ML-Based Tissue Conductivity Prediction for TTFields Transducer Placement
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Solution Overview
Problem
Determining precise transducer locations for tumor treating fields (TTFields) treatment is challenging due to the time-consuming and tedious process of identifying tissue types in medical images, which affects the effectiveness of TTFields delivery.
Innovation Solution
A machine learning model is trained using medical images and measured resistances from other subjects to predict conductivities for tissue types in a subject's medical image, allowing for the generation of optimized transducer locations for TTFields application without manual segmentation of tissue types.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual segmentation of tissue types is performed to determine conductivities for transducer placement, then treatment accuracy is improved, but time consumption and operational complexity increase significantly
Solution Approach 1:
The patent replaces manual segmentation and conventional conductivity determination methods with a machine learning model that automatically predicts tissue conductivities from medical images. The ML model processes images and outputs conductivity values without requiring manual tissue type identification, thereby maintaining treatment accuracy while dramatically reducing time consumption and operational complexity
Solution Approach 2:
The system enables self-service by allowing the machine learning model to autonomously determine tissue conductivities from medical images without requiring manual intervention for tissue segmentation. The model takes medical images as input and directly outputs conductivity predictions, making the process self-sufficient and eliminating the need for tedious manual analysis
2Measurement precision
If manual identification of tissue types is performed, then conductivity determination accuracy is improved, but device complexity and ease of operation deteriorate
Solution Approach 1:
The patent substitutes manual tissue identification operations with an automated machine learning-based system. The ML model automatically analyzes medical images and predicts tissue conductivities without requiring operators to manually segment or identify tissue types, thereby maintaining accuracy while significantly improving ease of operation
Solution Approach 2:
The machine learning model serves as an intermediary between medical images and conductivity determination. Instead of directly requiring manual tissue identification, the ML model acts as a mediator that processes images and outputs conductivity predictions, simplifying the operational workflow while preserving measurement precision
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 reduces computational time and improves treatment accuracy by tailoring TTFields delivery based on tissue conductivities, leading to more effective treatment planning and faster transducer placement.
Implementation Method 1
measured resistances from other subjects can be used to predict conductivities of tissue types for another subject
Data Source
AI summary
A method for generating at least one transducer location for delivering tumor treating fields to a subject is provided. The method includes obtaining a medical image of a subject, the medical image having a plurality of voxels, the medical image representing a plurality of tissue types of the subject, wherein at least one voxel is associated with each tissue type. The method further includes determining, using a trained machine learning model and the medical image of the subject, conductivities for the tissue types of the subject in the medical image, the trained machine learning model trained with medical images of a plurality of other subjects and resistances obtained from the application of tumor treating fields to the other subjects. The method further includes identifying a location of a tumor in the medical image and generating at least one transducer location for delivering tumor treating fields to the subject.