Development of computer vision algorithm towards assessment of suturing skill

Kil, Irfan; Jagannathan, Anand; Singapogu, Ravikiran B.; Groff, Richard E.

doi:10.1109/bhi.2017.7897197

Cited by 11 publications

(8 citation statements)

References 14 publications

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“…This section reviews the use of technology-enhanced suture training based on system analysis and deep learning models. In their study, Ton et al developed a computer vision algorithm to assess suturing ability [ 3 ]. This algorithm uses a synthetic deep-formed platform to evaluate suture skills.…”

Section: Introductionmentioning

confidence: 99%

Deep learning based suture training system

Mansour,

Cumak,

Kutlu

et al. 2023

Surgery Open Science

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Section: Introductionmentioning

confidence: 99%

Deep learning based suture training system

Mansour,

Cumak,

Kutlu

et al. 2023

Surgery Open Science

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“…In this stage (see Figure 4 ), internal video was first processed with a computer vision algorithm to obtain information about needle and thread movement ( 33 ). This information was then used to identify the individual suture cycles.…”

Section: Methodsmentioning

confidence: 99%

“…The axes of the force sensor are not generally aligned with the directions of the radial stitches, so a change of coordinates is required to determine the force components orthogonal and tangential to the stitch direction. Using the suture entry and exit points detected by computer vision ( 33 ), the suture direction at each suture location can be identified. Then, a change of coordinates can be applied to compute the force tangential to stitch direction and orthogonal to stitch direction (see Figure 7 ).…”

Section: Methodsmentioning

confidence: 99%

“…Contrary to

and

, the direction of unit vectors

and

depend on suture location. The vectors

and

are calculated from the suture entry and exit points, whose values are obtained using the computer vision algorithm ( 33 ).…”

Section: Methodsmentioning

confidence: 99%

“…Many metrics for skill assessment have been presented in the literature. These metrics can be classified as force-based metrics (15)(16)(17)(18)(19)(20)(21)(22)(23), motion-based metrics (8,18,(23)(24)(25)(26)(27)(28)(29)(30) and imagebased metrics (29,(31)(32)(33)(34)(35)(36)(37)(38)(39). Force-based metrics, such as absolute, mean, and peak forces and force volume (16)(17)(18)23), have been most successful at distinguishing novice vs. expert performance at surgical tasks.…”

mentioning

confidence: 99%

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Assessment of open surgery suturing skill: Simulator platform, force-based, and motion-based metrics

et al. 2022

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ObjectiveThis paper focuses on simulator-based assessment of open surgery suturing skill. We introduce a new surgical simulator designed to collect synchronized force, motion, video and touch data during a radial suturing task adapted from the Fundamentals of Vascular Surgery (FVS) skill assessment. The synchronized data is analyzed to extract objective metrics for suturing skill assessment.MethodsThe simulator has a camera positioned underneath the suturing membrane, enabling visual tracking of the needle during suturing. Needle tracking data enables extraction of meaningful metrics related to both the process and the product of the suturing task. To better simulate surgical conditions, the height of the system and the depth of the membrane are both adjustable. Metrics for assessment of suturing skill based on force/torque, motion, and physical contact are presented. Experimental data are presented from a study comparing attending surgeons and surgery residents.ResultsAnalysis shows force metrics (absolute maximum force/torque in z-direction), motion metrics (yaw, pitch, roll), physical contact metric, and image-enabled force metrics (orthogonal and tangential forces) are found to be statistically significant in differentiating suturing skill between attendings and residents.Conclusion and significanceThe results suggest that this simulator and accompanying metrics could serve as a useful tool for assessing and teaching open surgery suturing skill.

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