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From precision to strength: computer vision for suture quality assessment-an ex vivo pilot study
Roberto Spagnulo1, Francesco Marzola1, Federica Corso1
1Department of Surgical Sciences, University of Turin, Corso Dogliotti 14, 10128, Turin, Italy.
Surgical Endoscopy
|December 4, 2025
Summary
Spatial metrics of suture placement correlate with mechanical performance but are insufficient alone. This research could lead to intelligent systems for real-time surgical feedback and education.
Area of Science:
- Surgical Technology
- Biomechanical Engineering
- Medical Simulation
Background:
- Suturing assessment traditionally relies on subjective evaluation.
- Minimally invasive surgery (MIS) demands increased precision in surgical techniques.
- Objective metrics are needed to evaluate suture quality and operator skill in MIS.
Purpose of the Study:
- To investigate if spatial metrics of suture placement quantitatively reflect mechanical performance.
- To determine the influence of operator experience on technical outcomes.
- To assess the impact of different surgical platforms on suture quality.
Main Methods:
- Fifteen participants with varying MIS experience performed standardized suturing on porcine rectal specimens.
- Three platforms were used: conventional laparoscopy, daVinci Research Kit (dVRK), and Flex robotic endoscope.
- Spatial features of suture placement were extracted, and mechanical resistance was measured by intraluminal burst pressure.
Main Results:
- Laparoscopy yielded the highest mean burst pressure (17.38 mmHg), followed by dVRK (15.99 mmHg) and Flex (13.60 mmHg).
- Spacing irregularity negatively correlated with burst pressure (p < 0.05).
- Operator experience significantly impacted performance, with Masters achieving higher burst pressures and faster task completion times.
Conclusions:
- Spatial metrics show a correlation with mechanical resistance but are not sufficient as standalone indicators of suture quality.
- The findings suggest limitations in current platform-specific scoring.
- This research may enable intelligent intraoperative systems for real-time feedback, enhancing surgical education and quality assurance.
