Multimodal Digital Cognitive Assessment for Beta-Amyloid Prediction

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Solution Overview

Problem

Current methods for detecting beta-amyloid status, a biomarker for Alzheimer's disease, are expensive, invasive, and not universally available, requiring specialized facilities and equipment, limiting their accessibility and accuracy in diverse populations.

Innovation Solution

A multimodal digital cognitive assessment system using AI-enabled analysis of DCTclock, delayed verbal memory tests, temporal speech, and acoustic voice features, along with self-reported survey questions, to predict beta-amyloid status through a mobile device, providing a non-invasive, inexpensive, and widely applicable solution.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If PET scan or spinal tap is used to detect beta-amyloid status, then measurement precision is improved, but device complexity and cost increase substantially

Engineering Contradiction:

Improvebeta-amyloid status detection accuracyVSAvoidspecialized facilities and equipment requirements

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The patent replaces invasive mechanical procedures (spinal tap) and complex imaging equipment (PET scan) with a digital cognitive assessment system that uses software algorithms, machine learning models, and standard mobile devices to predict beta-amyloid status through analysis of cognitive performance patterns

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The patent creates a digital copy of cognitive assessment data from standard mobile devices and analyzes this copied information through machine learning models, eliminating the need for physical brain imaging or spinal procedures while maintaining predictive accuracy

Inventive Principle:

Principle #26Copying

2Measurement precision

If PET scan or spinal tap is used to detect beta-amyloid status, then measurement precision is improved, but loss of time increases

Engineering Contradiction:

Improvebeta-amyloid status detection accuracyVSAvoidtime required to perform assessment

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The patent extracts key cognitive performance features from standard mobile device data (response times, accuracy patterns, cognitive test results) and feeds these extracted features into machine learning models for rapid beta-amyloid status prediction, eliminating the time-consuming PET scanning or spinal tap procedures

Inventive Principle:

Principle #2Taking out (Extraction)

Solution Approach 2:

The patent performs preliminary cognitive assessments using standard mobile devices that can be administered quickly to patients, collecting data in advance that is then processed by machine learning models to predict beta-amyloid status without requiring time-intensive imaging or invasive procedures

Inventive Principle:

Principle #10Preliminary action

3Measurement precision

If specialized laboratory assessments are used to analyze blood samples, then measurement precision is improved, but ease of operation deteriorates

Engineering Contradiction:

Improvebeta-amyloid status detection accuracyVSAvoidrequirement for specialized medical facilities

Core Design Contradiction:

Measurement precisionVSEase of operation

Solution Approach 1:

The patent makes the beta-amyloid detection capability universal by using standard mobile devices that any clinician can operate, eliminating the need for specialized medical facilities, and allowing the same assessment to be performed in diverse settings including primary care clinics, patient homes, and research studies

Inventive Principle:

Principle #6Universality (Multi-functionality)

4Measurement precision

If current blood analysis methods are used, then measurement precision is improved, but adaptability deteriorates due to limited availability in diverse populations

Engineering Contradiction:

Improvebeta-amyloid status detection accuracyVSAvoidavailability across diverse populations

Core Design Contradiction:

Measurement precisionVSAdaptability or versatility

Solution Approach 1:

The patent changes the measurement parameters from requiring specialized laboratory equipment and invasive procedures to using digital cognitive assessment data collected on standard mobile devices, enabling the same beta-amyloid status prediction capability to be applied universally across diverse populations in various settings

Inventive Principle:

Principle #35Parameter changes

Data Source

PatentUS20250022550A1Systems and methods for predicting pet amyloid biomarker status using multimodal digital cognitive assessments

Publication Date: 2025.01.16 LINUS HEALTH INC

AI summary

Systems and methods are disclosed for predicting a biomarker status. A method of predicting a biomarker status comprises administering a battery of assessments to a patient; collecting multimodal data based on one or more responses to the battery of assessments from the patient; extracting one or more feature sets from the one or more response; providing the one or more feature sets to a trained machine learning model; predicting, using the trained machine learning model, a status of a biomarker of the patient; providing the prediction into a recommendation engine; determining one or more interventions based on the prediction, wherein the one or more interventions include values to the patient; and providing the one or more interventions as output.