CT-Based Lung Metrics for Personalized COPD Therapy Planning
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Solution Overview
Problem
Current treatments for chronic obstructive pulmonary disease (COPD) are inadequate, lacking a cure and associated with significant complications, and there is a need for innovative, personalized treatment planning and monitoring methods.
Innovation Solution
Utilizing machine learning algorithms to analyze computed tomography data and generate lung metrics for predicting patient responses to treatments, such as endobronchial implants, and monitoring treatment outcomes to personalize and improve therapeutic efficacy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional treatments are used for COPD, then treatment is provided, but treatment effectiveness is limited and complications occur
Solution Approach 1:
The system performs preliminary actions by using machine learning algorithms to predict patient response to treatment before treatment is administered. This allows for pre-screening and selection of patients most likely to benefit from specific interventions, thereby improving treatment effectiveness while avoiding complications in patients unlikely to respond positively.
Solution Approach 2:
The invention changes the parameter of treatment selection from conventional one-size-fits-all approaches to personalized treatment plans based on predicted response. By analyzing patient-specific parameters and predicting treatment outcomes, the system enables selection of optimal treatment parameters for each individual patient, improving reliability while reducing harmful effects.
2Reliability
If personalized treatment planning is implemented, then treatment effectiveness improves, but system complexity increases
Solution Approach 1:
The invention replaces complex manual treatment planning processes with automated machine learning algorithms. Instead of relying on clinicians to manually analyze patient data and predict treatment responses, the system uses computational models to automatically generate personalized treatment plans, improving effectiveness while managing complexity through automation.
Solution Approach 2:
The system creates a computational model (a copy) of the patient's physiological response to treatment based on their imaging data. This virtual model allows prediction of treatment outcomes without requiring actual treatment administration, enabling personalized planning while keeping the physical system relatively simple.
3Reliability
If monitoring systems are implemented to detect issues early, then patient outcomes improve, but measurement and detection complexity increases
Solution Approach 1:
The monitoring system performs preliminary detection by predicting potential treatment failures or complications before they manifest clinically. By analyzing imaging data and predicting response, the system identifies patients who may experience adverse outcomes, allowing for early intervention or treatment modification before actual harm occurs.
Data Source
AI summary
Methods for planning, predictive modeling, and monitoring therapies for pulmonary diseases are provided. In some embodiments, a method for planning a treatment for a patient having a pulmonary disease includes receiving patient data including computed tomography (CT) data of a lung of the patient. The method can include generating a set of lung metrics by inputting the patient data into a first machine learning algorithm. The method can also include predicting a response of the patient to treatment for the pulmonary disease by inputting the set of lung metrics into a second machine learning algorithm. The method can further include evaluating whether the patient is a candidate for the treatment for the pulmonary disease, based on the predicted response. The method can further include evaluating whether the patient has benefited following treatment and whether additional treatment is warranted.