Classifying neurological disease status using deep learning
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Solution Overview
Problem
Deep learning techniques for classifying neurological diseases using 3D medical scans face challenges such as overfitting and lack of interpretability, particularly in diagnosing conditions like Alzheimer's disease, due to the large number of voxels and limited labels per scan, and the 'black box' nature of these models.
Innovation Solution
A method and system utilizing a trained artificial neural network (ANN) that processes longitudinal patient data, including MRI, CT, and PET scans, to classify neurological diseases by partitioning data at the patient level, preventing overfitting, and identifying the most predictive image regions through techniques like class activation maps.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep learning techniques are used to analyze 3D medical scans, then classification accuracy is improved, but overfitting occurs due to the large number of voxels and limited labels per scan
Solution Approach 1:
The patent segments the 3D volumetric data into multiple 2D slices, transforming the problem from analyzing a single large 3D volume to analyzing multiple smaller 2D cross-sections. This segmentation reduces the computational complexity and the number of parameters the deep learning model must learn, thereby mitigating overfitting while maintaining classification accuracy through the cumulative information from multiple slices.
2Measurement precision
If deep learning models are used for disease classification, then diagnostic accuracy is improved, but interpretability deteriorates due to the black box nature of these models
Solution Approach 1:
The patent introduces visualization techniques as an intermediary between the deep learning model and the clinician. By generating visual representations that highlight the regions and features most influential in the model's classification decision, these intermediaries make the black box model's reasoning process transparent and interpretable, allowing clinicians to understand and trust the diagnostic results.
3Reliability
If longitudinal patient data is processed, then classification reliability is improved, but data complexity increases
Solution Approach 1:
The patent applies preliminary data processing and feature extraction steps before feeding longitudinal data into the deep learning model. By pre-processing the longitudinal scans to extract relevant features and reduce data dimensionality in advance, the system simplifies the input data structure while preserving the temporal and diagnostic information needed for reliable classification.
Data Source
AI summary
A method for classifying neurological disease status is described. The method includes acquiring, by a data preprocessor logic, patient image data. The method further includes generating, by a trained artificial neural network (ANN), a classification output based, at least in part, on the patient image data. The classification output corresponds to a neurological disease status of the patient. The trained ANN is trained based, at least in part, on longitudinal source data.