Biomarker-Guided Surgical Parameter Adjustment for Early PAL Prediction
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Solution Overview
Problem
Current surgical technologies lack adequate monitoring and predictive capabilities for complications such as adhesions, blood perfusion difficulties, tissue irregularities, and hemostasis issues, leading to prolonged procedures and post-surgical complications like anastomosis leaks, which are difficult to detect early.
Innovation Solution
A surgical instrument system that monitors user inputs and environmental data, using sensors to adjust settings based on patient-specific and surgical-environment-specific inputs, predicting potential complications and providing adaptive control to minimize risks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If surgical procedures are performed without adequate monitoring of biomarkers, then the surgical procedure can be completed, but complications such as adhesions, blood perfusion difficulties, tissue irregularities, and hemostasis issues may occur leading to prolonged procedures and post-surgical complications
Solution Approach 1:
The system performs preliminary monitoring of biomarkers (such as adhesion markers, perfusion markers, and hemostasis markers) before complications fully develop. By detecting early signs of potential issues through continuous biomarker monitoring, the system enables preventive actions to be taken before complications arise, thereby avoiding prolonged surgical procedures.
Solution Approach 2:
The system implements real-time feedback through continuous monitoring of surgical parameters and biomarkers. The monitoring system provides immediate feedback to surgeons about the patient's physiological state, allowing for timely adjustments to surgical technique and preventing complications that would otherwise prolong the procedure.
2Measurement precision
If traditional surgical monitoring methods are used, then the surgical procedure can be performed, but early detection of post-surgical complications like anastomosis leaks is difficult
Solution Approach 1:
The system uses biomarkers as intermediary indicators to detect complications such as anastomosis leaks. Instead of directly monitoring for leaks, the system monitors biochemical markers in the patient's system that change in response to leak conditions, providing indirect but reliable detection with simpler sensing requirements.
Solution Approach 2:
The monitoring system is designed to detect multiple types of complications (adhesions, perfusion issues, hemostasis problems, and anastomosis leaks) using a unified platform that monitors various biomarkers simultaneously, reducing the need for separate specialized monitoring systems for each complication type.
3Productivity
If surgical instrument settings are fixed and not adaptive, then the instrument is easier to operate, but it cannot optimize performance for patient-specific and surgical-environment-specific conditions
Solution Approach 1:
The surgical instrument system dynamically adjusts its operational parameters based on real-time monitoring of surgical conditions and patient response. The system transitions from static, pre-programmed settings to dynamic, adaptive control that automatically modifies instrument behavior according to the actual surgical environment and patient physiology.
Solution Approach 2:
The system incorporates automated decision-making capabilities that allow the surgical instrument to self-adjust its parameters based on monitored data. The control system processes sensor inputs and automatically optimizes instrument settings without requiring constant manual intervention from the surgeon, thereby improving efficiency while maintaining ease of operation.
Data Source
AI summary
A surgical computing system may receive measurement data from at least one sensing system. The measurement data may be associated with a set of patient biomarkers of a patient. A surgical computing system may obtain a set of patient parameters associated with the patient. A surgical computing system may generate vectorized patient data based on at least the measurement data and a set of patient-specific parameters. A surgical computing system may use a predictive model to predict an occurrence of a prolonged air leak (PAL) based at least on the vectorized patient data. A surgical computing system may generate a set of recommendations for preventing the PAL.