Toward a standard ontology of surgical process models

Gibaud, Bernard; Forestier, Germain; Feldmann, Carolin; Ferrigno, Giancarlo; Gonçalves, Paulo J. S.; Haidegger, Tamás; Julliard, Chantal; Katić, Darko; Kenngott, Hannes; Maier-Hein, Lena; März, Keno; Momi, Elena De; Nagy, Dénes Ákos; Nakawala, Hirenkumar; Neumann, Juliane; Neumuth, Thomas; Balderrama, Javier Rojas; Speidel, Stefanie; Wagner, Martin; Jannin, Pierre

doi:10.1007/s11548-018-1824-5

Cited by 63 publications

(44 citation statements)

References 66 publications

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“…To ensure realism of the scenario, the formal methodology used for its design started with video observations recorded in the OR. The surgical activities shown on these videos were encoded using the *Surgery Workflow Toolbox* [Annotate] software 1 based on OntoSPM, a generic ontology for the domain of surgical processes (Gibaud et al, 2018).…”

Section: Apparatusmentioning

confidence: 99%

Learning procedural skills with a virtual reality simulator: An acceptability study

Bracq

Michinov

Arnaldi

et al. 2019

Nurse Education Today

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Background: Virtual Reality (VR) simulation has recently been developed and has improved surgical training. Most VR simulators focus on learning technical skills and few on procedural skills. Studies that evaluated VR simulators focused on feasibility, reliability or easiness of use, but few of them used a specific acceptability measurement tool. Objectives: The aim of the study was to assess acceptability and usability of a new VR simulator for procedural skill training among scrub nurses, based on the Unified Theory of Acceptance and Use of Technology (UTAUT) model. Participants: The simulator training system was tested with a convenience sample of 16 nonexpert users and 13 expert scrub nurses from the neurosurgery department of a French University Hospital. Methods: The scenario was designed to train scrub nurses in the preparation of the instrumentation table for a craniotomy in the operating room (OR). Results: Acceptability of the VR simulator was demonstrated with no significant difference between expert scrub nurses and non-experts. There was no effect of age, gender or expertise. Workload, immersion and simulator sickness were also rated equally by all participants. Most participants stressed its pedagogical interest, fun and realism, but some of them also regretted its lack of visual comfort. Conclusion: This VR simulator designed to teach surgical procedures can be widely used as a tool in initial or vocational training.

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Section: Apparatusmentioning

confidence: 99%

Learning procedural skills with a virtual reality simulator: An acceptability study

Bracq

Michinov

Arnaldi

et al. 2019

Nurse Education Today

Self Cite

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“…An essential component of the control tower is the datadriven modeling and understanding of the clinical activities, an undertaking that taps into the emerging research field of Surgical Data Science [3], [4]. Machine learning has been key to generate models of procedural interventions from data [139], [140] and ontologies have also been developed to standardize the resulting models [141]. Implementations of such AI-based applications start to emerge in various institutions, besides the ones focusing on analysing endoscopic videos already mentioned in Section V. As video data remains one of the main source of information, they highly rely on deep learning.…”

Section: B An Endeavour Rooted In Surgical Data Sciencementioning

confidence: 99%

CAI4CAI: The Rise of Contextual Artificial Intelligence in Computer-Assisted Interventions

et al. 2020

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Data-driven computational approaches have evolved to enable extraction of information from medical images with a reliability, accuracy and speed which is already transforming their interpretation and exploitation in clinical practice. While similar benefits are longed for in the field of interventional imaging, this ambition is challenged by a much higher heterogeneity. Clinical workflows within interventional suites and operating theatres are extremely complex and typically rely on poorly integrated intra-operative devices, sensors, and support infrastructures. Taking stock of some of the most exciting developments in machine learning and artificial intelligence for computer assisted interventions, we highlight the crucial need to take context and human factors into account in order to address these challenges. Contextual artificial intelligence for computer assisted intervention, or CAI4CAI, arises as an emerging opportunity feeding into the broader field of surgical data science. Central challenges being addressed in CAI4CAI include how to integrate the ensemble of prior knowledge and instantaneous sensory information from experts, sensors and actuators; how to create and communicate a faithful and actionable shared representation of the surgery among a mixed human-AI actor team; how to design interventional systems and associated cognitive shared control schemes for online uncertainty-aware collaborative decision making ultimately producing more precise and reliable interventions.Index Terms-Artificial intelligence, computer assisted interventions, interventional workflow, intra-operative imaging, surgical planning, data fusion, surgical scene understanding, contextaware user interface, machine and deep learning, surgical data science

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“…Within all these domains, data collection at large scale became possible with the introduction of CIS systems, also as an enabler of Machine Learning methods [10]. This opened the case for a completely new field, dubbed Surgical Data Science [11], which really enables the assessment and benchmarking of CIS systems based on surgical process models [12].…”

Section: Computer-integrated Surgery: An Emerging Fieldmentioning

confidence: 99%

Probabilistic Method to Improve the Accuracy of Computer-Integrated Surgical Systems

2019

APH

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The technological development of the last decades resulted in the rise of entirely new paradigms in healthcare. Computer-Integrated Surgery (CIS) is providing innovative, minimally invasive solutions to heal complex injuries and diseases. It integrates robotic devices to the treatment delivery phase. By now, well over 6 million successful operations have been accomplished with various systems. In certain critical surgical procedures, where spatial accuracy is a must, physicians extensively rely on the help of CIS, and particularly on intra-operative navigation system. For these, the ways of use, including setup, registration and application accuracy metrics are provided by the manufacturers. Depending on the setup, inherent system errors can accumulate, and lead to significant deviation in position measurements. It is crucial to improve the precision of integrated setups, and to determine the overall task execution error. The stochastic approach proposed here offers an easy and straightforward solution to map and scale the error propagation. Applying pre-operative and on-site simulations, the optimal positioning of the navigation system can be achieved. This results in faster task execution and reduction of the probability of surgical errors. Surgical tracking systems have broader applications in endoscopic surgeries, and the method described in the article can be directly applied to these procedures too. It was tested in silico and on a neurosurgical prototype robot system developed at the Johns Hopkins University. The proposed features together can greatly increase the safety and reliability of all procedures where camera systems are involved, and ease the surgeon's task and potentially reduce operating time.

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Toward a standard ontology of surgical process models

Cited by 63 publications

References 66 publications

Learning procedural skills with a virtual reality simulator: An acceptability study

Learning procedural skills with a virtual reality simulator: An acceptability study

CAI4CAI: The Rise of Contextual Artificial Intelligence in Computer-Assisted Interventions

Probabilistic Method to Improve the Accuracy of Computer-Integrated Surgical Systems

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