OBJECTIVE: To develop a system for training central venous catheterization that does not require an expert observer. We propose a training system that uses video-based workflow recognition and electromagnetic tracking to provide trainees with real-time instruction and feedback. METHODS: The system provides trainees with prompts about upcoming tasks and visual cues about workflow errors. Most tasks are recognized from a webcam video using a combination of a convolutional neural network and a recurrent neural network. We evaluated the system's ability to recognize tasks in the workflow by computing the percent of tasks that were recognized and the average signed transitional delay between the system and reviewers. We also evaluated the usability of the system using a participant questionnaire. RESULTS: The system was able to recognize 86.2% of tasks in the workflow. The average signed transitional delay was -0.7s. The average usability score on the questionnaire was 4.7 out of 5 for the system overall. The participants found the interactive task list to be the most useful component of the system with an average score of 4.8 out of 5. CONCLUSION: Overall, the participants' response to the system was positive. Participants perceived that the system would be useful for central venous catheterization training. Our system provides trainees with meaningful instruction and feedback without needing an expert observer to be present. SIGNIFICANCE: We are able to provide trainees with more opportunities to access instruction and meaningful feedback by using workflow recognition.
OBJECTIVE: To develop a system for training central venous catheterization that does not require an expert observer. We propose a training system that uses video-based workflow recognition and electromagnetic tracking to provide trainees with real-time instruction and feedback. METHODS: The system provides trainees with prompts about upcoming tasks and visual cues about workflow errors. Most tasks are recognized from a webcam video using a combination of a convolutional neural network and a recurrent neural network. We evaluated the system's ability to recognize tasks in the workflow by computing the percent of tasks that were recognized and the average signed transitional delay between the system and reviewers. We also evaluated the usability of the system using a participant questionnaire. RESULTS: The system was able to recognize 86.2% of tasks in the workflow. The average signed transitional delay was -0.7s. The average usability score on the questionnaire was 4.7 out of 5 for the system overall. The participants found the interactive task list to be the most useful component of the system with an average score of 4.8 out of 5. CONCLUSION: Overall, the participants' response to the system was positive. Participants perceived that the system would be useful for central venous catheterization training. Our system provides trainees with meaningful instruction and feedback without needing an expert observer to be present. SIGNIFICANCE: We are able to provide trainees with more opportunities to access instruction and meaningful feedback by using workflow recognition.
Authors: Hitinder S Gurm; Jorge Sanz-Guerrero; Daniel D Johnson; Andrea Jensen; Milan Seth; Stanley J Chetcuti; Thomas Lalonde; Adam Greenbaum; Simon R Dixon; Albert Shih Journal: Catheter Cardiovasc Interv Date: 2015-10-22 Impact factor: 2.692
Authors: Andru Putra Twinanda; Gaurav Yengera; Didier Mutter; Jacques Marescaux; Nicolas Padoy Journal: IEEE Trans Med Imaging Date: 2018-10-25 Impact factor: 10.048