Development and validation of a sensor- and expert model-based training system for laparoscopic surgery: the iSurgeon

Kowalewski, Karl‐Friedrich; Hendrie, Jonathan D.; Schmidt, Mona Wanda; Garrow, Carly R.; Brückner, Thomas; Proctor, Tanja; Paul, Sai; Adigüzel, Davud; Bodenstedt, Sebastian; Erben, Andreas; Kenngott, Hannes; Erben, Young; Speidel, Stefanie; Müller‐Stich, Beat P.; Nickel, Felix

doi:10.1007/s00464-016-5213-2

Cited by 61 publications

(48 citation statements)

References 47 publications

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“…Sensorund Videodaten von Experten wurden beim laparoskopischen Knoten aufgenommen und dienten zur Erstellung von Expertenmodellen. Mithilfe eines 3-D-Avatars wurden den Trainees Ihre Bewegungen im Vergleich mit der Expertenbewegung in Echtzeit anzeigt und diese so durch die Übung geleitet, was zu kürzeren Lernkurven für laparoskopische Aufgaben beiträgt und eine objektive Messung der Performance ermöglicht [26].…”

Section: Augmented-und Virtual-reality-simulatorenunclassified

Machine Learning and Learning Machines: KI in der Aus- und Weiterbildung

et al. 2020

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ZUSAMMENFASSUNGDie Künstliche Intelligenz (KI) geht Hand in Hand mit der Digitalisierung der chirurgischen Aus- und Weiterbildung und wird in der Zukunft immer mehr an Bedeutung gewinnen. Die Anforderungen an KI in der chirurgischen Aus- und Weiterbildung sind vielfältig – genauso vielfältig sind ihre Einsatzmöglichkeiten. KI wird zunehmend im chirurgischen Curriculum berücksichtigt, auch wenn sie manchmal nicht auf den ersten Blick erkennbar ist und durch ihre Algorithmen eher im Hintergrund agiert. Die modernen digitalen Lehrmethoden und Sensortechnik eröffnen neue Wege für die Einbindung der KI in die chirurgische Aus- und Weiterbildung. Die ersten Schritte sind bereits implementiert – KI unterstützt die Erstellung von digitalem Bildmaterial zu Lehrzwecken oder erfasst mithilfe von Sensortechnik die chirurgische Leistung, um diese zu analysieren. Diverse Virtual- und Augmented-Reality-Simulatoren werden nicht nur als effektive Trainingswerkzeuge genutzt, sondern stellen vielmehr wertvolle Quellen für chirurgische Daten dar. All dies birgt enormes Potenzial für neue Erkenntnisse im Bereich der Lehrforschung, was zur Entwicklung von hocheffektiven und evidenzbasierten Lehrkonzepten beiträgt.

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Section: Augmented-und Virtual-reality-simulatorenunclassified

Machine Learning and Learning Machines: KI in der Aus- und Weiterbildung

et al. 2020

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“…Also, applications in surgical training will allow the trainees to benefit from a more standardized feedback. Training will not only be based on sensor information [36] but training results will enable comparison of trainees' performance. Because of its shared vocabulary OntoSPM can help to bridge the gap from the training lab to the OR.…”

Section: Strategy To Ensure Medical Relevance and Adoptionmentioning

confidence: 99%

Toward a standard ontology of surgical process models

et al. 2018

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“…Two studies were aimed at comparing the performance of the MK with respect to a mouse [45,46]; one compared the performance of the LMC to that of a mouse [47]; and two studies compared different COTS devices [48,49]. Seven studies had the aim of performing construct [50][51][52] or concurrent validation of the devices [53][54][55][56].…”

Section: User Testingmentioning

confidence: 99%

“…In 2014, the method was shown to represent a further step forward in the development of advanced training systems in MIS using the MicronTrackTM (Claron Technology Inc., Toronto, Canada) [54]. Beginning in 2016, several authors proposed the interesting possibility of using COTS devices for tracking of laparoscopic instruments, both the LMC [56,95,98,99] and the MK [100]. Others used the approach of tracking hand movements during MIS training [55,96].…”

Section: Interventionsmentioning

confidence: 99%

Use of commercial off-the-shelf devices for detection of manual gestures in surgery. A Systematic Literature Review (Preprint)

Alvarez-Lopez¹,

Maina²,

Saigí-Rubió³

2018

Preprint

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Background. The increasingly pervasive presence of technology in the operating room (OR) raises the need to study the interaction between the surgeon and the computer system. A new generation of tools known as commercial off-the-shelf (COTS) devices that enable non-contact gesture-based human-computer interaction (HCI) are currently being explored as a solution in surgical environments. The aim of this systematic review was to provide an account of the state-of-the-art of COTS devices in the detection of manual gestures in surgery, and to identify their use as a simulation tool for teaching motor skills in minimally invasive surgery (MIS). Methods. A systematic literature review was conducted in PubMed, Embase, ScienceDirect and IEEE for articles published between January 2000 and 2016 on the use of COTS devices for gesture detection in surgical environments, and in simulation for surgical skills learning in MIS. Results. A total of 2709 studies were identified, 76 of which met the search selection criteria. The Microsoft KinectTM and the Leap Motion ControllerTM were the most widely used COTS devices. The most common intervention was image manipulation in surgical and interventional radiology environments, followed by interaction with virtual reality environments for educational or interventional purposes; the possibility of using this technology to develop portable, low-cost simulators for skills learning in MIS was also examined. Given that the vast majority of articles found in this systematic review were proof-of-concept or prototype user and feasibility testing, we can conclude that this is a field that is still in the exploration phase in areas that require touchless manipulation in environments and settings that must adhere to asepsis and antisepsis protocols, such as angiography suites and operating rooms. Conclusions: COTS devices applied to hand and instrument GBIs in the field of simulation for skills learning and training in MIS could open up a promising field to achieve the ubiquitous training and pre-surgical warm-up.

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Development and validation of a sensor- and expert model-based training system for laparoscopic surgery: the iSurgeon

Cited by 61 publications

References 47 publications

Machine Learning and Learning Machines: KI in der Aus- und Weiterbildung

Machine Learning and Learning Machines: KI in der Aus- und Weiterbildung

Toward a standard ontology of surgical process models

Use of commercial off-the-shelf devices for detection of manual gestures in surgery. A Systematic Literature Review (Preprint)

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