S. Wijewickrema, Yun Zhou, J. Bailey, G. Kennedy, S. O'Leary
{"title":"在虚拟现实手术模拟中提供自动分步程序指导","authors":"S. Wijewickrema, Yun Zhou, J. Bailey, G. Kennedy, S. O'Leary","doi":"10.1145/2993369.2993397","DOIUrl":null,"url":null,"abstract":"One of the roadblocks to the wide-spread use of virtual reality simulation as a surgical training platform is the need for expert supervision during training to ensure proper skill acquisition. To fully utilize the capacity of virtual reality in surgical training, it is imperative that the guidance process is automated. In this paper, we discuss a method of providing one aspect of performance guidance: advice on the steps of a surgery or procedural guidance. We manually segment the surgical trajectory of an expert surgeon into steps and present them one at a time to guide trainees through a surgical procedure. We show, using a randomized controlled trial, that this form of guidance is effective in moving trainee behavior towards an expert ideal. To support practice variation and different surgical styles adopted by experts, separate guidance templates have to be generated. To enable this, we introduce a method of automatically segmenting a surgical trajectory into steps. We propose a pre-processing step that uses domain knowledge specific to our application to reduce the solution space. We show how this can be incorporated into existing trajectory segmentation methods, as well as a greedy approach that we propose. We compare this segmentation method to existing techniques and show that it is accurate and efficient.","PeriodicalId":396801,"journal":{"name":"Proceedings of the 22nd ACM Conference on Virtual Reality Software and Technology","volume":"5 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"14","resultStr":"{\"title\":\"Provision of automated step-by-step procedural guidance in virtual reality surgery simulation\",\"authors\":\"S. Wijewickrema, Yun Zhou, J. Bailey, G. Kennedy, S. O'Leary\",\"doi\":\"10.1145/2993369.2993397\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"One of the roadblocks to the wide-spread use of virtual reality simulation as a surgical training platform is the need for expert supervision during training to ensure proper skill acquisition. To fully utilize the capacity of virtual reality in surgical training, it is imperative that the guidance process is automated. In this paper, we discuss a method of providing one aspect of performance guidance: advice on the steps of a surgery or procedural guidance. We manually segment the surgical trajectory of an expert surgeon into steps and present them one at a time to guide trainees through a surgical procedure. We show, using a randomized controlled trial, that this form of guidance is effective in moving trainee behavior towards an expert ideal. To support practice variation and different surgical styles adopted by experts, separate guidance templates have to be generated. To enable this, we introduce a method of automatically segmenting a surgical trajectory into steps. We propose a pre-processing step that uses domain knowledge specific to our application to reduce the solution space. We show how this can be incorporated into existing trajectory segmentation methods, as well as a greedy approach that we propose. We compare this segmentation method to existing techniques and show that it is accurate and efficient.\",\"PeriodicalId\":396801,\"journal\":{\"name\":\"Proceedings of the 22nd ACM Conference on Virtual Reality Software and Technology\",\"volume\":\"5 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-11-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"14\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 22nd ACM Conference on Virtual Reality Software and Technology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/2993369.2993397\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 22nd ACM Conference on Virtual Reality Software and Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2993369.2993397","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Provision of automated step-by-step procedural guidance in virtual reality surgery simulation
One of the roadblocks to the wide-spread use of virtual reality simulation as a surgical training platform is the need for expert supervision during training to ensure proper skill acquisition. To fully utilize the capacity of virtual reality in surgical training, it is imperative that the guidance process is automated. In this paper, we discuss a method of providing one aspect of performance guidance: advice on the steps of a surgery or procedural guidance. We manually segment the surgical trajectory of an expert surgeon into steps and present them one at a time to guide trainees through a surgical procedure. We show, using a randomized controlled trial, that this form of guidance is effective in moving trainee behavior towards an expert ideal. To support practice variation and different surgical styles adopted by experts, separate guidance templates have to be generated. To enable this, we introduce a method of automatically segmenting a surgical trajectory into steps. We propose a pre-processing step that uses domain knowledge specific to our application to reduce the solution space. We show how this can be incorporated into existing trajectory segmentation methods, as well as a greedy approach that we propose. We compare this segmentation method to existing techniques and show that it is accurate and efficient.