Name
Abstract | ||
|---|---|---|
This paper presents a novel approach for evaluating technical skills in Transoesophageal Echocardiography (TEE). Our core assumption is that operational competency can be objectively expressed by specific motion-based measures. TEE experiments were carried out with an augmented reality simulation platform involving both novice trainees and expert radiologists. Probe motion data were collected and used to formulate various kinematic parameters. Subsequent analysis showed that statistically significant differences exist among the two groups for the majority of the metrics investigated. Experts exhibited lower completion times and higher average velocity and acceleration, attributed to their refined ability for efficient and economical probe manipulation. In addition, their navigation pattern is characterised by increased smoothness and fluidity, evaluated through the measures of dimensionless jerk and spectral arc length. Utilised as inputs to well-known clustering algorithms, the derived metrics are capable of discriminating experience levels with high accuracy (u003e84 %). |
Year | Venue | Field |
|---|---|---|
2016 | MIAR | Kinematics,Pattern recognition,Simulation,Jerk,Augmented reality,Artificial intelligence,Acceleration,Transoesophageal echocardiography,Engineering,Motion analysis,Cluster analysis,Smoothness |
DocType | Citations | PageRank |
Conference | 1 | 0.41 |
References | Authors | |
2 | 9 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Evangelos B Mazomenos | 1 | 98 | 11.86 |
| Francisco Vasconcelos | 2 | 76 | 8.63 |
| Jeremy Smelt | 3 | 1 | 0.41 |
| Henry Prescott | 4 | 1 | 0.41 |
| Marjan Jahangiri | 5 | 2 | 0.78 |
| Bruce Martin | 6 | 13 | 5.50 |
| Andrew Smith | 7 | 27 | 6.55 |
| Susan Wright | 8 | 1 | 0.41 |
| Danail Stoyanov | 9 | 792 | 81.36 |