ML Surgical Guidance for Anatomical Identification and Error Prevention
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Solution Overview
Problem
Surgical mistakes during procedures, particularly in minimally invasive and robotically assisted surgeries, are common due to misidentification of anatomical structures and incorrect procedural steps, exacerbated by limited endoscopic views and lack of haptic feedback.
Innovation Solution
A machine learning-based medical procedure system that provides guidance and support by analyzing sensor data from medical tools, identifying anatomical structures, and tracking procedural steps, using trained models to prevent errors and offer real-time decision support.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If minimally invasive surgery and robotically assisted surgery are performed, then surgical precision and patient recovery are improved, but the risk of misidentification of anatomical structures and surgical mistakes increases due to limited endoscopic field of view and lack of haptic feedback
Solution Approach 1:
The system implements real-time feedback by continuously monitoring surgical video feeds, sensor data from medical tools, and procedural checkpoints. The machine learning model analyzes this data stream and provides immediate feedback to the surgeon through the user interface, alerting to potential errors, confirming correct procedural steps, and warning of anatomical structure misidentification. This closed-loop feedback system compensates for the lack of natural haptic feedback in robotic surgery.
Solution Approach 2:
The patent introduces an intelligent software intermediary layer between the surgeon's controls and the surgical instruments. This intermediary uses machine learning models to interpret sensor data, predict surgical intent, and provide decision support without directly interfering with surgical precision. The intermediary acts as a virtual assistant that enhances reliability by filtering and contextualizing information for the surgeon.
2Measurement precision
If machine learning models are trained on medical procedure data to provide real-time guidance, then surgical error prevention and anatomical identification accuracy are improved, but system complexity and data processing requirements increase
Solution Approach 1:
The system segments the complex machine learning task into multiple specialized models: one for anatomical structure identification, another for procedural step recognition, and a third for error prediction. Each model processes specific aspects of the surgical data independently, then integrates their outputs through a decision fusion layer. This segmentation reduces computational complexity while maintaining high identification accuracy.
Solution Approach 2:
The system performs preliminary training and validation of machine learning models using extensive medical procedure datasets before deployment. Checkpoint models are pre-trained to recognize common anatomical structures and procedural patterns. During actual surgery, these pre-trained models provide rapid inference, reducing real-time computational burden and system complexity.
3Reliability
If real-time analysis of sensor data and video feeds is performed during surgery, then surgical mistake detection and procedural guidance are improved, but computational resource consumption and processing time increase
Solution Approach 1:
The system implements partial analysis by focusing computational resources on critical surgical moments and high-risk procedures. The machine learning model dynamically adjusts analysis intensity based on procedural phase, anatomical region, and detected risk levels. During low-risk phases, analysis is reduced to essential monitoring, conserving computational resources while maintaining detection capability for significant errors.
Solution Approach 2:
The system maintains continuous monitoring of surgical procedures through streamlined data processing pipelines. Rather than performing intensive analysis at every moment, the system continuously streams sensor data and video feeds through optimized inference engines that maintain readiness for rapid error detection while consuming minimal computational resources during stable procedural phases.
Data Source
AI summary
An apparatus, system and process for guiding a surgeon during a medical procedure to prevent surgical mistakes are described. The system may include a machine learning medical procedure server that generates one or more machine learning medical procedure models using, at least, medical procedure data captured during medical procedures performed at a plurality of different medical procedure systems. The system may also include a medical procedure system communicably coupled with the machine learning medical procedure server that receives a selected machine learning medical procedure model from the machine learning medical procedure server, and utilizes the selected machine learning medical procedure model during a corresponding medical procedure to control one or more operations of the medical procedure system.