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Backgrounds/Aims: Artificial intelligence (AI) technology has been used to assess surgery quality, educate, and evaluate surgical performance using video recordings in the minimally invasive surgery era. Much attention has been paid to automating surgical workflow analysis from surgical videos for an effective evaluation to achieve the assessment and evaluation. This study aimed to design a deep learning model to automatically identify surgical phases using laparoscopic cholecystectomy videos and automatically assess the accuracy of recognizing surgical phases. Methods: One hundred and twenty cholecystectomy videos from a public dataset (Cholec80) and 40 laparoscopic cholecystectomy videos recorded between July 2022 and December 2022 at a single institution were collected. These datasets were split into training and testing datasets for the AI model at a 2:1 ratio. Test scenarios were constructed according to structural characteristics of the trained model. No pre- or post-processing of input data or inference output was performed to accurately analyze the effect of the label on model training. Results: A total of 98,234 frames were extracted from 40 cases as test data. The overall accuracy of the model was 91.2%. The most accurate phase was Calot's triangle dissection (F1 score: 0.9421), whereas the least accurate phase was clipping and cutting (F1 score: 0.7761). Conclusions: Our AI model identified phases of laparoscopic cholecystectomy with a high accuracy.
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The present study was conducted to assess the potential vector role of feedstuffs for the area spreading of avian influenza virus (AIV). Firstly, feed samples were collected from commercial poultry facilities that experienced highly pathogenic avian influenza (H5N2) in 2014−2015 for AIV testing by a real-time RT−PCR specific for the viral matrix gene. Secondly, feed materials obtained from an AIV-negative farm were spiked with various concentrations of a low pathogenic AIV H5N2. Virus-spiked cell culture media were prepared in the same manner and used for comparison. The spiked feed and media samples were tested by a multiplex real-time RT−PCR ran in a quantitative manner, either immediately or after incubation at −20, 4, 22, and 37 °C for 24, 48, and 72 h. Some of the feedstuffs collected from the poultry facilities or feed mills were positive for AIV RNA but negative by the virus isolation (VI) test, while all the formaldehyde-treated feedstuffs were PCR-negative. In the spiked feeds, the AIV titer was 1−3 logs lower than that in the corresponding media, even when tested immediately after spiking, suggesting that feed might have a negative impact on the virus or PCR detection. The half-life of AIV RNA was shorter at a higher temperature. A significant decay in the viral RNA over time was noted at 37 °C (p < 0.05), suggesting that feedstuffs should be maintained in the cold chain when testing is desired. Furthermore, the thermal degradation of AIV suggests that the heat treatment of feeds could be an alternative to chemical treatment when contamination is suspected. Collectively, the study observations indicate that AIV survivability in feed is relatively low, thus rendering it a low risk.
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The stochastic elasticity of variance model introduced by Kim et al. (Appl Stoch Models Bus Ind 30(6):753-765, 2014) is a useful model for forecasting extraordinary volatility behavior which would take place in a financial crisis and high volatility of a market could be linked to default risk of option contracts. So, it is natural to study the pricing of options with default risk under the stochastic elasticity of variance. Based on a framework with two separate scales that could minimize the number of necessary parameters for calibration but reflect the essential characteristics of the underlying asset and the firm value of the option writer, we obtain a closed form approximation formula for the option price via double Mellin transform with singular perturbation. Our formula is explicitly expressed as the Black-Scholes formula plus correction terms. The correction terms are given by the simple derivatives of the Black-Scholes solution so that the model calibration can be done very fast and effectively.