2021
DOI: 10.3389/frobt.2021.625003
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Motion Smoothness Metrics for Cannulation Skill Assessment: What Factors Matter?

Abstract: Medical training simulators have the potential to provide remote and automated assessment of skill vital for medical training. Consequently, there is a need to develop “smart” training devices with robust metrics that can quantify clinical skills for effective training and self-assessment. Recently, metrics that quantify motion smoothness such as log dimensionless jerk (LDLJ) and spectral arc length (SPARC) are increasingly being applied in medical simulators. However, two key questions remain about the effica… Show more

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Cited by 9 publications
(11 citation statements)
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References 34 publications
(72 reference statements)
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“…Indeed, in a study by Caronni et al [ 32 ], the authors investigated differences in the smoothness of head movements between healthy subjects and patients with idiopathic cervical dystonia, using the same indices here proposed, but obtaining opposite results: in fact, in their study, only the SPARC was able to distinguish between healthy subject and patient with cervical dystonia. The authors interpreted the better behavior of the SPARC in light of the study by Singh et al [ 35 ], which suggested that the SPARC is more sensitive to changes in smaller movements. Given that our protocol involved large movements, this interpretation may also help to explain our findings.…”
Section: Discussionmentioning
confidence: 92%
“…Indeed, in a study by Caronni et al [ 32 ], the authors investigated differences in the smoothness of head movements between healthy subjects and patients with idiopathic cervical dystonia, using the same indices here proposed, but obtaining opposite results: in fact, in their study, only the SPARC was able to distinguish between healthy subject and patient with cervical dystonia. The authors interpreted the better behavior of the SPARC in light of the study by Singh et al [ 35 ], which suggested that the SPARC is more sensitive to changes in smaller movements. Given that our protocol involved large movements, this interpretation may also help to explain our findings.…”
Section: Discussionmentioning
confidence: 92%
“…Given that the mathematics behind these measures is quite different, LDLJ and SPARC likely quantify different aspects of smoothness 23 . Compared to t_LDLJ, t_SPARC seems more sensitive to changes in smaller movements 55 . In addition, t_SPARC is more robust to measurement noise 9 being, therefore, more reliable.…”
Section: Discussionmentioning
confidence: 99%
“…Three kinds of angle metrics on the basis of the angle time series profile during needle insertion were proposed and used in this study: the average insertion angle (α¯); the number of times the needle angle is changed during cannulation ( #angle_changes ), which denotes needle “digging”; and the log dimensionless jerk of insertion angle ( LDLJ ( α )), which evaluates “smoothness” of needle angle. 13 The details of the calculation of angle metrics are presented in Supplemental Table 1 of the Supplemental Material. Our previous study demonstrated that different segments of the cannulation task elicit segment-specific behavior.…”
Section: Methodsmentioning
confidence: 99%
“…11 In line with this, our team has developed a "smart" cannulation simulator with the ability to comprehensively quantify cannulation skill. [12][13][14][15][16][17] In this work-and to the best of our knowledge, for the first time-we present a simulator-based, quantitative study examining the relationship between needle angles, cannulation outcomes, and cannulation skill from a cohort of nurses and PCTs. Toward this, we introduce three kinds of novel angle metrics on the basis of needle insertion angle profiles during cannulation to quantify cannulation performance.…”
Section: Introductionmentioning
confidence: 99%