Surgical Plan Prediction Using Registered Pre/Postoperative Images
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Solution Overview
Problem
Existing surgical planning systems lack the ability to accurately predict surgical outcomes based on preoperative and postoperative images, leading to potential inaccuracies in instrument placement and tool selection, which can affect the effectiveness of surgical procedures.
Innovation Solution
A method and system that utilize image registration and artificial intelligence to measure differences between preoperative and postoperative images, generating a function to predict surgical outcomes and update surgical plans accordingly, incorporating features like implants and anatomical elements, and providing notifications or recommendations for tool selection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If surgical planning is based on surgeon preference and patient parameters without predictive analysis, then the surgical plan can be created quickly, but the accuracy of instrument placement and tool selection is compromised
Solution Approach 1:
The system performs predictive analysis of surgical outcomes before the actual surgery by comparing preoperative and postoperative images. This preliminary action allows surgeons to anticipate potential placement errors and adjust their planning accordingly, improving accuracy without adding complexity during the actual surgical procedure
Solution Approach 2:
The system establishes a feedback loop by comparing planned surgical outcomes with actual postoperative results. This feedback mechanism enables continuous improvement of surgical planning accuracy by learning from previous procedures and adjusting predictions for future cases
2Reliability
If traditional surgical planning methods are used without image-based prediction, then the surgical workflow remains simple, but the reliability of surgical outcomes cannot be ensured
Solution Approach 1:
The system captures and analyzes the difference between planned and actual surgical outcomes in advance, storing this information as training data. This preliminary capture of variability information prevents data loss and enables reliable prediction of surgical outcomes for future procedures
Solution Approach 2:
The system creates a digital model of surgical variability by copying and analyzing the differences between preoperative plans and postoperative actuals. This digital representation preserves information about surgical variability without requiring physical storage of all surgical data
Data Source
AI summary
Systems and methods for predicting surgical outcomes are provided. A surgical plan comprising information about a planned surgery and at least one preoperative image depicting a planned surgical result and at least one postoperative image depicting an actual surgical result resulting from execution of the planned surgery may be received. The postoperative image may be registered to the preoperative image. One or more features may be automatically identified in each of the postoperative image and the preoperative image. A difference may be automatically measured in at least one parameter of each of the one or more features to yield training data. A function for predicting the difference may be generated using artificial intelligence and based on the training data. The function may be applied to an unexecuted surgical plan.