Photodynamic Therapy Feedback Control for Real-Time Dosing
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Solution Overview
Problem
Current photodynamic therapy methods require repeated trial-and-error adjustments to optimize light dose and photosensitizer effectiveness across different living organisms, leading to inefficiencies in time, labor, and material usage, and lack a mechanism to control dosing in real-time without operator intervention.
Innovation Solution
A method that simultaneously measures and controls photodynamic therapy irradiation doses using a feedback mechanism with machine learning, pulsed laser sources, and imaging systems to determine viability rates and adjust laser fluence in real-time, eliminating the need for cytotoxicity tests and optimizing treatment efficacy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If trial-and-error adjustments are made to optimize light dose and photosensitizer effectiveness for each patient, then treatment effectiveness is improved, but time consumption and material costs increase significantly
Solution Approach 1:
The system performs preliminary characterization of the patient's tissue optical properties (absorption coefficient μa and reduced scattering coefficient μs') before treatment optimization. This preliminary measurement allows the system to predict the optimal light dose and photosensitizer concentration, eliminating the need for time-consuming trial-and-error adjustments during the actual treatment planning phase.
Solution Approach 2:
The system implements a feedback mechanism where the measured optical properties and treatment responses are continuously monitored. The measured values of μa and μs' are fed back into the optimization algorithm to adjust the light dose and photosensitizer concentration in real-time, ensuring treatment effectiveness without requiring multiple separate trial experiments.
2Measurement precision
If repeated cytotoxicity tests and animal experiments are conducted to determine optimal dosing, then dosing accuracy is improved, but material costs and labor requirements increase
Solution Approach 1:
The system replaces repeated physical cytotoxicity tests and animal experiments with an in silico optimization model based on measured optical properties. The Monte Carlo simulation and optimization algorithm calculate the optimal dosing parameters computationally, eliminating the need for repeated consumption of photosensitizer materials and biological samples in trial experiments.
Solution Approach 2:
The system changes the approach from empirical parameter determination through repeated testing to parameter calculation based on measured optical properties (μa and μs'). By measuring these optical parameters once and using them in the optimization model, the system achieves accurate dosing determination without repeated material consumption in tests.
3Adaptability or versatility
If operator intervention is required for dosing adjustments during therapy, then treatment flexibility is maintained, but automation level and efficiency decrease
Solution Approach 1:
The system implements self-service automation where the optimization algorithm automatically determines and adjusts the optimal light dose and photosensitizer concentration based on measured optical properties. The system performs the dosing optimization autonomously without requiring operator intervention, while still maintaining adaptability to individual patient characteristics through the measurement-based optimization approach.
Solution Approach 2:
The automated feedback loop continuously monitors treatment response and adjusts dosing parameters based on measured optical properties and treatment outcomes. This closed-loop feedback system maintains treatment flexibility and adaptability while fully automating the dosing adjustment process, eliminating the need for manual operator intervention.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enables precise, time-efficient, and cost-effective photodynamic therapy by quantitatively determining unhealthy cell viability and adjusting laser parameters to optimize treatment outcomes without the need for lengthy laboratory processes.
Implementation Method 1
pulsed laser sources
Implementation Method 2
photodynamic application and/or therapy irradiation (dosing)
Implementation Method 3
measures simultaneously how effective the photodynamic application and/or therapy irradiation (dosing) amounts
Implementation Method 4
imaging systems to determine viability rates
Data Source
AI summary
A method that simultaneously measures how effective the photodynamic application and/or therapy irradiation amounts and the therapy efficiency are on the unit cell-organism or in cell-microorganism communities is provided. The method uses a feedback mechanism without the need for an operator during therapy in cases where dosing is insufficient or excessive.