• Smart Media Journal
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• v.13 no.5
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• pp.62-71
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• 2024

This study conducted a team-based CPR simulation with 32 fourth-year emergency rescue students to determine the effectiveness of training feedback using body cameras used at emergency rescue sites, and measured awareness, training feedback effectiveness, and satisfactio+n before and after body camera feedback. , preferences and difficulties in using body camera devices were identified. Data analysis was performed using SPSS 27.0 program, including descriptive statistics, frequency analysis, paried t-test, and Wilcoxon signed rank test. As a result of the study, the perception of body camera use showed a positive change from 3.73±0.62 points to 4.45±0.54 points, and a positive satisfaction level of 3.98±0.51 was shown (p<.001). Additionally, there was a significant increase in self-check accuracy and performance score after body camera feedback (p<.001). Therefore, during team-based simulation resuscitation training, positive feedback effects in improving self-inspection ability and performance can be achieved by watching body camera videos and using self-checklists without direct feedback from the instructor.

• Journal of the Korean Society of Radiology
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• v.82 no.5
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• pp.1196-1206
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• 2021

Purpose To construct a standard dataset of contrast-enhanced CT images of liver tumors to test the performance and safety of artificial intelligence (AI)-based algorithms for clinical decision support systems (CDSSs). Materials and Methods A consensus group of medical experts in gastrointestinal radiology from four national tertiary institutions discussed the conditions to be included in a standard dataset. Seventy-five cases of hepatocellular carcinoma, 75 cases of metastasis, and 30-50 cases of benign lesions were retrieved from each institution, and the final dataset consisted of 300 cases of hepatocellular carcinoma, 300 cases of metastasis, and 183 cases of benign lesions. Only pathologically confirmed cases of hepatocellular carcinomas and metastases were enrolled. The medical experts retrieved the medical records of the patients and manually labeled the CT images. The CT images were saved as Digital Imaging and Communications in Medicine (DICOM) files. Results The medical experts in gastrointestinal radiology constructed the standard dataset of contrast-enhanced CT images for 783 cases of liver tumors. The performance and safety of the AI algorithm can be evaluated by calculating the sensitivity and specificity for detecting and characterizing the lesions. Conclusion The constructed standard dataset can be utilized for evaluating the machine-learning-based AI algorithm for CDSS.