Subscribe to RSS
DOI: 10.1055/a-2066-8009
Importance and potential of simulation training in interventional radiology
Article in several languages: English | deutsch
Abstract
Background Simulation training is a common method in many medical disciplines and is used to teach content knowledge, manual skills, and team skills without potential patient danger.
Methods Simulation models and methods in interventional radiology are explained. Strengths and weaknesses of both simulators for non-vascular and vascular radiological interventions are highlighted and necessary future developments are addressed.
Results Both custom-made and commercially available phantoms are available for non-vascular interventions. Interventions are performed under ultrasound guidance, with computed tomography assistance, or using mixed-reality methods. The wear and tear of physical phantoms can be countered with in-house production of 3D-printed models. Vascular interventions can be trained on silicone models or hightech simulators. Increasingly, patient-specific anatomies are replicated and simulated pre-intervention. The level of evidence of all procedures is low.
Conclusion Numerous simulation methods are available in interventional radiology. Training on silicone models and hightech simulators for vascular interventions has the potential to reduce procedural time. This is associated with reduced radiation dose for both patient and physician, which can also contribute to improved patient outcome, at least in endovascular stroke treatment. Although a higher level of evidence should be achieved, simulation training should already be integrated into the guidelines of the professional societies and accordingly into the curricula of the radiology departments.
Key Points:
-
There are numerous simulation methods for nonvascular and vascular radiologic interventions.
-
Puncture models can be purchased commercially or made using 3D printing.
-
Silicone models and hightech simulators allow patient-specific training.
-
Simulation training reduces intervention time, benefiting both the patient and the physician.
-
A higher level of evidence is possible via proof of reduced procedural times.
Citation Format
-
Kreiser K, Sollmann N, Renz M. Importance and potential of simulation training in interventional radiology. Fortschr Röntgenstr 2023; 195: 883 – 889
Key words
education - treatment planning - patient-specific rehearsal - hightech simulation - 3D printsPublication History
Received: 01 December 2022
Accepted: 22 March 2023
Article published online:
03 May 2023
© 2023. Thieme. All rights reserved.
Georg Thieme Verlag KG
Rüdigerstraße 14, 70469 Stuttgart, Germany
-
References
- 1 Badash I, Burtt K, Solorzano CA. et al. Innovationen in der Operationssimulation: ein Überblick über vergangene, aktuelle und zukünftige Techniken. Ann Transl Med 2016; 4: 453
- 2 Bienstock J, Heuer A. A review on the evolution of simulation-based training to help build a safer future. Medicine 2022; 101: e29503
- 3 Rothkrug A, Mahboobi SK. Simulation Training and Skill Assessment in Anesthesiology. 2022 bookchapter in: StatPearls [Internet]. https://www.ncbi.nlm.nih.gov/books/NBK557711/
- 4 Satin AJ. Simulation in Obstetrics. Obstet Gynecol 2018; 132 (01) 199-209
- 5 Pietersen PI, Bjerrum F, Tolsgaard MG. et al. Standard Setting in Simulation-based Training of Surgical Procedures: A Systematic Review. Ann Surg 2022; 275 (05) 872-882
- 6 Bube SH, Kingo PS, Madsen MG. et al. National Implementation of Simulator Training Improves Transurethral Resection of Bladder Tumours in Patients. Eur Urol Open Sci 2022; 39: 29-35
- 7 https://register.awmf.org/assets/guidelines/015-083l_S3_Vaginale-Geburt-am-Termin_2021-03.pdf abgerufen am 29.11.2022
- 8 https://register.awmf.org/de/leitlinien/detail/021-014 , abgerufen am 29.11.2022
- 9 Nayahangan LJ, Nielsen KR, Albrecht-Beste E. et al. Determining procedures for simulation-based training in radiology: a nationwide needs assessment. Eur Radiol 2018; 28 (06) 2319-2327
- 10 https://www.nephro-xperts.de/gelatine-ultraschall-phantome/ , abgerufen am 12.11.22
- 11 https://medicalskillstrainers.cae.com/ abgerufen am 13.11.22,
- 12 https://www.anatomie-modelle.de/Ultraschall-Modelle , abgerufen am 13.11.22
- 13 Kahr Rasmussen N, Andersen TT, Carlsen J. et al. Simulation-Based Training of Ultrasound-Guided Procedures in Radiology – A Systematic Review. Ultraschall in Med 2019; 40 (05) 584-602
- 14 http://perk.cs.queensu.ca/contents/perktutor abgerufen am 13.11.22;
- 15 Freschi C, Parrini S, Dinelli N. et al. Hybrid simulation using mixed reality for interventional ultrasound imaging training. Int J CARS 2015; 10: 1109-1115
- 16 Villard PF, Vidal FP, ap Cenydd L. et al. Interventional radiology virtual simulator for liver biopsy. Int J Comput Assist Radiol Surg 2014; 9 (02) 255-267
- 17 Picard M, Nelson R, Roebel J. et al. Use of Low-Fidelity simulation laboratory training for teaching radiology residents CT-guided procedures. J Am Coll Radiol 2016; 13 (11) 1363-1368
- 18 Baadh A, Fadl A, Georgiou N. et al. A pilot program for use of a homemade phantom for CT biopsy simulation training. [Abstract No. 376]. J Vasc Interv Radiol 2015; 26 (Suppl. 02) S167
- 19 Nakatani M, Kariya S, Ono Y. et al. Radiation Exposure and Protection in Computed Tomography Fluoroscopy. Interv Radiol 2022; 7 (02) 49-53
- 20 Cahalane AM, Habibollahi S, Staffa SJ. et al Helical CT versus intermittent CT fluoroscopic guidance for musculoskeletal needle biopsies: impact on radiation exposure, procedure time, diagnostic yield, and adverse events. Skeletal Radiology 2022; . Epub ahead of print.
