Development and face validation of ultrasound-guided renal biopsy virtual trainer
- Author(s): Andinet Enquobahrie 1 ; Sam Horvath 1 ; Sreekanth Arikatla 1 ; Avi Rosenberg 2 ; Kevin Cleary 3 ; Karun Sharma 3
-
-
View affiliations
-
Affiliations:
1:
Medical Computing , Kitware Inc , Carrboro, NC , USA ;
2: School of Medicine, Johns Hopkins University , Baltimore, MD , USA ;
3: Sheikh Zayed Institute for Pediatric Surgical Innovation, Children's National Health System , Washington, DC , USA
-
Affiliations:
1:
Medical Computing , Kitware Inc , Carrboro, NC , USA ;
- Source:
Volume 6, Issue 6,
December
2019,
p.
210 – 213
DOI: 10.1049/htl.2019.0081 , Online ISSN 2053-3713
Full text loading...
Inspec keywords: medical computing; surgery; needles; public domain software; paediatrics; patient treatment; biomedical ultrasonics; kidney; biomedical education; diseases; computer based training
Other keywords: renal pathologies; needle visualisation; hand-eye coordination; chronic kidney disease; high yield biopsy samples; automated skill assessment; US-guided renal biopsy; time 3.0 year to 23.0 year; open source software libraries; adult-paediatric nephrologists; kidney failure; interventional-diagnostic radiologists; virtual simulator; procedural skill competence; face validation; Web-based application; low-cost hardware components; tracking modules; clinical validation study; US-guided needle biopsy; US images; clinical experts; ultrasound-guided renal biopsy virtual trainer; competent biopsy technique
Subjects: Education and training; Computer-aided instruction; Patient care and treatment; Instructional computer use for education; Patient care and treatment; Sonic and ultrasonic radiation (biomedical imaging/measurement); Sonic and ultrasonic radiation (medical uses); Patient diagnostic methods and instrumentation; Sonic and ultrasonic applications; Biology and medical computing
References
-
-
1)
-
1. ‘Kidney Disease Statistics for the United States | NIDDK’, Available at: https://www.niddk.nih.gov/health-information/health-statistics/kidney-disease, (Accessed: 14th July 2019).
-
-
2)
-
2. Mackinnon, B., McKinlay, J., McQuarrie, E., et al: ‘Early ultrasound to detect complications after renal biopsy’, Nephrol. Dial. Transplant., 2010, 25, pp. 316–317 (doi: 10.1093/ndt/gfp540).
-
-
3)
-
3. Waldo, B., Korbet, S.M., Freimanis, M.G., et al: ‘The value of post-biopsy ultrasound in predicting complications after percutaneous renal biopsy of native kidneys’, Nephrol. Dial. Transplant., 2009, 24, pp. 2433–2439 (doi: 10.1093/ndt/gfp073).
-
-
4)
-
4. Caliskan, K.C, Ozcelik, G., Cakmakci, E., et al: ‘Real time US guided pediatric percutaneous renal biopsy: the traditional method vs angled tangential approach’, J. Belg. Soc. Radiol., 2014, 97, p. 206 (doi: 10.5334/jbr-btr.96).
-
-
5)
-
5. Farjad Sultan, S., Shorten, G., Iohom, G.: ‘Simulators for training in ultrasound guided procedures’, Med. Ultrason., 2013, 15, pp. 125–131 (doi: 10.11152/mu.2013.2066.152.sfs1gs2).
-
-
6)
-
6. Mendiratta-Lala, M., Williams, T., de Quadros, N., et al: ‘The use of a simulation center to improve resident proficiency in performing ultrasound-guided procedures’, Acad. Radiol., 2010, 17, pp. 535–540 (doi: 10.1016/j.acra.2009.11.010).
-
-
7)
-
7. Basdogan, C., De Rensselaer, S., Jung Kim, , et al: ‘Haptics in minimally invasive surgical simulation and training’, IEEE Comput. Graph. Appl., 2004, 24, pp. 56–64 (doi: 10.1109/MCG.2004.1274062).
-
-
8)
-
8. Liu, A., Tendick, F., Cleary, K., et al: ‘A survey of surgical simulation: applications, technology, and education’, Presence Teleoperators Virtual Environ., 2003, 12, pp. 599–614 (doi: 10.1162/105474603322955905).
-
-
9)
-
9. ‘Blue Phantom Ultrasound Training Medical Models and Ultrasound Simulators’, Available at: http://www.bluephantom.com/, (Accessed: 14th July 2019).
-
-
10)
-
10. ‘ToLTech - OPUS Medical Skills Trainer’, Available at: https://www.toltech.net/medical-simulators/products/opus-medical-skills-trainer, (Accessed: 29th August 2019).
-
-
11)
-
11. ‘U/S Mentor | Simbionix’, Available at: https://simbionix.com/simulators/us-mentor/, (Accessed: 29th August 2019).
-
-
12)
-
6. Fedorov, A., Beichel, R., Kalpathy-Cramer, J., et al: ‘3D slicer as an image computing platform for the quantitative imaging network’, Magn. Reson. Imaging, 2012, 30, (9), pp. 1323–1341 (doi: 10.1016/j.mri.2012.05.001).
-
-
13)
-
16. Lasso, A., Heffter, T., Rankin, A., et al: ‘Plus: opensource toolkit for ultrasound-guided intervention systems’, IEEE Trans. Biomed. Eng., 2014, 61, pp. 2527–2537 (doi: 10.1109/TBME.2014.2322864).
-
-
14)
-
14. ‘IMSTK’, Available at: www.imstk.org.
-
-
15)
-
15. ‘VTK: vtkImageReslice Class Reference’, Available at: https://vtk.org/doc/nightly/html/classvtkImageReslice.html, (Accessed: 29th August 2019).
-
-
16)
-
16. Schroeder, W., Maynard, R., Geveci, B.: ‘Flying edges: A high-performance scalable isocontouring algorithm’. IEEE Symp. on Large Data Analysis and Visualization 2015, LDAV 2015-Proc. 33–40, Chicago, IL, USA, 2015, doi:10.1109/LDAV.2015.7348069.
-
-
17)
-
17. Si, H.: ‘Tetgen, a delaunay-based quality tetrahedral mesh generator’, ACM Trans. Math. Softw., 2015, 41, pp. 1–36 (doi: 10.1145/2629697).
-
-
18)
-
18. Cignoni, P., Callieri, M., Corsini, M., et al: ‘MeshLab: an Open-Source Mesh Processing Tool. Sixth Eurographics Italian Chapter Conference, 2008, pp. 129–136.
-
-
19)
-
19. Felippa, C.A., Haugen, B.: ‘Unified formulation of small-strain corotational finite elements: I. Theory’, Comput. Methods Appl. Mech. Eng.,2005, 194, pp. 2285–2335 (doi: 10.1016/j.cma.2004.07.035).
-
-
1)
Related content
