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Digital biomarkers

A parameter, data technology, applied in bioinformatics, physical therapy or behavior, instrument, etc.

Pending Publication Date: 2022-03-11
F HOFFMANN LA ROCHE & CO AG
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Patient is able to sit without support but should never stand or walk without assistance

Method used

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Examples

Experimental program
Comparison scheme
Effect test

example 1

[0208] Example 1: Acquisition of data sets using a computer-implemented test for determining lung capacity (Test: Inspired Monster), central motor function test

[0209]

[0210] Covariates: 1: FVC (in liters), ICC = intraclass correlation coefficient

[0211] table 3

[0212] Realize the test of measuring lung volume on the mobile phone (iPhone); see Fig. 1 to figure 2 . The patient should let out a loud "ah" sound so that the monster will reach the finish line within 30 seconds. The phone needs to be placed at arm's length on the table in front of the patient. The louder the "ah", the faster the monster runs. A speech detector was used to detect sustained vocalizations and segment them each time an "ah" pause occurred. The patient needs to play the game with the aim of achieving the longest tone duration. Test results are expressed as the longest duration in seconds. A standard pitch variability is determined.

[0213] figure 2 The correlation of the forced vol...

example 2

[0214] Example 2: Acquisition of data sets using a computer-implemented test for determining finger strength by pressure measurement (Test: Knocking Monster), central motor function test

[0215]

[0216]

[0217] Covariates: 1:MFM-18, 2:MFM_D3

[0218] Table 4

[0219] A test of pressure measurement for finger strength by pressure measurement on a mobile phone (iPhone); see Figure 3 to Figure 4. The patient taps the monster with the index finger to make the monster return to its lair. The phone should be placed on the table. The monster should be tapped as fast as possible. The patient must select the preferred hand to use. Patients were required to play the game for 30 seconds to obtain the maximum pressure of a single tap, the median time to tap the monster after the monster appeared, and the total number of monsters tapped in the 30-second period. Determine the standard deviation of maximum pressure, the median of maximum pressure, the maximum pressure of a si...

example 3

[0221] Example 3: Using a computer-implemented test for determining the synchronicity of 2 fingers (fist and index finger of the same hand) by measuring the time lag between first and second fingers touching the screen for all detected double contacts (Test: squeeze tomato), remote motor function test to obtain data sets

[0222]

[0223]

[0224] Covariates: 1: MFM-17, 18, 19, 22; 2: MFM_D3; 3: Total 32 = MFM total score; 4: MFM-17

[0225] ICC: Intraclass Correlation Coefficient, DTA: Double Touch Asynchrony, P_GA: Pinch Gap Schedule 5

[0226] The test for double-touch asynchrony (DTA) is implemented on the mobile phone (iPhone); see Figure 5 to Figure 6 . The patient should pinch as many tomatoes as possible between the thumb and index finger of the designated hand within 30 seconds. The phone needs to be placed on the table. A preferred hand needs to be selected. Patients are required to play a game for 30 seconds.

[0227] Figure 6 Correlation of the clini...

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PUM

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Abstract

Aspects described herein relate to the field of disease tracking and diagnosis. Specifically, these aspects relate to a method of assessing muscle dysfunction and in particular spinal muscular atrophy (SMA) in a subject, the method comprising the steps of determining at least one parameter of a sensor measurement dataset from the subject using a mobile device, and comparing the determined at least one parameter with a reference, as a result, the muscle disability, in particular SMA, will be evaluated. Aspects described herein also relate to a mobile device comprising a processor, at least one pressure sensor and a database, and software tangibly embedded in the device and performing the method of the invention when running on the device; and the use of such a device for assessing muscle disability and in particular SMA.

Description

technical field [0001] Aspects described herein relate to the field of disease tracking and supporting diagnostic processes, in particular the field of assessing a subject for muscular incapacitation, particularly spinal muscular atrophy (SMA). Aspects described herein also relate to a mobile device comprising a processor, at least one pressure sensor and a database, and software tangibly embedded in the device and which, when run on the device, performs and the use of such a device for assessing muscle disability and in particular SMA. Aspects described herein also relate to a computer-implemented method of using machine learning to predict a clinical anchor score in a subject, particularly a patient with a muscle disability and in particular SMA. Background technique [0002] Spinal muscular atrophy (SMA) in its broadest sense describes a spectrum of inherited and acquired central nervous system (CNS) disorders characterized by progressive loss of motor neurons in the spi...

Claims

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Application Information

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IPC IPC(8): A61B5/00A61B5/22
CPCA61B5/4076A61B5/4082A61B5/407A61B5/224A61B5/225A61B5/4842A61B5/4848A61B5/742A61B5/7445A61B5/7475G16H50/20A61B5/1124G16H20/30G16H40/63G16B50/20A61B5/0022A61B5/4538A61B5/4839A61B5/6898A61B5/7275
Inventor C·戈森斯M·林德曼F·利普斯梅尔D·沃尔夫
Owner F HOFFMANN LA ROCHE & CO AG
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