Artificial intelligence-enhanced electrocardiography for early assessment of coronavirus disease 2019 severity

Abstract Despite challenges in severity scoring systems, artificial intelligence-enhanced electrocardiography (AI-ECG) could assist in early coronavirus disease 2019 (COVID-19) severity prediction. Between March 2020 and June 2022, we enrolled 1453 COVID-19 patients (mean age: 59.7 ± 20.1 years; 54.2% male) who underwent ECGs at our emergency department before severity classification. The AI-ECG algorithm was evaluated for severity assessment during admission, compared to the Early Warning Scores (EWSs) using the area under the curve (AUC) of the receiver operating characteristic curve, precision, recall, and F1 score. During the internal and external validation, the AI algorithm demonstrated reasonable outcomes in predicting COVID-19 severity with AUCs of 0.735 (95% CI: 0.662–0.807) and 0.734 (95% CI: 0.688–0.781). Combined with EWSs, it showed reliable performance with an AUC of 0.833 (95% CI: 0.830–0.835), precision of 0.764 (95% CI: 0.757–0.771), recall of 0.747 (95% CI: 0.741–0.753), and F1 score of 0.747 (95% CI: 0.741–0.753). In Cox proportional hazards models, the AI-ECG revealed a significantly higher hazard ratio (HR, 2.019; 95% CI: 1.156–3.525, p = 0.014) for mortality, even after adjusting for relevant parameters. Therefore, application of AI-ECG has the potential to assist in early COVID-19 severity prediction, leading to improved patient management

Lotus leaf-inspired droplet-based electricity generator with low-adhesive superhydrophobicity for a wide operational droplet volume range and boosted electricity output

© 2022 Elsevier LtdAs one of the nanogenerators which exploit the potential of the water cycle, a droplet-based electricity generator (DEG) has recently been leading the research field due to its high efficiency. Hence, if the DEG's effectiveness could be extended to the microscale water cycle, such as raindrop, precipitation, fog, and dew, the application fields of DEG would be countless. While introducing a hydrophobic layer could be a solution to achieving such a wide operational droplet volume range, the hydrophobicity has been so far understood only in terms of increased electricity output of the DEG on the ground that promotes droplet sliding. Herein, we report a lotus leaf-mimicking DEG (simply, LL-DEG) with a low-adhesive superhydrophobic surface of the dielectric layer. The LL-DEG shows not only an increased electricity output with an energy conversion efficiency of 13.7%, but a wide operational droplet volume range to allow normal operation with a droplet volume down to 6 µL. For the first time, we deeply analyze how the lotus leaf-mimicking surface can increase the electricity output of DEG and derive the average rate of droplet contact area change over time by introducing a new parameter, which affects the electricity output. Furthermore, how the lotus leaf-mimicking surface expands the operational droplet volume range is systematically discussed from both the investigations of quasi-static and dynamic states of droplet wetting. The superiority of LL-DEG is confirmed from the demonstration in a rainfall environment including raindrop energy harvesting and self-cleaning property, which is essential for practical utilization in outdoor conditions. Finally, based on the pH-sensitive electricity output, the applicability of the LL-DEG is demonstrated as a raindrop acidity alert. This work, which extends the DEG's effectiveness to the microscale water cycle, is expected to advance the practical utilization of DEG.11Nsciescopu

Datasheet1_Artificial intelligence-estimated biological heart age using a 12-lead electrocardiogram predicts mortality and cardiovascular outcomes.docx

BackgroundThere is a paucity of data on artificial intelligence-estimated biological electrocardiography (ECG) heart age (AI ECG-heart age) for predicting cardiovascular outcomes, distinct from the chronological age (CA). We developed a deep learning-based algorithm to estimate the AI ECG-heart age using standard 12-lead ECGs and evaluated whether it predicted mortality and cardiovascular outcomes.MethodsWe trained and validated a deep neural network using the raw ECG digital data from 425,051 12-lead ECGs acquired between January 2006 and December 2021. The network performed a holdout test using a separate set of 97,058 ECGs. The deep neural network was trained to estimate the AI ECG-heart age [mean absolute error, 5.8 ± 3.9 years; R-squared, 0.7 (r = 0.84, p FindingsIn the Cox proportional hazards models, after adjusting for relevant comorbidity factors, the patients with an AI ECG-heart age of 6 years older than the CA had higher all-cause mortality (hazard ratio (HR) 1.60 [1.42–1.79]) and more major adverse cardiovascular events (MACEs) [HR: 1.91 (1.66–2.21)], whereas those under 6 years had an inverse relationship (HR: 0.82 [0.75–0.91] for all-cause mortality; HR: 0.78 [0.68–0.89] for MACEs). Additionally, the analysis of ECG features showed notable alterations in the PR interval, QRS duration, QT interval and corrected QT Interval (QTc) as the AI ECG-heart age increased.ConclusionBiological heart age estimated by AI had a significant impact on mortality and MACEs, suggesting that the AI ECG-heart age facilitates primary prevention and health care for cardiovascular outcomes.</p