3,840
Views
12
CrossRef citations to date
0
Altmetric
Review Article

Surgical process modelling strategies: which method to choose for determining workflow?

, &
Pages 91-104 | Received 25 Oct 2018, Accepted 04 Mar 2019, Published online: 27 Mar 2019

References

  • MacKenzie CL, Ibbotson JA, Cao CGL, et al. Hierarchical decomposition of laparoscopic surgery: a human factors approach to investigating the operating room environment. Minimally Invas Ther Allied Technol. 2001;10:121–127.
  • Bouarfa L, Jonker PP, Dankelman J. Discovery of high-level tasks in the operating room. J Biomed Inform. 2011;44:455–462.
  • Nomm S, Petlenkov E, Vain J, et al. Recognition of the surgeon's motions during endoscopic operation by statistics based algorithm and n. in 17th World Congress, International Federation of Automatic Control. IFAC. 2008. Seoul.
  • Forestier G, Riffaud L, Petitjean F, et al. Surgical skills: can learning curves be computed from recordings of surgical activities? Int J Comput Assist Radiol Surg. 2018;13:629–636.
  • Uemura M, Jannin P, Yamashita M, et al. Procedural surgical skill assessment in laparoscopic training environments. Int J CARS. 2016;11:543–552.
  • Nakawala H, Ferrigno G, De Momi E. Development of an intelligent surgical training system for thoracentesis. Artif Intell Med. 2018;84:50–63.
  • Riffaud L, Neumuth T, Morandi X, et al. Recording of surgical processes: a study comparing senior and junior neurosurgeons during lumbar disc herniation surgery. Neurosurgery. 2010;67:ons325–ons331.
  • Schumann S, Bühligen U, Neumuth T. Outcome quality assessment by surgical process compliance measures in laparoscopic surgery. Artif Intell Med. 2015;63:85–90.
  • Dias RD, Conboy HM, Gabany JM, et al. Development of an Interactive Dashboard to Analyze Cognitive Workload of Surgical Teams During Complex Procedural Care. in Proceedings - 2018 IEEE International Conference on Cognitive and Computational Aspects of Situation Management, CogSIMA 2018. 2018.
  • Dias RD, Conboy HM, Gabany JM, et al. Intelligent interruption management system to enhance safety and performance in complex surgical and robotic procedures. 2018, Springer Verlag: International Workshop on Computer-Assisted and Robotic Endoscopy Workshop on Clinical Image-Based Procedures International Workshop on OR 2.0 Context-Aware Operating Theaters International Workshop on Skin Image Analysis: 62-8.
  • Liebmann P, Neumuth T. Model driven design of workflow schemata for the operating room of the future. in INFORMATIK 2010 - Service Science - Neue Perspektiven fur die Informatik, Beitrage der 40. Jahrestagung der Gesellschaft fur Informatik e.V. (GI). 2010.
  • Baumgart A, Schüpfer G, Welker A, et al. Status quo and current trends of operating room management in Germany. Curr Opin Anaesthesiol. 2010;23:193–200.
  • Dexter F, Epstein RH, Traub RD, et al. Making management decisions on the day of surgery based on operating room efficiency and patient waiting times. Anesthesiology. 2004;101:1444–1453.
  • Jannin P, Morandi X. Surgical models for computer-assisted neurosurgery. Neuroimage. 2007;37:783–791.
  • Loeve AJ, Al-Issawi J, Fernandez-Gutiérrez F, et al. Workflow and intervention times of MR-guided focused ultrasound - Predicting the impact of new techniques. J Biomed Inform. 2016;60:38–48.
  • Jalote-Parmar A, Badke-Schaub P, Ali W, et al. Cognitive processes as integrative component for developing expert decision-making systems: a workflow centered framework. J Biomed Inf. 2010;43:60–74.
  • Hilgart MM, Ritterband LM, Thorndike FP, et al. Using instructional design process to improve design and development of internet interventions. J Med Internet Res. 2012;14:e89.
  • Forestier G, Petitjean F, Riffaud L, et al. Automatic matching of surgeries to predict surgeons' next actions. Artif Intell Med. 2017;81:3–11.
  • Franke S, Meixensberger J, Neumuth T. Intervention time prediction from surgical low-level tasks. J Biomed Inform. 2013;46:152–159.
