Benedikt Mayer1, Monique Meuschke2, Jimmy Chen3, Beat P Müller-Stich4, Martin Wagner4, Bernhard Preim2, Sandy Engelhardt3. 1. Department of Simulation and Graphics, University of Magdeburg, Magdeburg, Germany. benedikt@isg.cs.uni-magdeburg.de. 2. Department of Simulation and Graphics, University of Magdeburg, Magdeburg, Germany. 3. Department of Cardiac Surgery, Group Artificial Intelligence in Cardiovascular Medicine, University of Heidelberg, Heidelberg, Germany. 4. Department of General, Visceral and Transplantation Surgery, Heidelberg University Hospital, Heidelberg, Germany.
Abstract
PURPOSE: Integrated operating rooms provide rich sources of temporal information about surgical procedures, which has led to the emergence of surgical data science. However, little emphasis has been put on interactive visualization of such temporal datasets to gain further insights. Our goal is to put heterogeneous data sequences in relation to better understand the workflows of individual procedures as well as selected subsets, e.g., with respect to different surgical phase distributions and surgical instrument usage patterns. METHODS: We developed a reusable web-based application design to analyze data derived from surgical procedure recordings. It consists of aggregated, synchronized visualizations for the original temporal data as well as for derived information, and includes tailored interaction techniques for selection and filtering. To enable reproducibility, we evaluated it across four types of surgeries from two openly available datasets (HeiCo and Cholec80). User evaluation has been conducted with twelve students and practitioners with surgical and technical background. RESULTS: The evaluation showed that the application has the complexity of an expert tool (System Usability Score of 57.73) but allowed the participants to solve various analysis tasks correctly (78.8% on average) and to come up with novel hypotheses regarding the data. CONCLUSION: The novel application supports postoperative expert-driven analysis, improving the understanding of surgical workflows and the underlying datasets. It facilitates analysis across multiple synchronized views representing information from different data sources and, thereby, advances the field of surgical data science.
PURPOSE: Integrated operating rooms provide rich sources of temporal information about surgical procedures, which has led to the emergence of surgical data science. However, little emphasis has been put on interactive visualization of such temporal datasets to gain further insights. Our goal is to put heterogeneous data sequences in relation to better understand the workflows of individual procedures as well as selected subsets, e.g., with respect to different surgical phase distributions and surgical instrument usage patterns. METHODS: We developed a reusable web-based application design to analyze data derived from surgical procedure recordings. It consists of aggregated, synchronized visualizations for the original temporal data as well as for derived information, and includes tailored interaction techniques for selection and filtering. To enable reproducibility, we evaluated it across four types of surgeries from two openly available datasets (HeiCo and Cholec80). User evaluation has been conducted with twelve students and practitioners with surgical and technical background. RESULTS: The evaluation showed that the application has the complexity of an expert tool (System Usability Score of 57.73) but allowed the participants to solve various analysis tasks correctly (78.8% on average) and to come up with novel hypotheses regarding the data. CONCLUSION: The novel application supports postoperative expert-driven analysis, improving the understanding of surgical workflows and the underlying datasets. It facilitates analysis across multiple synchronized views representing information from different data sources and, thereby, advances the field of surgical data science.
Authors: Lena Maier-Hein; Swaroop S Vedula; Stefanie Speidel; Nassir Navab; Ron Kikinis; Adrian Park; Matthias Eisenmann; Hubertus Feussner; Germain Forestier; Stamatia Giannarou; Makoto Hashizume; Darko Katic; Hannes Kenngott; Michael Kranzfelder; Anand Malpani; Keno März; Thomas Neumuth; Nicolas Padoy; Carla Pugh; Nicolai Schoch; Danail Stoyanov; Russell Taylor; Martin Wagner; Gregory D Hager; Pierre Jannin Journal: Nat Biomed Eng Date: 2017-09 Impact factor: 25.671
Authors: Carly R Garrow; Karl-Friedrich Kowalewski; Linhong Li; Martin Wagner; Mona W Schmidt; Sandy Engelhardt; Daniel A Hashimoto; Hannes G Kenngott; Sebastian Bodenstedt; Stefanie Speidel; Beat P Müller-Stich; Felix Nickel Journal: Ann Surg Date: 2021-04-01 Impact factor: 12.969