Background: Inadequate situational awareness accounts for two-thirds of preventable complications in anesthesia. An essential tool for situational awareness in the perioperative setting is the patient monitor. However, the conventional monitor has several weaknesses. Avatar-based patient monitoring may address these shortcomings and promote situation awareness, a prerequisite for good decision making.
Objective: The spatial distribution of visual attention is a fundamental process for achieving adequate situation awareness and thus a potential quantifiable surrogate for situation awareness. Moreover, measuring visual attention with a head-mounted eye-tracker may provide insights into usage and acceptance of the new avatar-based patient monitoring modality.
Methods: This prospective eye-tracking study compared anesthesia providers' visual attention on conventional and avatar-based patient monitors during simulated critical anesthesia events. We defined visual attention, measured as fixation count and dwell time, as our primary outcome. We correlated visual attention with the potential confounders: performance in managing simulated critical anesthesia events (task performance), work experience, and profession. We used mixed linear models to analyze the results.
Results: Fifty-two teams performed 156 simulations. After a manual quality check of the eye-tracking footage, we excluded 57 simulations due to technical problems and quality issues. Participants had a median of 198 (IQR 92.5-317.5) fixations on the patient monitor with a median dwell time of 30.2 (IQR 14.9-51.3) seconds. We found no significant difference in participants' visual attention when using avatar-based patient monitoring or conventional patient monitoring. However, we found that with each percentage point of better task performance, the number of fixations decreased by about 1.39 (coefficient -1.39; 95% CI -2.44 to -0.34; P=.02), and the dwell time diminished by 0.23 seconds (coefficient -0.23; 95% CI: -0.4 to -0.06; P=.01).
Conclusions: Using eye tracking, we found no significant difference in visual attention when anesthesia providers used avatar-based monitoring or conventional patient monitoring in simulated critical anesthesia events. However, we identified visual attention in conjunction with task performance as a surrogate for situational awareness.
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http://dx.doi.org/10.2196/35642 | DOI Listing |
Sci Rep
January 2025
Department of Electrical Power, Adama Science and Technology University, Adama, 1888, Ethiopia.
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Ministry of Higher Education, Mataria Technical College, Cairo, 11718, Egypt.
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Department of Ophthalmology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, 100730, China; Key Lab of Ocular Fundus Diseases, Chinese Academy of Medical Sciences, Beijing 100730, China. Electronic address:
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View Article and Find Full Text PDFNutrients
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Department of Computer Engineering, Inje University, Gimhae 50834, Republic of Korea.
Background: Food image recognition, a crucial step in computational gastronomy, has diverse applications across nutritional platforms. Convolutional neural networks (CNNs) are widely used for this task due to their ability to capture hierarchical features. However, they struggle with long-range dependencies and global feature extraction, which are vital in distinguishing visually similar foods or images where the context of the whole dish is crucial, thus necessitating transformer architecture.
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