Machine Learning for Surgical Skill Assessment from Raw Theater Data
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current surgical skill assessment methods are subjective, labor-intensive, and lack scalability, as they rely heavily on human experts and struggle to correlate raw surgical data with specific skills due to asymmetry between expert and novice surgeons.
Innovation Solution
Developed automated surgical skill-assessment systems using machine learning models to analyze surgical data, providing objective and scalable feedback by correlating surgical actions with specific skills through supervised, unsupervised, and reinforcement learning methodologies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human experts manually review surgical videos to assess surgical skills, then assessment accuracy is improved, but scalability and efficiency deteriorate due to the limited availability of expert surgeons
Solution Approach 1:
The patent replaces the mechanical system of human expert review with an automated computer-based system that uses machine learning models to analyze surgical data. The system processes surgical videos and raw data through automated algorithms, eliminating the need for human experts to manually review each case while maintaining assessment capability.
Solution Approach 2:
The patent introduces an intermediary automated assessment system that bridges the gap between surgical performance data and skill evaluation. This intermediary system processes raw surgical data and transforms it into meaningful skill assessments, reducing direct dependence on expert surgeons for every assessment.
2Productivity
If automated systems are used to assess surgical skills, then scalability and efficiency are improved, but measurement precision deteriorates due to the difficulty of correlating raw surgical data with specific skills
Solution Approach 1:
The patent performs preliminary actions by collecting and organizing diverse surgical data during the surgical procedure itself. The system captures video footage, operational data, and contextual information in real-time, preparing structured data sets that can be later analyzed by machine learning models to establish accurate skill correlations.
Solution Approach 2:
The patent transforms raw surgical data into meaningful parameters by extracting specific features and metrics from the data. The system converts unstructured video and operational data into quantifiable parameters that can be correlated with surgical skills, enabling precise automated assessment.
3Adaptability or versatility
If diverse surgical data is collected from multiple sources, then comprehensive assessment capability is improved, but device complexity and data processing difficulty worsen
Solution Approach 1:
The patent segments the complex data processing task into distinct modules and components. The system divides data collection, processing, and analysis into separate functional units that handle different types of surgical data independently, reducing overall system complexity while maintaining comprehensive assessment capability.
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.