Mobile Device Distal Motor Test for Neurological Disease Staging
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Solution Overview
Problem
Current methods for staging neurological diseases like multiple sclerosis, Huntington's disease, and spinal muscular atrophy are cumbersome, subjective, and often require specialized equipment, making them inefficient for widespread use and accurate disease management.
Innovation Solution
A computer-implemented method and system using a mobile device with a touchscreen display to administer a distal motor test, extracting digital biomarker feature data from user inputs, and calculating clinical parameters such as EDSS, FVC, or TMS, leveraging acceleration data and statistical analysis for objective disease progression assessment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional staging methods are used for neurological diseases, then diagnostic accuracy can be maintained through expert judgment, but the process becomes cumbersome and requires specialized equipment and facilities
Solution Approach 1:
The patent replaces traditional mechanical/physical testing equipment with a mobile computing device that uses software-based assessments, accelerometers, and digital biomarkers to perform disease staging, eliminating the need for specialized medical equipment while maintaining diagnostic capability
Solution Approach 2:
The patent creates digital copies of clinical assessment functions by implementing staging algorithms and analysis models on mobile devices, allowing the same diagnostic capabilities to be replicated across multiple portable devices without requiring original specialized equipment
2Measurement precision
If traditional clinical staging is performed by medical specialists, then diagnostic precision can be maintained, but the process becomes subjective and time-consuming
Solution Approach 1:
The patent implements automated feedback loops where the mobile device continuously collects data from sensors and user interactions, processes this information through analysis models, and provides real-time or near-real-time disease staging results, eliminating the time delay inherent in manual specialist evaluation
Solution Approach 2:
The patent transforms subjective clinical judgment into objective quantitative measurements by collecting digital biomarker data from accelerometers, touchscreen interactions, and sensor inputs, then processing these parameters through algorithms to generate standardized staging scores that are both precise and rapidly computable
3Measurement precision
If specialized equipment is used for determining clinical parameters like forced vital capacity, then measurement accuracy is improved, but accessibility and ease of use are reduced
Solution Approach 1:
The patent makes the mobile device universal by enabling it to perform multiple diagnostic functions including but not limited to forced vital capacity measurement, distal motor function assessment, and various other neurological evaluations, replacing the need for multiple specialized devices with a single multi-functional platform
Data Source
AI summary
A computer-implemented method for quantitatively determining a clinical parameter which is indicative of the status or progression of a disease, comprises: providing a distal motor test to a user of a mobile device, the mobile device having a touchscreen display, wherein providing the distal motor test to the user of the mobile device comprises: causing the touchscreen display of the mobile device to display a test image; receiving an input from the touchscreen display of the mobile device, the input indicative of an attempt by a user to place a first finger on a first point in the test image and a second finger on a second point in the test image, and to pinch the first finger and the second finger together, thereby bringing the first point and the second point together; and extracting digital biomarker feature data from the received input wherein, either: (i) the extracted digital biomarker feature data is the clinical parameter, or (ii) the method further comprises calculating the clinical parameter from the extracted digital biomarker feature data.