Interpretable Multimodal Indexes for Early ALS Progression Tracking
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Solution Overview
Problem
Existing deep learning models require large amounts of labeled data for new tasks, which is labor-intensive and time-consuming, and current clinical methods for tracking ALS progression, such as the ALSFRS-R scale, lack sensitivity in early stages and granularity.
Innovation Solution
A parallel input, parallel output (PIPO) AI system using a Transformer architecture with self-attention mechanisms and human-in-the-loop (HITL) active learning for core set discovery to identify efficacious biomarkers from multimodal speech data, integrating human knowledge to accelerate model training with minimal data annotation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep learning models are used to track ALS progression, then measurement precision can be improved, but the quantity of labeled data required increases significantly
Solution Approach 1:
The system performs preliminary action by pre-training the Transformer model on large amounts of unlabeled speech data before fine-tuning on labeled clinical data. This allows the model to learn general speech patterns and features in advance, reducing the amount of labeled data needed for the specific ALS detection task.
Solution Approach 2:
The patent introduces an intermediary approach by using semi-supervised learning where a small amount of labeled data guides the model after pre-training on unlabeled data. This intermediary labeled dataset acts as a bridge between the large unlabeled corpus and the final specialized model, reducing the overall labeling burden.
2Measurement precision
If more labeled data is collected to improve model performance, then measurement precision improves, but loss of time increases due to annotation requirements
Solution Approach 1:
The system applies partial action by using a small subset of labeled data for fine-tuning after pre-training, rather than requiring comprehensive labeling of all available speech data. This partial labeling approach achieves sufficient model performance without the time cost of complete data annotation.
Solution Approach 2:
By performing pre-training on unlabeled data beforehand, the system prepares the model in advance so that minimal subsequent labeling is needed. This preliminary action on abundant unlabeled data eliminates the need for time-consuming annotation of large datasets.
3Ease of operation
If clinical scales like ALSFRS-R are used for monitoring, then ease of operation is maintained, but measurement precision deteriorates in early disease stages
Solution Approach 1:
The patent substitutes the mechanical manual assessment process with an automated AI system that analyzes speech recordings. The Transformer model automatically extracts biomarkers from speech data, replacing the manual clinical scale administration and scoring process while providing superior early detection capability.
Solution Approach 2:
The system enables self-service by allowing automatic analysis of patient speech recordings without requiring manual clinical assessment. The AI model independently processes speech data, extracts features, and generates progression estimates, reducing dependency on manual intervention while improving early-stage detection precision.
Data Source
AI summary
A computer-implemented method of generating interpretable, composite marker indexes that are discriminative and noise-robust is provided. The method comprises storing remotely collected multimodal digital markers from a first cohort and a second cohort. The method further comprises grouping multicollinear features in the multimodal digital markers into clusters, and then selecting representative features for the clusters for multiple classification tasks that require discrimination between the first cohort and the second cohort. The method further comprises linearly combining the representative features into an interpretable, composite marker index such that relative contributions of each of the representative features to the interpretable, composite marker index are known.