Highlights d AI system that can diagnose COVID-19 pneumonia using CT scans d Prediction of progression to critical illness d Potential to improve performance of junior radiologists to the senior level d Can assist evaluation of drug treatment effects with CT quantification

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It was recently brought to our attention that our paper was missing information regarding when the patient chest computed tomography (CT) scans were obtained and that there were some discrepancies in the clinical metadata, associated with the very large image dataset, that we made publicly available through the China National Center for Bioinformation (http://ncov-ai.big.ac.cn/ download?lang=en). All of the chest CT and clinical metadata used in our prognostic analysis were collected from patients at the time of hospital admission, and we have now added this statement to the STAR Methods section of our paper. We believe that the errors in the clinical metadata were introduced when the chest CT images, clinical metadata, and codes were transferred to the web server, and we have now corrected the errors manually. Although these corrections do not alter any of the conclusions made in the paper, we do apologize for these errors and any confusion that they may have caused.

Triboelectric nanogenerators (TENGs) show exceptional promise for converting wasted mechanical energy into electrical energy. This study investigates the use of laser-induced graphene (LIG) composites as an exciting class of triboelectric materials in TENGs. Infrared laser irradiation is used to convert the surfaces of the two carbon sources, polyimide (PI) and cork, into LIG. This gives the bilayer composite films the high conductivity associated with LIG and the triboelectric properties of the carbon source. A LIG/PI composite is used to fabricate TENGs based on conductor-to-dielectric and metal-free dielectric-to-dielectric device geometries with open-circuit voltages >3.5 kV and peak power >8 mW. Additionally, a single sheet of PI is converted to a metal-free foldable TENG. The LIG is also embedded within a PDMS matrix to form a single-electrode LIG/PDMS composite TENG. This single-electrode TENG is highly flexible and stretchable and was used to generate power from mechanical contact with skin. The LIG composites present a class of triboelectric materials that can be made from naturally occurring and synthetic carbon sources.

Common lung diseases are first diagnosed via chest X-rays. Here, we show that a fully automated deep-learning pipeline for chest-X-ray-image standardization, lesion visualization and disease diagnosis can identify viral pneumonia caused by Coronavirus disease 2019 (COVID-19), assess its severity, and discriminate it from other types of pneumonia. The deep-learning system was developed by using a heterogeneous multicentre dataset of 145,202 images, and tested retrospectively and prospectively with thousands of additional images across four patient cohorts and multiple countries. The system generalized across settings, discriminating between viral pneumonia, other types of pneumonia and absence of disease with areas under the receiver operating characteristic curve (AUCs) of 0.88–0.99, between severe and non-severe COVID-19 with an AUC of 0.87, and between severe or non-severe COVID-19 pneumonia and other viral and non-viral pneumonia with AUCs of 0.82–0.98. In an independent set of 440 chest X-rays, the system performed comparably to senior radiologists, and improved the performance of junior radiologists. Automated deep-learning systems for the assessment of pneumonia could facilitate early intervention and provide clinical-decision support.