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https://www.selleckchem.com/products/z-lehd-fmk-s7313.html Radiology services encountering the coronavirus disease-19 pandemic will need to modify their daily operational practices. • Leadership, patient risk stratification, adequate manpower, operational workflow clarity, and workplace/social responsibility will help Radiology services safely and sustainably deal with the current disease outbreak.OBJECTIVES To develop and evaluate the performance of a deep learning-based system for automatic patellar height measurements using knee radiographs. METHODS The deep learning-based algorithm was developed with a data set consisting of 1018 left knee radiographs for the prediction of patellar height parameters, specifically the Insall-Salvati index (ISI), Caton-Deschamps index (CDI), modified Caton-Deschamps index (MCDI), and Keerati index (KI). The performance and generalizability of the algorithm were tested with 200 left knee and 200 right knee radiographs, respectively. The intra-class correlation coefficient (ICC), Pearson correlation coefficient, mean absolute difference (MAD), root mean square (RMS), and Bland-Altman plots for predictions by the system were evaluated in comparison with manual measurements as the reference standard. RESULTS Compared with the reference standard, the deep learning-based algorithm showed high accuracy in predicting the ISI, CDI, and KI (left knee ICC = 0.91-0.95, r = 0.84-mance to radiologists in measuring ISI, CDI, and KI.PURPOSE To evaluate the ablation zone diameter (AZD) using six modes of corneal topography after small-incision lenticule extraction (SMILE) and femtosecond laser-assisted in situ keratomileusis (FS-LASIK) for myopia and to compare the programmed and postoperative AZDs METHODS This retrospective comparative study included 86 right eyes in 86 patients who underwent SMILE or FS-LASIK at the Shandong Eye Institute between June 2016 and August 2017. Data were collected preoperatively and at 1, 3, and 6 months postoperativ

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