Assessing clinical utility of machine learning and artificial intelligence approaches to analyze speech recordings in multiple sclerosis: A pilot study
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00064165%3A_____%2F22%3A10448729" target="_blank" >RIV/00064165:_____/22:10448729 - isvavai.cz</a>
Alternative codes found
RIV/00216208:11110/22:10448729 RIV/00216208:11320/22:10448729 RIV/00216208:11210/22:10448729 RIV/68407700:21230/22:00360472
Result on the web
<a href="https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=6FRuBJNuIb" target="_blank" >https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=6FRuBJNuIb</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.compbiomed.2022.105853" target="_blank" >10.1016/j.compbiomed.2022.105853</a>
Alternative languages
Result language
angličtina
Original language name
Assessing clinical utility of machine learning and artificial intelligence approaches to analyze speech recordings in multiple sclerosis: A pilot study
Original language description
Background: An early diagnosis together with an accurate disease progression monitoring of multiple sclerosis is an important component of successful disease management. Prior studies have established that multiple sclerosis is correlated with speech discrepancies. Early research using objective acoustic measurements has discovered measurable dysarthria.Method: The objective was to determine the potential clinical utility of machine learning and deep learning/AI approaches for the aiding of diagnosis, biomarker extraction and progression monitoring of multiple sclerosis using speech recordings. A corpus of 65 MS-positive and 66 healthy individuals reading the same text aloud was used for targeted acoustic feature extraction utilizing automatic phoneme segmentation. A series of binary classification models was trained, tuned, and evaluated regarding their Accuracy and area-under-the-curve.Results: The Random Forest model performed best, achieving an Accuracy of 0.82 on the validation dataset and an area-under-the-curve of 0.76 across 5 k-fold cycles on the training dataset. 5 out of 7 acoustic features were statistically significant.Conclusion: Machine learning and artificial intelligence in automatic analyses of voice recordings for aiding multiple sclerosis diagnosis and progression tracking seems promising. Further clinical validation of these methods and their mapping onto multiple sclerosis progression is needed, as well as a validating utility for English-speaking populations.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
60203 - Linguistics
Result continuities
Project
<a href="/en/project/LX22NPO5107" target="_blank" >LX22NPO5107: National institute for Neurological Research</a><br>
Continuities
V - Vyzkumna aktivita podporovana z jinych verejnych zdroju
Others
Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Computers in Biology and Medicine
ISSN
0010-4825
e-ISSN
1879-0534
Volume of the periodical
148
Issue of the periodical within the volume
September
Country of publishing house
US - UNITED STATES
Number of pages
9
Pages from-to
105853
UT code for WoS article
000862542100002
EID of the result in the Scopus database
2-s2.0-85134675274