Lesion Prediction Model for Early Radiological Detection
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Solution Overview
Problem
Current radiological imaging methods rely on visual detection of lesions by radiologists, which often occurs after lesions have grown sufficiently large, leading to delayed detection and potentially more intensive and harmful treatments.
Innovation Solution
A method involving the use of a processor to train a model using pre-lesion and post-lesion images to predict the location of future lesions, allowing for early detection and localization through comparison of regions of interest in images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If conventional visual detection methods are used, then the detection process is simple and straightforward, but lesion detection is delayed until lesions become sufficiently large to be visible
Solution Approach 1:
The system performs preliminary analysis of radiological images to identify subtle features and patterns that precede visible lesion formation. By analyzing images before lesions become visually detectable and comparing them to subsequent images where lesions are confirmed, the system predicts future lesion locations, enabling early intervention before the lesions grow to a visible size.
2Loss of time
If more frequent patient exams are conducted, then earlier detection of lesions may be enabled, but the cost and burden on patients increases
Solution Approach 1:
The system replaces the mechanical approach of frequent physical exams with an automated computational analysis system. The processor analyzes existing radiological images using trained models to predict lesion development, eliminating the need for more frequent imaging exams while achieving the same early detection goal.
3Object-affected harmful factors
If lesions are detected later, then the detection process remains simple, but more intensive and harmful treatments are required
Solution Approach 1:
The system performs preliminary detection of sub-visual lesion indicators before they develop into visible lesions requiring intensive treatment. By predicting lesion locations in advance, the system enables early intervention with milder treatments, preventing the progression to stages where aggressive therapies are necessary.
4Reliability
If a model is trained to predict lesion locations, then early detection is enabled, but the system complexity increases
Solution Approach 1:
The system performs preliminary training using paired radiological images - images taken before lesion appearance and corresponding images taken after lesions become visible. The processor learns to identify precursors to lesion formation by comparing these paired images, enabling reliable prediction of future lesion locations while maintaining a manageable system architecture.
Data Source
AI summary
Various methods and systems are provided for identifying future occurrences of lesions in a patient. In one example, a method includes feeding first images collected from one or more patients prior to appearance of lesions and second images collected from the one or more patients after appearance of the lesions to a processor to train a model to predict a location of a future lesion. The method further includes inputting third images collected from a new patient to the processor to infer regions for future lesions, and displaying the inferred regions in a probability map at a display unit to indicate areas of increased likelihood of lesion occurrence.