Surgical Microscope Assistance Using Tool-Agnostic Activity Maps
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Solution Overview
Problem
Existing surgical microscopes require significant manual effort to set up during operations, and existing assistance technologies lack flexibility and accuracy due to reliance on manually created databases and explicit recognition of surgical instruments.
Innovation Solution
A tool-type agnostic approach using machine-learned algorithms to analyze microscopy images and identify activity areas based on temporal context, determining a map of increased instrument presence, which controls assistance functionalities like repositioning the surgical microscope or providing user guidance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manually created databases are used for assistance functionality, then the system can provide guidance based on previous surgeries, but the flexibility and accuracy are restricted
Solution Approach 1:
The patent replaces manually created databases with automated machine learning algorithms that process microscopy images in real-time. The system uses deep neural networks to automatically detect surgical instruments, track their movement, and identify activity areas without requiring pre-programmed databases of previous surgeries, thereby achieving both high accuracy and flexibility
Solution Approach 2:
The system changes the fundamental parameter of how surgical context is stored and retrieved - from static manually created databases to dynamic real-time image processing. The machine learning model continuously learns from the current surgical field, adapting to different procedures and instrument types without requiring explicit reprogramming
2Measurement precision
If explicit recognition of surgical instruments is performed, then the assistance functionality can identify specific tool types, but the system complexity increases
Solution Approach 1:
The patent extracts the essential function of instrument identification from complex explicit recognition systems. Instead of requiring the system to identify specific instrument types, the machine learning model simply detects the presence of any surgical instrument and tracks its movement, extracting only the necessary information (presence/absence and position) to control assistance functionalities
Solution Approach 2:
The approach inverts the traditional recognition paradigm - rather than trying to identify and classify specific instrument types, the system inverts the problem by focusing on detecting the generic presence of surgical instruments and their movement patterns. This inversion simplifies the recognition system while maintaining effectiveness for controlling assistance functions
3Adaptability or versatility
If multiple settings are configured in surgical microscopes, then the system can adapt to different surgical needs, but much manual outlay is required during operation
Solution Approach 1:
The patent implements self-service functionality where the surgical microscope automatically adjusts its own settings based on real-time image analysis. The machine learning algorithm processes microscopy images, identifies activity areas, and autonomously controls microscope parameters such as focus and illumination without requiring manual intervention from the surgeon
Solution Approach 2:
The system establishes a closed-loop feedback mechanism where the microscope continuously captures images, the machine learning model analyzes them to detect instrument presence and movement, and the system automatically adjusts settings based on this feedback. This real-time feedback loop enables adaptive control without manual configuration
Data Source
AI summary
The invention relates to techniques for controlling an assistance functionality for a surgical operation on a patient. A machine-trained algorithm is used to obtain a map (76) of an operation region. This map can be used to control an assistance functionality in the context of the surgical operation. The map is indicative of one or more activity regions (101-103), which are associated with an increased probability of the presence of surgical instruments.