Virtual reality, ultrasound-guided liver biopsy simulator: development and performance discrimination

• Published in: British journal of radiology Vol. 85; no. 1013; pp. 555 - 561
• Main Authors: JOHNSON, S. J, HUNT, C. M, WOOLNOUGH, H. M, CRAWSHAW, M, KILKENNY, C, GOULD, D. A, ENGLAND, A, SINHA, A, VILLARD, P. F
• Format: Journal Article
• Language: English
• Published: London British Institute of Radiology 01.05.2012; The British Institute of Radiology
• Subjects: Biological and medical sciences; Biopsy; Biopsy - methods; Clinical Competence; Cognitive science; Computer applications; Computer Science; Computer Simulation; Development; Digestive system; Educational Measurement - methods; Ethics; Feedback; Female; Graphics; Humans; Imaging, Three-Dimensional; Investigative techniques, diagnostic techniques (general aspects); Liver; Liver - pathology; Male; Medical sciences; Pathology. Cytology. Biochemistry. Spectrometry. Miscellaneous investigative techniques; Probes; Prospective Studies; Psychology; Radiology; Radiology, Interventional - standards; Skin; Ultrasonography, Interventional; User-Computer Interface; Sonography; Performance evaluation; Guidance; Liver biopsy; Nuclear medicine; Interventional ultrasonography; Virtual reality; Radiology; Simulator; Augmented reality
• ISSN: 0007-1285; 1748-880X
• DOI: 10.1259/bjr/47436030

The aim of this article was to identify and prospectively investigate simulated ultrasound-guided targeted liver biopsy performance metrics as differentiators between levels of expertise in interventional radiology. Task analysis produced detailed procedural step documentation allowing identification of critical procedure steps and performance metrics for use in a virtual reality ultrasound-guided targeted liver biopsy procedure. Consultant (n=14; male=11, female=3) and trainee (n=26; male=19, female=7) scores on the performance metrics were compared. Ethical approval was granted by the Liverpool Research Ethics Committee (UK). Independent t-tests and analysis of variance (ANOVA) investigated differences between groups. Independent t-tests revealed significant differences between trainees and consultants on three performance metrics: targeting, p=0.018, t=-2.487 (-2.040 to -0.207); probe usage time, p = 0.040, t=2.132 (11.064 to 427.983); mean needle length in beam, p=0.029, t=-2.272 (-0.028 to -0.002). ANOVA reported significant differences across years of experience (0-1, 1-2, 3+ years) on seven performance metrics: no-go area touched, p=0.012; targeting, p=0.025; length of session, p=0.024; probe usage time, p=0.025; total needle distance moved, p=0.038; number of skin contacts, p<0.001; total time in no-go area, p=0.008. More experienced participants consistently received better performance scores on all 19 performance metrics. It is possible to measure and monitor performance using simulation, with performance metrics providing feedback on skill level and differentiating levels of expertise. However, a transfer of training study is required.