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We aimed to determine the effect of optic disc tilt on deep learning-based optic disc classification. Image annotation was performed to label pathologic changes of the optic disc (normal, glaucomatous optic disc changes, disc swelling, and disc pallor) and note the appearance of a tilted optic disc (non-tilted versus tilted). Deep learning-based classification modeling was implemented to develop an optic-disc appearance classification. We acquired 2,507 fundus photographs from 2,236 subjects. Of the 2,507 data, 1,010 (40.3%) had tilted optic discs. The AUC of the models trained and tested using the non-tilted disc dataset was 0.988 ± 0.002, 0.991 ± 0.003, and 0.986 ± 0.003 for VGG16, VGG19, and DenseNet121, respectively. The AUC of the models trained and tested using the tilted disc dataset was 0.924 ± 0.046, 0.928 ± 0.017, and 0.935 ± 0.008. The model performance indicated by the AUC was better for non-tilted discs, regardless of the dataset used for training. In each pathologic change, non-tilted disc models showed better sensitivity than the tilted disc model. In the groups of glaucoma, disc pallor, and disc swelling, non-tilted disc models showed better specificity than the tilted disc model. We developed deep learning-based optic disc appearance classification systems using the fundus photographs of patients with and without tilted optic discs. The classification accuracy was lower in patients with the appearance of tilted discs compared to non-tilted discs, suggesting the need for identifying and adjusting for the effect of optic disc tilt on the optic disc classification algorithm in future development.
Objective A knowledgebase (KB) transition of a clinical decision support (CDS) system occurred at the study site. The transition was made from one commercial database to another, provided by a different vendor. The change was applied to all medications in the institute. The aim of this study was to analyze the effect of KB transition on medication-related orders and alert patterns in an emergency department (ED). Methods Data of patients, medication-related orders and alerts, and physicians in the ED from January 2018 to December 2020 were analyzed in this study. A set of definitions was set to define orders, alerts, and alert overrides. Changes in order and alert patterns before and after the conversion, which took place in May 2019, were assessed. Results Overall, 101,450 patients visited the ED, and 1,325 physicians made 829,474 prescription orders. Alert rates (alert count divided by order count) for periods A and B were 12.6% and 14.1%, and override rates (alert override count divided by alert count) were 60.8% and 67.4%, respectively. Of the 296 drugs that were used more than 100 times during each period, 64.5% of the drugs had an increase in alert rate after the transition. Changes in alert rates were tested using chi-squared test and Fisher’s exact test. Conclusion We found that the CDS KB transition was associated with a significant change in alert patterns at the medication level in the ED. Careful consideration is advised when such a transition is performed.
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