  • Meeuwsen FC, van Luyn F, Blikkendaal MD, et al. Surgical phase modelling in minimal invasive surgery. Surg Endosc. 2018Springer US. 1–7. 10.1007/s00464-018-6417-4.
  • Neumuth T. Surgical process modeling. Innov Surg Sci. 2017;2:123–137.
  • Lalys F, Jannin P. Surgical process modelling: a review. Int J Comput Assist Radiol Surg. 2014;9:495–511.
  • Katić D, Julliard C, Wekerle AL, et al. LapOntoSPM: an ontology for laparoscopic surgeries and its application to surgical phase recognition. Int J CARS. 2015;10:1427–1434.
  • Burgert O, Neumuth T, Lempp F, et al. Linking top-level ontologies and surgical workflows. Int J Comput Assist Radiol Surg. 2006;1:437–438.
  • Gibaud B, Forestier G, Feldmann C, et al. Toward a standard ontology of surgical process models. Int J CARS. 2018;13:1397–1408.
  • Lo BPL, Darzi A, Yang GZ. Episode classification for the analysis of tissue/instrument interaction with multiple visual cues, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2003:230–7.
  • Meng F, D'Avolio LW, Chen AA, et al. Generating models of surgical procedures using UMLS concepts and multiple sequence alignment. AMIA … Annual Symposium proceedings/AMIA Symposium. AMIA Symposium, 2005:520–4.
  • Zappella L, Béjar B, Hager G, et al. Surgical gesture classification from video and kinematic data. Med Image Anal. 2013;17:732–745.
  • Malik R, White PS, Macewen CJ. Using human reliability analysis to detect surgical error in endoscopic DCR surgery. Clin Otolaryngol. 2003;28:456–460.
  • Neumuth T, Kaschek B, Neumuth D, et al. An observation support system with an adaptive ontology-driven user interface for the modeling of complex behaviors during surgical interventions. Behav Res Methods. 2010;42:1049–1058.
  • Neumuth T, Czygan M, Strauss G, et al. Computer assisted acquisition of surgical process models with a sensor-driven ontology. MICCAI Workshop on Modeling and Monitoring of Computer Assisted Interventions (M2CAI), London, 2009.
  • Raimbault M, Morandi X, Jannin P. Towards models of surgical procedures: analyzing a database of neurosurgical cases. In O. Ratib & S. Horii (Eds.), Medical Imaging 2005 - PACS and imaging informatics. Bellingham, WA: SPIE Press; 2005;5748:97–104. doi: 10.1117/12.594053.
  • Kranzfelder M, Zywitza D, Jell T, et al. Real-time monitoring for detection of retained surgical sponges and team motion in the surgical operation room using radio-frequency-identification (RFID) technology: a preclinical evaluation. J Surg Res. 2012;175:191–198.
  • Stauder R, Okur A, Peter L, et al. Random forests for phase detection in surgical workflow analysis, in 5th International Conference on Information Processing in Computer-Assisted Interventions, IPCAI 2014. 2014, Springer Verlag: Fukuoka. p. 148–57.
  • Bouget D, Lalys F, Jannin P. Surgical tools recognition and pupil segmentation for cataract surgical process modeling. New York, USA: Studies in Health Technology and Informatics; 2012.
  • Rockstroh M, Wittig M, Franke S, et al. Video-based detection of device interaction in the operating room. Biomed Tech (Berl). 2016;61:567–576.
  • Sznitman R, Richa R, Taylor RH, et al. Unified detection and tracking of instruments during retinal microsurgery. IEEE Trans Pattern Anal Mach Intell. 2013;35:1263–1273.
  • Glaser B, Dänzer S, Neumuth T. Intra-operative surgical instrument usage detection on a multi-sensor table. Int J CARS. 2015;10:351–362.
  • Glaser B, Schellenberg T, Franke S, et al. Surgical instrument similarity metrics and tray analysis for multi-sensor instrument identification. in Progress in Biomedical Optics and Imaging - Proceedings of SPIE. 2015.
  • Lalys F, Riffaud L, Bouget D, et al. A framework for the recognition of high-level surgical tasks from video images for cataract surgeries. IEEE Trans Biomed Eng. 2012;59:966–976.
