Analysis of Kinematic Differences in Hand Motion between Novice and Experienced Operators in IR: A Pilot Study

The Effect of Time Pressure on Motion Economy and Smoothness of Interventional Radiology Trainee Performance in Simulated Central Venous Line Placement

PURPOSE

To evaluate the effect of being under time pressure on procedural performance using hand motion analysis.

MATERIALS AND METHODS

Eight radiology trainees performed central venous access on a phantom while recording video and hand motion data using an electromagnetic motion tracker. Each trainee performed the procedure six times: the first three trials without any prompts (control), while for the next three, they were asked to perform the task as fast as possible (time pressure). Validated hand motion metrics were analyzed, and two blinded and independent evaluators rated procedural performance using a previously validated task-specific global rating scale (GRS). Motion/time ratios and linear mixed-effect methods were used to control for time, and constants for both strategies were compared.

RESULTS

Hand motion analysis showed that trainees completed the simulated procedure faster under time pressure (46 ± 18 s vs. 56 ± 27 s, p = 0.008) than during the control strategy. However, when controlling for time, trainees moved their hands 79 more centimeters (p = 0.04), made 15 more translational movements (p = 0.003) and 18 more rotational movements (p = 0.01) when under time pressure compared to at their own pace.

CONCLUSION

Although trainees could perform the procedure faster under time pressure, there was a deterioration in hand motion economy and smoothness. This suggests that hand motion metrics offer a more comprehensive assessment of technical performance than time alone.

Analyzing the Evolution of Needle and Ultrasound Probe Manipulation Skills of Interventional Radiology Trainees With Time and Experience

PURPOSE

To test the hypothesis that hand motion analysis can measure the progression of needle and ultrasound probe manipulation skills of interventional radiology trainees in central venous line placement.

MATERIALS AND METHODS

An expert cohort of 6 interventional radiologists and 4 anesthesiologists and a trainee cohort of 6 novice trainees (<50 central lines) and 5 experienced trainees (>50 central lines) performed simulated central venous access. Four novices and 1 experienced trainee repeated the task 1 year later. An electromagnetic motion tracking system tracked the needle hand and ultrasound probe. Path length, translational, and rotational movements were calculated separately for the needle hand and probe sensor. These metrics were used to calculate motion metrics based scores on a scale of 0 to 3 for each sensor. Nonparametric statistics were used, and the data are reported as median ± interquartile range.

RESULTS

Comparing novice and experienced trainees, there was a significant difference in probe scores (experienced vs. novice: 1 ± 2 vs. 0 ± 0, P = 0.04) but not in needle-hand scores (1 ± 1.5 vs. 0 ± 1, P = 0.26). Trainees showed a significant increase in probe scores at the 1-year follow-up (baseline vs. follow-up: 0 ± 1 vs. 2.5 ± 1.8, P = 0.003), but no significant difference was observed in the needle manipulation metrics. Experts differed significantly from experienced trainees for all metrics for both sensors (P < 0.05), with the exception of the path length of the probe.

CONCLUSIONS

Acquisition of improved dexterity of the probe may occur before improvement in the dexterity with the needle hand for interventional radiology trainees.

Expert-Novice Level Classification Using Graph Convolutional Network Introducing Confidence-Aware Node-Level Attention Mechanism

In this study, we propose a classification method of expert-novice levels using a graph convolutional network (GCN) with a confidence-aware node-level attention mechanism. In classification using an attention mechanism, highlighted features may not be significant for accurate classification, thereby degrading classification performance. To address this issue, the proposed method introduces a confidence-aware node-level attention mechanism into a spatiotemporal attention GCN (STA-GCN) for the classification of expert-novice levels. Consequently, our method can contrast the attention value of each node on the basis of the confidence measure of the classification, which solves the problem of classification approaches using attention mechanisms and realizes accurate classification. Furthermore, because the expert-novice levels have ordinalities, using a classification model that considers ordinalities improves the classification performance. The proposed method involves a model that minimizes a loss function that considers the ordinalities of classes to be classified. By implementing the above approaches, the expert-novice level classification performance is improved.

Characterization of Surgical Movements As a Training Tool for Improving Efficiency

INTRODUCTION

Surgical experience is often reflected by efficient, fluid, and well-calculated movements. For a new trainee, learning these characteristics is possible only by observation as there is no quantification system to define these factors. We analyzed surgeons' hand movements with different experience levels to characterize their movements according to experience.

METHODS

Hand motions were recorded by an inertial measurement unit (IMU) mounted on the hands of the surgeons during a simulated surgical procedure. IMU data provided acceleration and Eulerian angles: yaw, roll, and pitch corresponding to hand motions as radial/ulnar deviation, pronation/supination, and extension/flexion, respectively. These variables were graphically depicted and compared between three surgeons.

RESULTS

Participants were assigned to three groups based on years of surgical experience: group 1: >15 y; group 2: 3-10 y; and group 3: 0-1 y. Visualization of the roll motion, being the main motion during suturing, showed the clear difference in fluidity and regularity of the movements between the groups, showing minimal wasted movements for group 1. The angle of the roll motion, measured at the minimum, midpoint, and maximum points was significantly different between the groups. As expected, the experienced group completed the procedure first; however, the acceleration was not different between the groups.

CONCLUSIONS

Surgeons' hand movements can be easily characterized and quantified by an IMU device for automatic assessment of surgical skills. These characteristics graphically visualize a surgeon's regularity, fluidity, economy, and efficiency. The characteristics of an experienced surgeon can serve as a training model and as a reference tool for trainees.