Surgical Corridor Evaluation Using Machine Learning
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Solution Overview
Problem
Current surgical approaches for treating skull base lesions rely heavily on surgeon preference rather than precise anatomical assessment, due to the complexity of accurately evaluating and comparing surgical corridor dimensions in the petroclival region.
Innovation Solution
A machine learning-based tool that uses volumetric skull data to quantitatively assess surgical corridors by identifying critical bony anatomy, allowing for a geometric definition of corridor constraints and enabling direct comparison of different surgical routes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If surgeon preference is used to select surgical corridors, then surgical decision-making is simplified, but accuracy and objectivity of corridor assessment deteriorate
Solution Approach 1:
The patent replaces manual visual assessment and surgeon preference-based decision-making with an automated machine learning system. The ML model processes volumetric CT data to automatically identify anatomical structures and calculate corridor dimensions, substituting the mechanical/subjective process with an automated objective system that provides precise quantitative measurements without human bias.
Solution Approach 2:
The patent introduces a machine learning intermediary system between the raw volumetric CT data and the surgical corridor assessment. This intermediary automatically processes the complex 3D anatomical data, identifies critical structures, and calculates corridor metrics, serving as a mediator that transforms raw data into actionable surgical planning information with high precision and objectivity.
2Reliability
If manual assessment of surgical corridors is performed, then anatomical complexity is acknowledged, but assessment speed and productivity deteriorate
Solution Approach 1:
The patent replaces time-consuming manual measurement and visual assessment with automated machine learning processing. The system automatically segments anatomical structures, calculates corridor volumes and dimensions, and generates surgical planning reports, dramatically increasing productivity while maintaining or improving assessment accuracy through consistent algorithmic analysis.
Solution Approach 2:
The machine learning model performs preliminary processing of volumetric CT data by automatically identifying and segmenting critical anatomical structures before the surgical planning process. This preliminary action prepares the data in advance, allowing for rapid quantitative assessment of surgical corridors without requiring manual measurement during the surgical decision-making process.
3Measurement precision
If quantitative geometric definition of corridors is implemented, then objectivity and comparability of surgical routes improve, but device complexity increases
Solution Approach 1:
The patent uses a machine learning intermediary that automatically handles the complex computations required for quantitative corridor definition. The ML model acts as a mediator that translates complex 3D anatomical data into standardized geometric measurements, abstracting away the computational complexity from the user while delivering precise quantitative results for corridor volume, area, and dimensional characteristics.
Solution Approach 2:
The patent creates a computational model (digital copy) of the patient's skull anatomy based on volumetric CT data. This digital replica allows for repeated quantitative measurements and geometric analyses without physically manipulating the actual anatomy, enabling precise corridor dimension calculation while keeping the physical assessment process simple and straightforward.
Data Source
AI summary
A surgical planning tool evaluates a volumetric image of the skull to identify landmarks and define surgical corridors that can be compared and visualized for access to the skull for removal of tumors and the like.