PCNL Surgical Planning Using Predictive Patient Data Models
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Solution Overview
Problem
Medical procedures such as percutaneous nephrolithotomy (PCNL) are challenging to plan due to numerous influencing factors, leading to high complication rates and variability in duration, required instruments, expertise level, and personnel, with each case being unique based on patient-specific factors like obesity, calyx proximity to organs, and stone characteristics.
Innovation Solution
A computer-implemented method using a machine learning algorithm to predict planning data for PCNL procedures by analyzing historical patient, task, and planning data, providing user recommendations on staffing, equipment, and procedural adjustments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual planning is used for PCNL procedures, then flexibility and adaptability to unique patient cases is maintained, but planning quality and consistency deteriorate due to high complexity and numerous influencing factors
Solution Approach 1:
A prediction model acts as an intermediary system between the complex patient data and the planning process. The model processes numerous influencing factors (patient anatomy, stone characteristics, procedural parameters) and outputs structured planning recommendations, thereby reducing the cognitive load on planners while improving consistency and quality of planning outcomes.
Solution Approach 2:
The manual planning process is partially replaced with an automated prediction model that uses machine learning algorithms. The model substitutes human cognitive processing with computational analysis, enabling consistent evaluation of complex factors without human bias or variability, thus improving planning reliability.
2Measurement precision
If comprehensive planning data is collected for all patient factors, then planning accuracy improves, but data processing time and resource requirements increase
Solution Approach 1:
The prediction model transforms raw, diverse patient data (imaging measurements, demographic parameters, procedural variables) into standardized planning parameters. By changing the representation of data from its original complex form to optimized planning parameters, the system maintains high accuracy while reducing processing requirements for subsequent planning decisions.
Solution Approach 2:
The system performs preliminary data processing and feature extraction automatically before the planning stage. Historical data is pre-processed to identify patterns and relationships, and the prediction model pre-calculates planning recommendations based on input data, thereby reducing the time required for actual planning decision-making.
3Reliability
If prediction models are trained on historical data, then planning recommendations become more accurate, but the system complexity and training requirements increase
Solution Approach 1:
The prediction model is designed to be self-updating through continuous learning from new procedural data. The system automatically incorporates outcomes from performed procedures to refine its predictions, eliminating the need for manual retraining by experts while improving accuracy over time. This self-service capability reduces the operational complexity of maintaining an accurate prediction system.
Data Source
AI summary
Computer-implemented method for determining planning data for a surgical process of a subject in a urology procedure, comprising: providing, by a processor, a prediction model trained to predict planning data based on at least one of: historical patient data, historical task data, historical planning data, current patient data and current task data (S100); obtaining, by the processor, at least one of: current patient data and current task data (S200); inputting the at least one of the current patient data and current task data to the prediction model in order to determine planning data (S300); providing a user recommendation on planning of a urology procedure planning, such as a PCNL procedure, by the processor, based on the determined planning data (S400).