3D Femur Resection Planning to Avoid Trochlear Notching
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Robotic surgical systems in orthopedic procedures, such as robotically assisted total knee arthroplasty, can position cut guides in ways that could lead to negative outcomes due to inappropriate combinations of resection angles and depths, such as notching the trochlea, which are not easily detected by traditional instrumentation.
Innovation Solution
A computer-implemented method using machine learning models to predict and prevent such negative outcomes by training on simulated resections, analyzing resection parameters, anatomical landmarks, and bone models, generating warnings for potentially harmful configurations before actual bone cutting occurs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If robotic surgical systems are used to position cut guides with flexible resection parameters, then surgical flexibility and adaptability are improved, but the risk of negative outcomes such as trochlear notching increases due to inappropriate parameter combinations
Solution Approach 1:
The system performs preliminary detection of harmful resection outcomes by simulating the planned bone resection on a 3D bone model before actual surgery. The machine learning model predicts whether the planned resection parameters will cause trochlear notching or other harmful effects, allowing surgeons to adjust parameters beforehand to avoid negative outcomes.
Solution Approach 2:
The system provides real-time feedback to surgeons about the potential harmful effects of selected resection parameters. The machine learning model analyzes the planned resection and outputs warnings or recommendations, enabling surgeons to modify their plans based on this feedback before proceeding with the actual bone cutting.
2Object-affected harmful factors
If traditional instrumentation is used for bone resection, then the risk of trochlear notching is reduced, but measurement precision and detection capability for complex resection outcomes deteriorate
Solution Approach 1:
The system replaces traditional mechanical measurement and detection instruments with a computer-based machine learning model that processes 3D bone models and resection parameters. This digital system provides superior detection capability for predicting trochlear notching and other harmful outcomes compared to physical instrumentation.
Solution Approach 2:
The system creates a digital 3D copy or model of the patient's bone anatomy and performs virtual resection simulations on this copy. This allows for precise measurement and detection of potential harmful outcomes without affecting the actual bone, enabling high-precision analysis that would be difficult with traditional instruments.
3Manufacturing precision
If surgeon control over implant placement and resection parameters is allowed, then surgical precision and customization are improved, but the opportunity for errors increases
Solution Approach 1:
The system maintains surgeon control over implant placement and resection parameters while providing automated feedback about potential errors. The machine learning model analyzes the surgeon's planned parameters and warns of possible harmful outcomes, allowing the surgeon to maintain autonomy while reducing error risk through informed decision-making.
Solution Approach 2:
The system applies preliminary anti-action by detecting and warning against harmful parameter combinations before the surgeon finalizes the resection plan. This preemptive error prevention allows surgeons to correct potential mistakes before they are committed, maintaining surgical precision while enhancing reliability.
Data Source
Figure 1
Figure 2A
Figure 2B
AI summary
A technique for predicting bone resection issues during robotic surgery is provided. The technique includes accessing robotic surgery resection parameters and receiving landmarks from the distal end of a patient's femur. A 3D model of the distal femur is generated based on the landmarks. A virtual robotic resection of the distal femur is simulated using the model and parameters. Analysis of the simulated resection predicts possible issues like notching of the trochlea. Warnings are generated if problems are predicted, allowing the surgeon to adjust the plan preoperatively. By simulating the robotic bone resection, potential problems can be anticipated and avoided through appropriate changes to the surgical plan.