Surgical Video Feature Extraction for Real-Time Skill Assessment
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current machine learning architectures face challenges in efficiently processing large surgical procedure video data sets with commercially available computing resources, particularly in extracting relevant features for real-time assessment of surgical performance without overloading systems or compromising accuracy, and in providing interpretable results to physicians.
Innovation Solution
A machine learning architecture that combines dimensionality reduction and sequential relation architectures to extract and link features indicative of surgical instruments, using techniques like autoencoders and Mask-RNN for instance segmentation, enabling the processing of video data with commercially available resources and generating real-time alerts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human reviewers analyze surgical procedure videos, then surgical performance can be assessed, but reviewer fatigue and subjective biases limit the quantity and quality of reviews
Solution Approach 1:
The patent replaces human reviewers with an automated machine learning system that uses computer vision and deep learning models to analyze surgical videos. The system extracts visual features, tracks surgical instruments, and assesses performance metrics automatically, eliminating human fatigue and subjectivity while maintaining assessment accuracy and enabling review of large volumes of procedures.
Solution Approach 2:
The machine learning system performs self-training and continuous improvement by learning from annotated surgical videos and adjusting its models. The system automatically annotates performance metrics and refines its analysis capabilities over time, enabling sustained high-quality review without human intervention.
2Speed
If surgical procedure videos are processed with high computational resources, then processing speed increases, but hardware cost and complexity increase
Solution Approach 1:
The patent divides the video processing task into separate stages: feature extraction, instrument tracking, and performance assessment. Each stage uses optimized computational methods and can be processed independently, allowing efficient use of computing resources and enabling real-time analysis without requiring excessive hardware power.
Solution Approach 2:
The system performs preliminary feature extraction and pre-processing of surgical videos before final performance assessment. By preparing and organizing video data in advance, the system reduces computational burden during real-time analysis and enables faster processing speeds with moderate hardware resources.
3Productivity
If machine learning models process surgical video data, then processing speed increases, but data volume and processing complexity increase
Solution Approach 1:
The patent extracts and focuses on the most critical visual features from surgical videos, such as surgical instrument positions, movements, and interactions with tissue. By concentrating computational resources on these key features rather than processing every pixel and detail, the system efficiently handles large volumes of video data without excessive processing complexity.
Solution Approach 2:
The system transforms two-dimensional video frames into three-dimensional spatial representations and temporal sequences, enabling comprehensive analysis of surgical procedures. This dimensional transformation allows the machine learning models to capture complex surgical actions and instrument movements while maintaining manageable data complexity through structured representation.
Data Source
AI summary
Computer implemented methods and systems are provided for training a machine learning architecture for surgical performance tracking and measurement based on surgical procedure video data set. The methods and systems include, in a first aspect, a sequential relation architecture and a dimensionality reduction architecture. In a second aspect, the methods and systems include a surgical instrument instance segmentation architecture, a decomposition model, and a sequential relation architecture. The video data is processed on a frame level to generate compressed or reduced representations of the video data.