  • Lalys F, Riffaud L, Bouget D, et al. An application-dependent framework for the recognition of high-level surgical tasks in the OR. Medical image computing and computer-assisted intervention: MICCAI … International Conference on Medical Image Computing and Computer-Assisted Intervention 2011;14(Pt 1): p. 331–8.
  • Blum T, Feußner H, Navab N. Modeling and segmentation of surgical workflow from laparoscopic video, in 13th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2010. 2010: Beijing. p. 400–7.
  • Funke I, Jenke A, Mees ST, et al. Temporal coherence-based self-supervised learning for laparoscopic workflow analysis, in 1st International Workshop on OR 2.0 Context-Aware Operating Theaters, OR 2.0 2018, 5th International Workshop on Computer Assisted Robotic Endoscopy, CARE 2018, 7th International Workshop on Clinical Image-Based Procedures, CLIP 2018, and 1st International Workshop on Skin Image Analysis, ISIC 2018, held in conjunction with the 21st International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2018, A. Malpani, et al., Editors. 2018, Springer Verlag. p. 85–93.
  • Chae YS, Lee SH, Lee HK, et al. 6DOF optical tracking system using afocal optics for image guided surgery. in International Symposium of Optomechatronics Technology, ISOT 2015. 2015. EDP Sciences.
  • Zhang H, Banovac F, Lin R, et al. Electromagnetic tracking for abdominal interventions in computer aided surgery. Comput Aided Surg. 2006;11:127–136.
  • Xiao Y, Hu P, Hu H, et al. An algorithm for processing vital sign monitoring data to remotely identify operating room occupancy in real-time. Anesth Analg. 2005;101:823–829.
  • Takashi Suzuki KY, Tamura M, Muragaki Y, et al. Video Information Management System for Information Guided Neurosurgery. Computer Aided Surgery: 7th Asian Conference on Computer Aided Surgery, Bangkok, Thailand, August 2011, Proceedings 2012:75–82.
  • Ahmadi SA, Padoy N, Rybachuk K, et al. Motif Discovery in OR Sensor Data with Application to Surgical Workflow Analysis and Activity Detection. M2CAI workshop, Med Image Comput Comput Assist Interv, 2009.
  • Jalote-Parmar A, Badke-Schaub P. Workflow Integration Matrix: a framework to support the development of surgical information systems. Design Studies. 2008;29:338–368.
  • Zhang X, Du Y, Qi L, et al. Repairing process models containing choice structures via logic petri nets. IEEE Access. 2018.
  • Neumuth T, Pretschner A, Trantakis C, Fischer M, Lemke HU, Burgert O. An Approach to XML-based Description of Intraoperative Surgical Workflows. In Berliner XML-Tage 2005, R. Eckstein, R. Tolksdorf, (Eds.), Humboldt-Univ, Berlin. 2005:147–52.
  • Barbagallo S, Corradi L, Ville De Goyet J D, et al. Optimization and planning of operating theatre activities: an original definition of pathways and process modeling. BMC Med Inform Decis Mak. 2015;15:38.
  • Qi J, Jiang Z, Zhang G, et al. A surgical management information system driven by workflow. in Service Operations and Logistics, and Informatics, 2006. SOLI'06. IEEE International Conference on. 2006. IEEE.
  • Bandt M, Kühn R, Schick S, et al. Beyond Flexibility- Workflows in the perioperative sector of the healthcare domain. Elect Comm EASST. 2011;37.
  • Blum T, Padoy N, Feußner H, Navab N. Workflow mining for visualization and analysis of surgeries. Int J Comput. Assisted Radiol. Surg. 2008;3:379–386. doi:10.1007/s11548-008-0239-0
  • Neumuth T, Jannin P, Schlomberg J, et al. Analysis of surgical intervention populations using generic surgical process models. Int J CARS. 2011;6:59–71.
  • Neumuth T, Loebe F, Jannin P. Similarity metrics for surgical process models. Artif Intell Med. 2012;54:15–27.
  • van der Aalst W. Business alignment: using process mining as a tool for Delta analysis and conformance testing. Requirements Eng. 2005;10:198–211.
  • Neumuth T, Scholl MH, Mansmann S, et al. Data warehousing technology for surgical workflow analysis. in 21st IEEE International Symposium on Computer-Based Medical Systems, CBMS 2008. Jyvaskyla.