- 21 Van den Bosch V, Salim HS, Chen NZ. et al. Augmented Reality-Assisted CT-Guided Puncture: A Phantom Study. Cardiovasc Intervent Radiol 2022; 45 (08) 1173-1177
- 22 Trace AP, Ortiz D, Deal A. et al. Radiology’s Emerging Role in 3D Printing Applications in Health Care. J Am Coll Radiol 2016; 13: 856-862.e4
- 23 Goudie C, Kinnin J, Bartellas M. et al. The Use of 3D Printed Vasculature for Simulation-based Medical Education Within Interventional Radiology. Cureus 2019; 11 (04) e4381
- 24 Wu TC, Weng JY, Lin CJ. et al. Patient-Specific 3D-Print Extracranial Vascular Simulators and Infrared Imaging Platform for Diagnostic Cerebral Angiography Training. Healthcare 2022; 10 (11) 2277
- 25 Aramburu J, Antón R, Fukamizu J. et al. In Vitro Model for Simulating Drug Delivery during Balloon-Occluded Transarterial Chemoembolization. Biology 2021; 10 (12) 1341
- 26 Miranpuri AS, Nickele CM, Akture E. et al. Neuroangiography simulation using a silicone model in the angiography suite improves trainee skills. J Neurointerv Surg 2014; 6 (07) 561-564
- 27 Morita R, Abo D, Soyama T. et al. Usefulness of preoperative simulation with patient-specific hollow vascular models for high-flow renal arteriovenous fistula embolization using a preloading coil-in-plug technique. Radiol Case Rep 2022; 17 (10) 3578-3586
- 28 Itagaki MW. Using 3D printed models for planning and guidance during endovascular intervention: a technical advance. Diagn Interv Radiol 2015; 21 (04) 338-341
- 29 Stana J, Grab M, Kargl R. et al. 3D printing in the planning and teaching of endovascular procedures. Radiologie 2022;
- 30 https://cathi.de/en/hardware-products#hardware-systems , abgerufen am 30.11.2022
- 31 Kreiser K, Gehling K, Zimmer C. Simulation in Angiography – Experiences from 5 Years Teaching, Training, and Research. Fortschr Röntgenstr 2019; 191 (06) 547-552
- 32 Nielsen CA, Lönn L, Konge L. et al. Simulation-Based Virtual-Reality Patient-Specific Rehearsal Prior to Endovascular Procedures: A Systematic Review. Diagnostics 2020; 10 (07) 500
- 33 Chuah KC, Stuckey SL, Berman IG. Silent embolism in diagnostic cerebral angiography: detection with diffusion-weighted imaging. Australas Radiol 2004; 48 (02) 133-138
- 34 Bendszus M, Koltzenburg M, Burger R. et al. Silent embolism in diagnostic cerebral angiography and neurointerventional procedures: a prospective study. Lancet 1999; 354: 1594-1597
- 35 Kreiser K, Gehling KG, Ströber L. et al. Simulation Training in Neuroangiography: Transfer to Reality. Cardiovasc Intervent Radiol 2020; 43 (08) 1184-1191
- 36 Schneider MS, Sandve KO, Kurz KD. et al. Metric based virtual simulation training for endovascular thrombectomy improves interventional neuroradiologists’ simulator performance. Interventional Neuroradiology 2022;
- 37 Spiotta AM, Turner RD, Turk AS. et al. The case for a milestone-based simulation curriculum in modern neuroendovascular training. J Neurointerv Surg 2016; 8 (04) 429-433