Machine Learning Surgical Planning for Implant Positioning
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Solution Overview
Problem
Existing surgical planning systems for procedures like total knee and hip arthroplasty often require modifications during surgery due to insufficient preoperative planning based on patient-specific information, leading to inefficiencies and potential inaccuracies.
Innovation Solution
A machine learning model trained on historical surgical data is used to generate a predictor equation for implant positioning and resections, optimized for individual patient anatomy, with real-time updates and robotic assistance for precise execution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional surgical planning methods are used, then surgical workflow is simple, but surgical planning accuracy is insufficient leading to intraoperative modifications
Solution Approach 1:
The machine learning model performs surgical planning calculations and generates resection guidance before the surgery begins, allowing the surgical team to review and adjust the plan beforehand. This preliminary action ensures high accuracy in implant positioning while maintaining a relatively simple intraoperative workflow, as the complex computations are completed in advance rather than during the surgical procedure.
Solution Approach 2:
The patent introduces a machine learning model as an intermediary between the surgeon's intent and the actual surgical execution. This intermediary processes patient-specific anatomical data and generates optimized resection plans, thereby improving surgical planning accuracy without requiring the surgeon to directly perform complex calculations during surgery.
2Reliability
If patient-specific surgical planning is implemented, then surgical outcomes are improved, but computation time increases
Solution Approach 1:
The machine learning model performs computationally intensive patient-specific planning calculations before surgery begins, generating detailed resection guidance and implant positioning recommendations in advance. This allows complex computations to be completed offline, ensuring high reliability of surgical outcomes without consuming valuable intraoperative time.
Solution Approach 2:
The system uses pre-trained machine learning models with optimized parameters that have been trained on extensive historical surgical data. This pre-training allows the model to quickly generate accurate patient-specific plans without requiring extensive computation during the surgical workflow, thus improving outcomes while minimizing time loss.
3Manufacturing precision
If machine learning models are used for surgical planning, then implant positioning precision is improved, but data processing complexity increases
Solution Approach 1:
The machine learning model serves as an intermediary that automatically processes complex patient-specific anatomical data and generates precise implant positioning recommendations. This intermediary handles the data processing complexity behind the scenes, allowing surgeons to benefit from high positioning precision without directly managing the complex data processing requirements.
Solution Approach 2:
The machine learning model autonomously processes surgical planning data and generates recommendations without requiring manual intervention or complex data preparation by the surgical team. The system self-manages the data processing complexity, extracting relevant features and generating precise positioning guidance automatically, thereby improving implant positioning precision while keeping the user interface simple.
Data Source
AI summary
Methods, non-transitory computer readable media, and surgical computing devices are illustrated that improve surgical planning using machine learning. With this technology, a machine learning model is trained based on historical case log data sets associated with patients that have undergone a surgical procedure. The machine learning model is applied to current patient data for a current patient to generate a predictor equation. The current patient data comprises anatomy data for an anatomy of the current patient. The predictor equation is optimized to generate a size, position, and orientation of an implant, and resections required to achieve the position and orientation of the implant with respect to the anatomy of the current patient, as part of a surgical plan for the current patient. The machine learning model is updated based on the current patient data and current outcome