterial disease, and asymptomatic carotid stenosis revascularization significantly decreased (1484 (16.2%) vs. 1014 (14.3%), p = 0.0009; 1401 (15.29%) vs. 959 (13.52%), p = 0.0006; and 1558 (17.01%) vs. 934 (13.17%), p < 0.0001, respectively), while admissions for revascularization or major amputations for chronic limb-threatening ischemia and urgent revascularization for symptomatic carotid stenosis significantly increased (1204 (16.98%) vs. 1245 (13.59%), p < 0.0001; 355 (5.01%) vs. 358 (3.91%), p = 0.0007; and 153 (2.16%) vs. 140 (1.53%), p = 0.0009, respectively). Conclusions: The suspension of elective procedures during the COVID-19 pandemic caused a significant reduction in post-EVAR surveillance, and in the hospitalization of asymptomatic carotid stenosis revascularization and Rutherford 3 peripheral arterial disease. Consequentially, we observed a significant increase in admissions for urgent revascularization for symptomatic carotid stenosis, as well as for revascularization or major amputations for chronic limb-threatening ischemia

Deep Learning for Automatic Vision-Based Recognition of Industrial Surface Defects: A Survey

Automatic vision-based inspection systems have played a key role in product quality assessment for decades through the segmentation, detection, and classification of defects. Historically, machine learning frameworks, based on hand-crafted feature extraction, selection, and validation, counted on a combined approach of parameterized image processing algorithms and explicated human knowledge. The outstanding performance of deep learning (DL) for vision systems, in automatically discovering a feature representation suitable for the corresponding task, has exponentially increased the number of scientific articles and commercial products aiming at industrial quality assessment. In such a context, this article reviews more than 220 relevant articles from the related literature published until February 2023, covering the recent consolidation and advances in the field of fully-automatic DL-based surface defects inspection systems, deployed in various industrial applications. The analyzed papers have been classified according to a bi-dimensional taxonomy, that considers both the specific defect recognition task and the employed learning paradigm. The dependency on large and high-quality labeled datasets and the different neural architectures employed to achieve an overall perception of both well-visible and subtle defects, through the supervision of fine or/and coarse data annotations have been assessed. The results of our analysis highlight a growing research interest in defect representation power enrichment, especially by transferring pre-trained layers to an optimized network and by explaining the network decisions to suggest trustworthy retention or rejection of the products being evaluated