Detection of blood stains using computer vision-based algorithms and their association with postoperative outcomes in thoracoscopic lobectomies.

OBJECTIVES: Our goal was to develop high throughput computer vision (CV) algorithms to detect blood stains in thoracoscopic surgery and to determine how the detected blood stains are associated with postoperative outcomes. METHODS: Blood pixels in surgical videos were identified by CV algorithms trained with thousands of blood and non-blood pixels randomly selected and manually labelled. The proportion of blood pixels (PBP) was computed for key video frames to summarize the blood stain information during surgery. Statistical regression analyses were utilized to investigate the potential association between PBP and postoperative outcomes, including drainage volume, prolonged tube indwelling duration (≥5 days) and bleeding volume. RESULTS: A total of 275 patients undergoing thoracoscopic lobectomy were enrolled. The sum of PBP after flushing (P < 0.022), age (P = 0.005), immediate postoperative air leakage (P < 0.001), surgical duration (P = 0.001) and intraoperative bleeding volume (P = 0.033) were significantly associated with drainage volume in multivariable linear regression analysis. After adjustment using binary logistic regression analysis, the sum of the PBP after flushing [P = 0.017, odds ratio 1.003, 95% confidence interval (CI) 1.000-1.005] and immediate postoperative air leakage (P < 0.001, odds ratio 4.616, 95% CI 1.964-10.847) were independent predictors of prolonged tube indwelling duration. In the multivariable linear regression analysis, surgical duration (P < 0.001) and the sum of the PBP of the surgery (P = 0.005) were significantly correlated with intraoperative bleeding volume. CONCLUSIONS: This is the first study on the correlation between CV and postoperative outcomes in thoracoscopic surgery. CV algorithms can effectively detect from surgical videos information that has good prediction power for postoperative outcomes.

Author Correction: Near-real-time monitoring of global CO2 emissions reveals the effects of the COVID-19 pandemic.

A Correction to this paper has been published: https://doi.org/10.1038/s41467-020-20254-5.

Near-real-time monitoring of global CO2 emissions reveals the effects of the COVID-19 pandemic.

The COVID-19 pandemic is impacting human activities, and in turn energy use and carbon dioxide (CO2) emissions. Here we present daily estimates of country-level CO2 emissions for different sectors based on near-real-time activity data. The key result is an abrupt 8.8% decrease in global CO2 emissions (-1551 Mt CO2) in the first half of 2020 compared to the same period in 2019. The magnitude of this decrease is larger than during previous economic downturns or World War II. The timing of emissions decreases corresponds to lockdown measures in each country. By July 1st, the pandemic's effects on global emissions diminished as lockdown restrictions relaxed and some economic activities restarted, especially in China and several European countries, but substantial differences persist between countries, with continuing emission declines in the U.S. where coronavirus cases are still increasing substantially.