ACL Revision CT Segmentation for Tunnel and Hardware Localization
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Solution Overview
Problem
Revision ACL reconstruction procedures face challenges in accurately locating existing tunnels and hardware from previous procedures, leading to issues with improper graft fixation and tensioning, and hardware removal complications due to inaccurate imaging and readings in conventional methods.
Innovation Solution
A machine learning algorithm is employed to automatically identify tunnels and hardware in CT images, generating synthetic ACL reconstruction CT images for improved preoperative and intraoperative planning, using thresholding and 3D modeling to guide surgeons in revision ACL procedures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional MRI and CT imaging methods are used to locate existing tunnels and hardware, then the imaging process is straightforward, but the accuracy and reliability of tunnel and hardware identification is insufficient
Solution Approach 1:
The patent replaces manual visual analysis of CT images with an automated machine learning system. The ML model processes CT scan data to automatically identify tunnels, hardware, and abnormal tunnel widening, eliminating the need for surgeons to manually interpret complex imaging data and thereby improving identification accuracy without increasing operational complexity.
Solution Approach 2:
The patent introduces a machine learning intermediary system between the CT scanner and the surgeon's decision-making process. This intermediary automatically processes the CT images, identifies anatomical structures and abnormalities, and provides structured information that guides surgical planning, thereby improving measurement precision while keeping the overall system simple to use.
2Reliability
If manual visual analysis of CT images is performed to determine tunnel positioning, then the process is simple, but the reliability of operative planning is compromised due to inaccurate readings
Solution Approach 1:
The patent performs preliminary automated analysis of CT images before the surgeon begins operative planning. The machine learning system pre-identifies tunnels, hardware, and abnormal widening, allowing the surgeon to build upon this prepared information rather than starting from scratch, thereby improving reliability while reducing the time required for comprehensive image analysis.
Solution Approach 2:
The patent substitutes manual visual analysis with an automated machine learning system that reliably identifies anatomical structures and abnormalities. This substitution improves operative planning reliability by eliminating human error in image interpretation while the automated processing occurs rapidly, minimizing time loss.
3Manufacturing precision
If existing tunnels and hardware are not accurately identified, then the surgical procedure can be performed quickly, but the risk of improper graft fixation and tensioning increases
Solution Approach 1:
The patent introduces a machine learning intermediary that processes CT images to automatically identify tunnels and hardware with high precision. This intermediary provides accurate anatomical information to the surgical planning process, ensuring precise tunnel positioning while allowing the actual surgical procedure to proceed efficiently once the planning is complete.
Solution Approach 2:
The patent performs preliminary automated identification of tunnels and hardware before the surgical procedure. This pre-analysis ensures that all anatomical structures are accurately located and documented, providing the precision needed for proper graft fixation and tensioning while allowing the surgical team to proceed efficiently with the actual operation based on this prepared information.
Data Source
AI summary
Disclosed are systems and methods for a computerized framework that provides novel mechanisms for the automatic identification of existing tunnels and hardware, which can be used for compiling of a preoperative and/or intraoperative plan for an anterior cruciate ligament (ACL) revision procedure. The operative plan, among other benefits, automatically avails surgeons with capabilities to locate the tunnels physically, and guides them in their revision ACL reconstruction procedure. According to some embodiments, the disclosed framework can generate synthetic ACL reconstruction CT images from CT images of patients without previous primary ACL reconstruction. The framework can generate realistic ACL reconstruction CTs, which can be used as input for training machine learning or deep learning models. Moreover, this can improve the accuracy, robustness and generalization capacity (e.g., identification of tunnels and hardware in MRIs and CTs) of the machine learning and deep learning based models for ACL tunnel segmentation.