Surgical Instrument Skill Quantification via Motion Segmentation
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Solution Overview
Problem
Current virtual training systems, including those for surgical robotics, lack an objective method to quantify clinical technical skill, leading to inadequate training and increased mistakes, as they struggle to distinguish between skilled and unskilled operators based on motion data variability.
Innovation Solution
A system and method for quantifying technical skill by collecting and comparing data from skilled and unskilled operators, using a surgical system like the da Vinci Surgical System, which segments and labels motion data into surgemes and dexemes, and employs machine learning techniques to model and compare expertise levels, enabling objective skill assessment and training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If motion data is collected from operators performing surgical tasks, then skill quantification becomes possible, but the variability in motion measurements makes it difficult to distinguish between skilled and unskilled operators
Solution Approach 1:
The patent segments motion data into atomic units called 'surgemes' and further into 'dexemes' (primitive motion segments). This segmentation allows the system to analyze individual motion components separately, filtering out random variability while preserving skill-related patterns. By breaking down complex surgical motions into discrete, analyzable segments, the system can reliably distinguish skilled from unskilled operators even when overall motion data shows variability.
Solution Approach 2:
The system implements feedback mechanisms that provide real-time and post-operative evaluation of operator performance. By comparing measured motion data against established skill models and providing feedback, the system enables continuous improvement and more accurate skill quantification, transforming the variability in motion measurements into actionable insights for skill assessment.
2Ease of operation
If virtual training systems are used for surgical training, then training accessibility improves, but the systems lack objective methods to quantify clinical technical skill
Solution Approach 1:
The patent replaces subjective mechanical evaluation methods with automated computational analysis. Instead of relying on human observers to assess surgical skill, the system uses computer-based motion capture, segmentation algorithms, and machine learning models to objectively quantify skill levels. This substitution enables precise skill measurement in virtual training environments while maintaining the accessibility benefits of virtual training.
3Quantity of substance
If operators perform repeatable tasks multiple times, then more data is collected for analysis, but the operator exhibits many small motion characteristic variations that obscure skill level
Solution Approach 1:
The system segments each repeated task performance into atomic surgemes and dexemes, allowing it to identify consistent patterns across multiple trials while filtering out random variations. By analyzing the segmentation results across multiple repetitions, the system can reliably detect skill level even when individual performances show small motion characteristics variations.
Solution Approach 2:
The patent transforms motion data from raw continuous measurements into discrete categorical parameters through segmentation and labeling. This parameter transformation converts variable continuous motion data into standardized categories that can be reliably compared across multiple repetitions, enabling accurate skill detection despite natural variations in performance.
Data Source
AI summary
A system quantifying clinical skill of a user, comprising: collecting data relating to a surgical task done by a user using a surgical device; comparing the data for the surgical task to other data for another similar surgical task; quantifying the clinical skill of the user based on the comparing of the data for the surgical task to the other data for the other similar surgical task; outputting the clinical skill of the user.