Predictive Model for Personalized Refractive Surgery Recommendations
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Solution Overview
Problem
LASIK surgeries often result in residual refractive errors and poor uncorrected visual acuity due to the lack of personalized recommendations, as the selection of surgery type and parameters is typically a manual process, and large amounts of data on laser eye surgeries are not readily available, making it challenging to determine suitable personalized laser eye surgery recommendations.
Innovation Solution
A computer system uses pre-operative eye characteristic and demographic data, combined with a predictive model formulated from other patients' data, to predict post-operative uncorrected visual acuity (UCVA) values through linear regression, matching patients to suitable refractive surgery types and recommending specific surgery parameters for improved outcomes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual selection process is used for LASIK surgery type, then surgeon expertise and flexibility are maintained, but personalization and precision of surgery selection deteriorate
Solution Approach 1:
A computer-based predictive model acts as an intermediary between the surgeon's expertise and the final surgery selection. The system processes patient data through linear regression algorithms to generate personalized surgery recommendations, while the surgeon retains final decision-making authority. This intermediary system enhances precision by objectively analyzing multiple patient parameters without replacing human judgment.
Solution Approach 2:
The manual mechanical process of surgery selection is partially replaced with an automated computational system. The predictive model automatically processes patient data, performs linear regression analysis, and generates surgery type recommendations based on statistical patterns from historical data, reducing reliance on purely manual assessment while maintaining surgeon oversight.
2Reliability
If personalized recommendations are implemented, then post-operative UCVA outcomes are improved, but data processing requirements and system complexity increase
Solution Approach 1:
The system performs preliminary analysis of patient data before surgery selection to predict potential outcomes. By pre-processing patient characteristics through linear regression models and generating predicted UCVA maps for different surgery types, the system enables informed decision-making before the actual surgical procedure, thereby improving outcome reliability.
Solution Approach 2:
The predictive model creates virtual copies of patient scenarios by generating simulated post-operative UCVA maps for different surgery types. These copied outcomes allow comparison of potential results without actual surgery, enabling personalized recommendations based on statistical patterns from historical patient data.
3Measurement precision
If multiple post-operative time periods are analyzed, then accuracy of UCVA prediction is improved, but measurement and data collection complexity increase
Solution Approach 1:
The system pre-collects and stores patient data at multiple post-operative time points (1 day, 1 week, 1 month) during routine clinical follow-ups. This preliminary data collection enables subsequent predictive analysis without requiring additional specialized measurements, as the data is gathered during standard care procedures.
Data Source
AI summary
Aspects extend to methods, systems, and computer program products for providing personalized surgery recommendations for eye patients. Surgery types, and surgery parameters can be recommended for a patient based on predicted post-operative UCVA for the patient if the surgery types and surgery parameters were to be used. Predicting post-operative UCVA can be handled as a regression problem based on patient demography and pre-operative examination details. In an additional aspect, surgery parameters are automatically determined and/or optimized for improved post-operative UCVA by including surgery parameters in a regression model.