Machine Learning Surgical Skill Assessment for Scalable Feedback
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Solution Overview
Problem
Current surgical skill assessment methods are subjective, labor-intensive, and lack scalability, particularly due to the asymmetry in the availability of expert surgeons, making it difficult to provide meaningful feedback to surgeons.
Innovation Solution
Developed automated surgical skill-assessment systems using machine learning models that analyze surgical data to objectively evaluate surgical performance, accounting for the asymmetry in expert and novice data, and provide actionable feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human experts manually review surgical videos to assess surgical skills, then assessment accuracy can be maintained, but the system lacks scalability and requires significant time and resources
Solution Approach 1:
The patent replaces the mechanical system of human expert review with an automated computational system using machine learning models. The system processes surgical video data, sensor data, and electronic health record data through algorithms that automatically evaluate surgical performance metrics, replacing the need for manual human assessment while maintaining or improving scalability.
Solution Approach 2:
The patent creates a virtual model or digital twin of the surgical process by capturing and analyzing video footage, sensor readings, and clinical data. This copied representation of surgical performance allows automated evaluation without requiring original human experts to review actual procedures, enabling scalable assessment through data replication and analysis.
2Reliability
If more expert surgeons are involved in reviewing videos to improve assessment reliability, then feedback quality improves, but the availability of experts becomes a limiting factor
Solution Approach 1:
The patent enables the surgical assessment system to evaluate its own performance and provide feedback automatically without requiring external human experts for each assessment. The machine learning models are trained on diverse surgical data and can independently evaluate surgical performance, making the system self-sufficient and eliminating dependence on limited expert availability.
Solution Approach 2:
The patent creates a universal assessment system that can evaluate multiple surgical skills and procedures through a single automated platform. The machine learning models are designed to handle various surgical contexts, patient types, and procedural complexities, making the system adaptable to diverse surgical scenarios without requiring specialized human experts for each case.
3Productivity
If automated systems are used to assess surgical skills, then scalability improves, but the ability to capture nuanced surgical performance may be reduced
Solution Approach 1:
The patent adds multiple data dimensions to capture nuanced surgical performance by integrating video analysis, sensor data from surgical instruments, physiological monitoring, and electronic health records. This multi-dimensional approach allows the automated system to capture comprehensive surgical performance metrics that go beyond simple visual observation, enabling scalable assessment without losing nuanced information.
Solution Approach 2:
The patent performs preliminary data collection and processing during the surgical procedure itself by integrating sensors and video capture systems. This preliminary action ensures that all relevant performance data is captured in real-time before the assessment occurs, allowing the automated system to analyze comprehensive information without losing nuanced details that might be missed in post-hoc review.
Data Source
AI summary
Various of the disclosed embodiments relate to computer systems and computer-implemented methods for measuring and monitoring surgical performance. For example, the system may receive raw data acquired from the surgical theater, generate and select features from the data amenable to analysis, and then train a machine learning classifier using the selected features to facilitate subsequent assessment of other surgeons' performances. Generation and selection of the features may itself involve application of a machine learning classifier in some embodiments. While some embodiments contemplate raw data acquired from surgical robotic systems, some embodiments facilitate assessments upon data acquired from non-robotic surgical theaters.