3D Anatomy Modeling for Automated Surgical Defect Quantification
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Solution Overview
Problem
Conventional surgery planning tools lack information on healthy anatomy, leading to inadequate assessment of damage size and location, and provide limited support for high-level surgical decisions, relying solely on damaged anatomy and lacking transparency and accuracy in pre-operative planning.
Innovation Solution
The system employs 3D statistical shape models (SSM) to quantify defects by comparing damaged anatomy to a reference model of healthy anatomy, providing automated defect classification and population-based decision support, incorporating historical data for informed surgical planning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional surgery planning tools are used, then the planning process is simple, but the accuracy of defect assessment is insufficient due to lack of healthy anatomy reference
Solution Approach 1:
The system performs preliminary actions by pre-acquiring and storing healthy anatomy reference data from patient population databases before the actual surgical planning process. This allows the comparison between damaged and healthy anatomy to be made accurately during planning, improving defect assessment precision without adding complexity to the core surgical planning workflow
Solution Approach 2:
The system creates a virtual copy of healthy anatomy from the statistical shape model and patient population data, which can be overlaid and compared against the damaged anatomy. This copying approach enables accurate defect quantification without requiring actual healthy tissue samples or complex imaging procedures
2Productivity
If manual defect assessment methods are used, then the system is easy to operate, but the productivity and efficiency of surgical planning are reduced
Solution Approach 1:
The system performs automated defect quantification and classification by comparing the patient's damaged anatomy against the statistical shape model and healthy reference data. This self-service capability automatically generates defect assessments, measurements, and classifications without requiring manual measurements or complex user interactions, thereby improving productivity while maintaining ease of operation through automated workflows
Solution Approach 2:
The system replaces manual mechanical measurement methods with automated computational algorithms that compare 3D anatomical models against statistical shape models. This substitution of manual processes with automated image processing and computational analysis significantly improves surgical planning efficiency while reducing the operational burden on users
3Reliability
If only damaged anatomy data is used, then the data collection process is simple, but the reliability of surgical decisions is insufficient
Solution Approach 1:
The system merges multiple data sources including damaged anatomy imaging data, healthy anatomy reference data from patient populations, statistical shape model data, and historical surgical outcome data. This combination of diverse data sources creates a comprehensive dataset that improves the reliability of surgical decisions by providing multiple perspectives and reference points for assessment
Solution Approach 2:
The system incorporates feedback loops where surgical outcomes from treated patients are fed back into the statistical shape model and patient population database. This continuous feedback mechanism improves the reliability of future surgical decisions by learning from actual outcomes and refining the reference data used for planning, creating a self-improving system
4Loss of information
If conventional planning tools are used, then the transparency of the process is maintained, but the information completeness for decision-making is insufficient
Solution Approach 1:
The system adds another dimension to surgical planning by incorporating healthy anatomy reference data and statistical shape models alongside the traditional damaged anatomy imaging. This additional dimensional information provides comprehensive defect quantification, classification, and context that was previously unavailable, ensuring no critical information is lost while maintaining system manageability through modular architecture
Data Source
AI summary
Certain aspects of the present disclosure provide techniques for preparing medical treatment plans, comprising: acquiring medical image data associated with an anatomy of a patient; creating a three-dimensional anatomy model based on the medical image data; fitting a statistical shape model to the three-dimensional anatomy model; determining one or more quantitative measurements based on the fitted statistical shape model; and classifying a defect associated with the anatomy of the patient based on the one or more quantitative measurements.