AI Orthopedic Planning System for Automated Bone Segmentation
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Solution Overview
Problem
Existing orthopedic surgery planning systems rely heavily on manual user interaction for pre-operative planning, limiting efficiency and accuracy, and lack comprehensive bone quality evaluations necessary for precise surgical planning.
Innovation Solution
An automated orthopedic surgery planning system utilizing Artificial Intelligence (AI) and Deep Learning to perform bone segmentation, classification, landmark detection, and bone quality evaluations, including osteophytes detection and bone density assessment, while integrating user preferences for personalized planning and allowing remote web-based operation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated AI-based bone segmentation and landmark detection are implemented, then productivity and measurement precision are improved, but device complexity increases due to integration of deep learning models and multi-planar rendering modules
Solution Approach 1:
The system segments the complex orthopedic planning task into distinct functional modules: a deep learning module for bone segmentation and landmark detection, a multi-planar rendering module for 2D/3D visualization, and a surgical planning module for procedure design. This modular segmentation allows each component to specialize in specific functions, improving overall productivity while managing complexity through clear separation of concerns.
Solution Approach 2:
The patent introduces a multi-planar rendering module as an intermediary between the deep learning analysis results and the surgical planning interface. This intermediary transforms complex 3D bone models and segmentation data into intuitive 2D cross-sectional views (axial, coronal, sagittal planes) that are easier for surgeons to interpret, thereby improving productivity without requiring surgeons to directly interact with complex AI models.
2Measurement precision
If comprehensive bone quality evaluations including osteophytes detection and bone density assessment are performed, then measurement precision and diagnostic accuracy are improved, but loss of time increases due to additional processing requirements
Solution Approach 1:
The deep learning model performs preliminary automatic detection of bone quality features (osteophytes, bone density variations, cortical thickness) during the initial bone segmentation phase. By identifying these features upfront while the bone model is being created, the system avoids time-consuming separate analysis steps later, thus improving measurement precision without proportionally increasing total processing time.
Solution Approach 2:
The system continuously evaluates bone quality metrics throughout the image processing pipeline rather than performing discrete sequential analyses. The deep learning model simultaneously extracts multiple bone quality features (density, texture, structural integrity) in a single pass through the medical images, maintaining continuous useful action that improves diagnostic accuracy while minimizing time loss.
3Ease of operation
If automatic pre-operative planning proposals are generated with integration of user preferences, then ease of operation is improved, but loss of information increases if user-specific customization requirements are not properly captured
Solution Approach 1:
The system implements a feedback mechanism where automatically generated pre-operative planning proposals are presented to the surgeon for review and validation. The surgeon can provide feedback by accepting, modifying, or rejecting specific planning elements, and this feedback is used to refine future automatic proposals. This closed-loop feedback ensures that user preferences are continuously captured and integrated, improving ease of operation while preventing loss of critical customization information.
Solution Approach 2:
The system enables surgeons to customize their own user profiles with preferred planning parameters, visualization settings, and procedural preferences. The automatic planning module then uses these self-defined user preferences to generate personalized proposals without requiring manual configuration for each case. This self-service approach improves ease of operation while preserving individual surgeon-specific information through automated preference management.
Data Source
AI summary
The present application describes an automatic orthopedic surgery planning system and method thereof. Embodiments described herein include a computer-implemented method for automatic orthopedic surgery planning comprising the steps of: importing at least one orthopedic medical image from a patient into a software application; selecting a medical procedure to apply to the imported orthopedic medical image; generating a bone model and landmark position of the imported orthopedic medical image; adjusting the landmark position of the imported orthopedic medical image; automatically create a pre-operative planning proposal for the orthopedic medical image; validation of the proposed automatic pre-operative planning proposal; and data file export of the orthopedic surgery planning proposal in case of positive validation.