Surgical Outcome Prediction Using Perioperative Image Analytics
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Solution Overview
Problem
Existing predictive models for surgical outcomes, particularly in spine surgery, are limited by their reliance on patient demographic and clinical information without incorporating image-based data, leading to uncertainty, variability, and resource wastage due to inappropriate treatment choices.
Innovation Solution
A clinical decision support platform that utilizes data analytics for predictive modeling, integrating perioperative images and data to derive image-based analytic features, predicting surgical outcomes using machine learning and AI techniques, and providing patient-specific treatment recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If clinicians manually integrate all available healthcare data into decision-making processes, then patient-specific decision accuracy may improve, but the time and resource requirements become unreasonably high
Solution Approach 1:
The patent replaces manual data integration (mechanical human effort) with an automated clinical decision support system that uses machine learning models and natural language processing to analyze healthcare data, extract relevant features, and generate treatment recommendations automatically
Solution Approach 2:
The system enables self-service by allowing the computational model to autonomously process and integrate healthcare data without requiring clinician intervention for each data point, while still providing actionable insights that improve decision accuracy
2Measurement precision
If image-based information is incorporated into predictive modeling, then patient outcome prediction accuracy improves, but the system complexity increases
Solution Approach 1:
The patent segments the complex analysis task into distinct components: image preprocessing, feature extraction using CNNs, clinical data processing, and outcome prediction, allowing each module to be optimized independently and reducing overall system complexity
Solution Approach 2:
The patent introduces intermediate feature representations that bridge raw image data and final predictions, using learned feature vectors as mediators that capture essential patterns while simplifying the relationship between input images and output predictions
3Reliability
If multiple therapeutic modalities are considered for heterogeneous patients, then treatment effectiveness improves, but the difficulty of selecting appropriate treatments increases
Solution Approach 1:
The patent transforms the treatment selection problem into a parameter optimization problem by representing different therapeutic modalities as distinct treatment plans with associated parameters, allowing the system to select optimal parameters based on patient-specific features and predicted outcomes
Solution Approach 2:
The system incorporates feedback mechanisms where predicted outcomes for different treatment options guide the selection process, allowing clinicians to compare expected benefits and risks across multiple modalities before making a decision
Data Source
AI summary
A device may receive a set of perioperative images including a set of pre-operative images depicting one or more anatomical structures of a surgical candidate. The set of pre-operative images may be processed using image analysis techniques to determine a first set of quantitative measures related to the anatomical structure(s) of the surgical candidate. The device may use a data model that has been trained based on perioperative data associated with a patient cohort sharing clinical characteristics with the surgical candidate to predict outcomes from one or more therapeutic options for the surgical candidate based on the first set of quantitative measures and a second set of quantitative measures related to a profile associated with the surgical candidate. Based on the predicted outcomes, the device may provide, to a client device, a recommendation relating to the therapeutic options for the surgical candidate and information to support the recommendation.