Dataset Information

[Translational medicine-Development of simulation models for training in vascular surgery].

ABSTRACT: The establishment of new surgical methods and techniques is associated with a learning curve which can lead to a higher morbidity and mortality for patients during training. To counteract this problem, within the framework of surgical and endovascular training and to transfer the learning curve from the patients to the simulator, life-like models are used and tested. The benefits of such simulator training have been shown in multiple disciplines. This article presents the steps from the conception to the production and validation of a simulator for ultrasound-guided arterial and venous puncture. The goal was to develop a cost-efficient high-fidelity simulator that enables ultrasound-guided percutaneous vascular puncture, direct haptic and visual feedback and the use of some percutaneous devices in a manner as complete and life-like as possible. The finished prototype enables an ultrasound-guided puncture of arteries and veins and the model enables the deployment of endovascular devices and closure systems. To be able to provide and carry out a structured training removed from external influences and challenges is in the interest of clinical departments, trainees and ultimately patient safety. Simulation training on life-like models can provide a valuable contribution in this context and represents a welcome supplement to traditional clinical training.

SUBMITTER: Plimon M

PROVIDER: S-EPMC9427093 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

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Publications

[Translational medicine-Development of simulation models for training in vascular surgery].

Plimon Markus M Assadian Afshin A

Gefasschirurgie : Zeitschrift fur vaskulare und endovaskulare Chirurgie : Organ der Deutschen und der Osterreichischen Gesellschaft fur Gefasschirurgie unter Mitarbeit der Schweizerischen Gesellschaft fur Gefasschirurgie 20220830 5

The establishment of new surgical methods and techniques is associated with a learning curve which can lead to a higher morbidity and mortality for patients during training. To counteract this problem, within the framework of surgical and endovascular training and to transfer the learning curve from the patients to the simulator, life-like models are used and tested. The benefits of such simulator training have been shown in multiple disciplines. This article presents the steps from the concepti ...[more]

PMID: 36060552

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Project description:Simulation-based training is increasingly being used for assessment and training of psychomotor skills involved in medicine. The application of artificial intelligence and machine learning technologies has provided new methodologies to utilize large amounts of data for educational purposes. A significant criticism of the use of artificial intelligence in education has been a lack of transparency in the algorithms' decision-making processes. This study aims to 1) introduce a new framework using explainable artificial intelligence for simulation-based training in surgery, and 2) validate the framework by creating the Virtual Operative Assistant, an automated educational feedback platform. Twenty-eight skilled participants (14 staff neurosurgeons, 4 fellows, 10 PGY 4-6 residents) and 22 novice participants (10 PGY 1-3 residents, 12 medical students) took part in this study. Participants performed a virtual reality subpial brain tumor resection task on the NeuroVR simulator using a simulated ultrasonic aspirator and bipolar. Metrics of performance were developed, and leave-one-out cross validation was employed to train and validate a support vector machine in Matlab. The classifier was combined with a unique educational system to build the Virtual Operative Assistant which provides users with automated feedback on their metric performance with regards to expert proficiency performance benchmarks. The Virtual Operative Assistant successfully classified skilled and novice participants using 4 metrics with an accuracy, specificity and sensitivity of 92, 82 and 100%, respectively. A 2-step feedback system was developed to provide participants with an immediate visual representation of their standing related to expert proficiency performance benchmarks. The educational system outlined establishes a basis for the potential role of integrating artificial intelligence and virtual reality simulation into surgical educational teaching. The potential of linking expertise classification, objective feedback based on proficiency benchmarks, and instructor input creates a novel educational tool by integrating these three components into a formative educational paradigm.

Project description:BackgroundIncreasing technico-manual complexity of procedures and time constraints necessitates effective neurosurgical training. For this purpose, both screen- and model-based simulations are under investigation. Approaches including 3D printed brains, gelatin composite models, and virtual environments have already been published. However, quality of brain surgery simulation is limited due to discrepancies in visual and haptic experience. Similarly, virtual training scenarios are still lacking sufficient real-world resemblance. In this study, we introduce a novel simulator for realistic neurosurgical training that combines real brain tissue with 3D printing and augmented reality.MethodsBased on a human CT scan, a skull base and skullcap were 3D printed and equipped with an artificial dura mater. The cerebral hemispheres of a calf's brain were placed in the convexity of the skullcap and tumor masses composed of aspic, water, and fluorescein were injected in the brain. The skullcap and skull base were placed on each other, glued together, and filled up with an aspic water solution for brain fixation. Then, four surgical scenarios were performed in the operating room as follows: (1) simple tumor resection, (2) complex tumor resection, (3) navigated biopsy via burr hole trepanation, and (4) retrosigmoidal craniotomy. Neuronavigation, augmented reality, fluorescence, and ocular-as well as screen-based (exoscopic)-surgery were available for the simulator training. A total of 29 participants performed at least one training scenario of the simulator and completed a 5-item Likert-like questionnaire as well as qualitative interviews. The questionnaire assessed the realism of the tumor model, skull, and brain tissue as well as the capability for training purposes.ResultsVisual and sensory realism of the skull and brain tissue were rated,"very good," while the sensory and visual realism of the tumor model were rated "good." Both overall satisfaction with the model and eligibility of the microscope and neurosurgical instruments for training purposes were rated with "very good." However, small size of the calf's brain, its limited shelf life, and the inability to simulate bleedings due to the lack of perfusion were significant drawbacks.ConclusionThe combination of 3D printing and real brain tissue provided surgical scenarios with very good real-life resemblance. This novel neurosurgical model features a versatile setup for surgical skill training and allows for efficient training of technological support like image and fluorescence guidance, exoscopic surgery, and robotic technology.

Dataset Information

[Translational medicine-Development of simulation models for training in vascular surgery].

Publications

[Translational medicine-Development of simulation models for training in vascular surgery].

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