Augmented Reality for COVID-19 Aid Diagnosis: Ct-Scan segmentation based Deep Learning

The virus new variants of Coronavirus disease 2019 (COVID-19) continue to appear, making the situation more challenging and threatening. The COVID-19 pandemic has profoundly affected health systems and medical centres worldwide. The primary clinical tools used in diagnosing patients presenting with respiratory distress and suspected COVID-19 symptoms are radiology examinations. Recently emerging artificial intelligence (AI) technologies further strengthen the power of imaging tools and help medical specialists. This paper presents an Augmented Reality (AR) tool for COVID-19 aid diagnosis, including Computerised Tomography Ct-scans segmentation based Deep Learning, 3D reconstruction, and AR visualisation. Segmentation is a critical step in AI-based COVID-19 image processing and analysis;we use the popular segmentation networks, including classic U-Net. Quantitative and qualitative evaluation showed reasonable performance of U-Net for lung and COVID-19 lesions segmentation. The AR-COVID-19 aid diagnosis system could be used for medical education professional training and as a support visualisation and reading tool for radiologist. © 2022 IEEE.

Screening for economic hardship for child welfare-involved families during the COVID-19 pandemic: A rapid partnership response.

BACKGROUND: Pandemics have a wide range of economic, health and social consequences related to both the spread of a disease and efforts made by government leaders to contain it which may be particularly detrimental for the child welfare-involved population. This is because child welfare agencies serve some of the highest needs children and families. A significant proportion of these families face economic hardship, and as a result of containment measures for COVID-19, more families inevitably will. OBJECTIVE: Given the range of negative consequences related to the pandemic and the evolving supports available to families, child protection workers needed a clinical tool to guide and support work with families informed by an understanding of economic hardship. The objective of this paper is to report on the development and implementation strategy of a tool to be used for practice intervention during the pandemic. METHODS: Action research methodology was utilized in the creation of the clinical tool. The tool's development and implementation occurred through an academic/child welfare sector partnership involving child welfare agencies representing diverse regions and populations in Ontario, Canada. Factor analysis of representative child welfare data from the Ontario Incidence Study of Reported Child Abuse and Neglect 2018 (OIS-2018) on economic hardship was used to inform the development of questions on the clinical tool. RESULTS: The development and implementation strategy of the clinical tool are described, including the results from analyses of the OIS-2018. CONCLUSIONS: Future directions for the project are discussed, including considerations for using this tool beyond the pandemic.