ABSTRACT
Artificial intelligence (AI) adopting deep learning technology has been widely used in the med-ical imaging domain in recent years. It realized the automatic judgment of benign and malig-nant solitary pulmonary nodules (SPNs) and even replaced the work of doctors to some extent. However, misdiagnoses can occur in certain cases. Only by determining the causes can AI play a larger role. A total of 21 Coronavirus disease 2019 (COVID-19) patients were diagnosed with SPN by CT imaging. Their Clinical data, including general condition, imaging features, AI re-ports, and outcomes were included in this retrospective study. Although they were confirmed COVID-19 by testing reverse transcription-polymerase chain reaction (RT-PCR) with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), their CT imaging data were misjudged by AI to be high-risk nodules for lung cancer. Imaging characteristics included burr sign (76.2%), lobulated sign (61.9%), pleural indentation (42.9%), smooth edges (23.8%), and cavity (14.3%). The accuracy of AI was different from that of radiologists in judging the nature of be-nign SPNs (p < 0.001, κ = 0.036 < 0.4, means the two diagnosis methods poor fit). COVID-19 patients with SPN might have been misdiagnosed using the AI system, suggesting that the AI system needs to be further optimized, especially in the event of a new disease outbreak.
ABSTRACT
In the previous stage, there were too many patients with Corona virus disease 2019 (COVID-19) in Wuhan. Ordinary people, patients, even doctors, had a great sense of desperate. On the one hand, almost all doctors participated in the treatment of patients of COVID-19. On the other hand, the government restricted residents to go out, and the sick people were also afraid of being infected with COVID-19 when seeking medical treatment. Whether cancer patients seek medical treatment or not has become a contradiction for a long time. Our Viewpoint paper is to provide a positive signal to doctors and patients that patients with in the middle or advanced stage of cancer can receive radiotherapy and/or chemotherapy normally under protective measures.
ABSTRACT
Industrial production of apparel consumes large quantity of freshwater and discharges effluents that intensify the problem of freshwater shortage and water pollution. The industrial water footprint (IWF) of a piece of apparel includes the water footprint (WF) of the fabric, apparel accessories (e.g. zipper, fastener, sewing thread) and industrial production processes. The objective of this paper is to carry out a pilot study on IWF accounting for three kinds of typical zipper (i.e. metal zipper, polyethylene terephthalate (PET) zipper and polyoxymethylene copolymer (Co-POM) zipper) that are commonly used for apparel production. The results reveal that product output exerts a remarkable influence on zipper's average IWF. Metal zipper has the largest IWF and followed by Co-POM zipper and PET zipper. Painting, dyeing and primary processing are the top three water-consuming processes and contribute about 90% of the zipper's IWF. Painting consumes the largest amount of freshwater among all processes and occupies more than 50% of the zipper's IWF. In addition, the grey water footprint (WFgrey) provides the greatest contribution, more than 80%, to the zipper's IWF. Based on these results, this paper also provides several strategies aimed at water economization and pollution reduction during industrial production of zipper.