Surgical Instrument Localization Using Spatial-Temporal Context Fusion
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Solution Overview
Problem
Existing robotic assistance systems in surgeries, particularly neurosurgery and spinal operations, face challenges in accurately interpreting spatial and temporal information from camera images due to the risk of algorithms favoring or ignoring one modality, leading to incorrect control signals and poor recognition of surgical instruments, especially under varying conditions.
Innovation Solution
A computer-implemented method and system that combines machine learning systems to balance the influence of spatial and temporal information from digital images, using parameter values and influence parameters to generate control signals for robotic assistance, incorporating constraints to ensure accurate recognition of instruments, including unknown types, by training multiple machine learning systems to weigh and combine spatial and temporal features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If spatial information is used for instrument recognition, then recognition accuracy in known data is improved, but adaptability to unknown scenes and instruments deteriorates
Solution Approach 1:
The patent combines spatial information from digital images with temporal information from optical flow calculations into a unified input for a machine learning system. This merging allows the system to leverage the precision of spatial recognition while gaining the adaptability of temporal context, resolving the contradiction between accurate known instrument recognition and adaptability to unknown surgical scenes.
2Adaptability or versatility
If temporal information is used for instrument recognition, then adaptability to changeable situations is improved, but recognition accuracy in known data deteriorates
Solution Approach 1:
The patent integrates temporal information (optical flow) with spatial information (digital images) as combined inputs to the machine learning system. This combination ensures that the system maintains high recognition accuracy for known instruments while simultaneously adapting to changeable surgical situations, as the temporal context provides additional cues without sacrificing spatial precision.
3Loss of information
If both spatial and temporal information are fused without constraints, then information utilization is improved, but risk of algorithm bias and incorrect interpretation increases
Solution Approach 1:
The patent employs a loss function that incorporates constraints to guide the machine learning system in balancing the contribution of spatial and temporal information. This feedback mechanism prevents algorithm bias by penalizing extreme preferences for one modality over the other, ensuring reliable interpretation of surgical scenes while fully utilizing available information.
Solution Approach 2:
The patent introduces an influence parameter that dynamically adjusts the weighting of spatial versus temporal information based on the specific surgical context. This parameter change mechanism allows the system to optimize information utilization for each situation while maintaining reliability by preventing extreme biases toward either spatial or temporal modalities.
Data Source
AI summary
A computer-implemented method for generating a control signal by locating at least one instrument by way of a combination of machine learning systems on the basis of digital images is described. In this case, the method includes determining parameter values of a movement context by using the at least two digital images and determining an influence parameter value which controls an influence of one of the digital images and the parameter values of the movement context on the input data which are used within a first trained machine learning system, which has a first learning model, for generating the control signal.