Assessing clinical utility of machine learning and artificial intelligence approaches to analyze speech recordings in multiple sclerosis: A pilot study

Goldenberg, 2012, Multiple sclerosis review, Pharmacol. Ther., 37, 175 Dobson, 2019, Multiple sclerosis - a review, Eur. J. Neurol., 26, 27, 10.1111/ene.13819 Pavelek, 2021, Comparison of therapies in MS patients after the first demyelinating event in real clinical practice in the Czech republic: data from the national registry ReMuS, Front. Neurol., 11, 1833, 10.3389/fneur.2020.593527 McGinley, 2021, Diagnosis and treatment of multiple sclerosis: a review, JAMA, 325, 765, 10.1001/jama.2020.26858 Bjornevik, 2022, Longitudinal analysis reveals high prevalence of Epstein-Barr virus associated with multiple sclerosis, Science, 375, 296, 10.1126/science.abj8222 Krienke, 2021, A noninflammatory mRNA vaccine for treatment of experimental autoimmune encephalomyelitis, Science, 371, 145, 10.1126/science.aay3638 Thompson, 2018, Diagnosis of multiple sclerosis: 2017 revisions of the McDonald criteria, Lancet Neurol., 17, 162, 10.1016/S1474-4422(17)30470-2 Gerald, 1987, Multiple sclerosis: associated speech and language disorders, Aust. J. Hum. Commun. Disord., 15, 15, 10.3109/asl2.1987.15.issue-2.02 Rusz, 2018 Rusz, 2018, Characteristics of motor speech phenotypes in multiple sclerosis, Mult. Scler. Relat. Disord., 19, 62, 10.1016/j.msard.2017.11.007 Hartelius, 2000, Prevalence and characteristics of dysarthria in a multiple-sclerosis incidence cohort: relation to neurological data, Folia Phoniatrica Logop., 52, 160, 10.1159/000021531 Noffs, 2021, Speech metrics, general disability, brain imaging and quality of life in multiple sclerosis, Eur. J. Neurol., 28, 259, 10.1111/ene.14523 Waleed Salehi, 2020, Review on machine and deep learning models for the detection and prediction of Coronavirus, Mater. Today Proc., 33, 3896, 10.1016/j.matpr.2020.06.245 Salehi, 2021, A prospective and comparative study of machine and deep learning techniques for smart healthcare applications, Mob. Health Adv. Res. Appl., 163 Yousef, 2021, A comparative study of different machine learning techniques for brain tumor analysis, Mater. Today Proc., 10.1016/j.matpr.2021.03.303 Polman, 2011, Diagnostic criteria for multiple sclerosis: 2010 Revisions to the McDonald criteria, Ann. Neurol., 69, 292, 10.1002/ana.22366 Kurtzke, 1983, Rating neurologic impairment in multiple sclerosis: an expanded disability status scale (EDSS), Neurology, 33, 10.1212/WNL.33.11.1444 Gronwall, 1977, Paced auditory serial-addition task: a measure of recovery from concussion, Percept. Mot. Skills, 44, 367, 10.2466/pms.1977.44.2.367 Smith, 1973 Rusz, 2021, Guidelines for speech recording and acoustic analyses in dysarthrias of movement disorders, Mov. Disord., 36, 803, 10.1002/mds.28465 Čapek, 1939 Pollák, 2007, HMM-based phonetic segmentation in Praat environment, 537 Boersma Machač, 2010 Kuhn Kuhn, 2008, Building predictive models in R using the caret package, J. Stat. Software, 28, 1 Moro-Velazquez, 2021, Advances in Parkinson's Disease detection and assessment using voice and speech: a review of the articulatory and phonatory aspects, Biomed. Signal Process Control, 66, 10.1016/j.bspc.2021.102418 Rozenstoks, 2020, Automated assessment of oral diadochokinesis in multiple sclerosis using a neural network approach: effect of different syllable repetition paradigms, IEEE Trans. Neural. Syst. Rehabil. Eng. Publ. IEEE Eng. Med. Biol. Soc., 28, 32, 10.1109/TNSRE.2019.2943064 Vavougios, 2018, An electroglottographical analysis-based discriminant function model differentiating multiple sclerosis patients from healthy controls, Neurol. Sci., 39, 847, 10.1007/s10072-018-3267-8 Noffs, 2020, Acoustic speech analytics are predictive of cerebellar dysfunction in multiple sclerosis, Cerebellum Lond Engl, 19, 691, 10.1007/s12311-020-01151-5 Zhao, 2020, Ensemble learning predicts multiple sclerosis disease course in the SUMMIT study, Npj Digit Med., 3, 1 Seccia, 2021, Machine learning use for prognostic purposes in multiple sclerosis, Life, 11, 122, 10.3390/life11020122 Bates, 2003, Voxel-based lesion-symptom mapping, Nat. Neurosci., 6, 448, 10.1038/nn1050 Wilson, 2017, Lesion-symptom mapping in the study of spoken language understanding, Lang Cogn Neurosci., 32, 891, 10.1080/23273798.2016.1248984 Rusz, 2019, Brain volumetric correlates of dysarthria in multiple sclerosis, Brain Lang., 194, 58, 10.1016/j.bandl.2019.04.009 Clark, 2014, Clinical and imaging characterization of progressive spastic dysarthria, Eur. J. Neurol., 21, 368, 10.1111/ene.12271 Tykalova, 2017, Distinct patterns of imprecise consonant articulation among Parkinson's disease, progressive supranuclear palsy and multiple system atrophy, Brain Lang., 165, 1, 10.1016/j.bandl.2016.11.005 Rusz, 2021, Speech biomarkers in rapid eye movement sleep behavior disorder and Parkinson disease, Ann. Neurol., 90, 62, 10.1002/ana.26085 Volín, 2017 Time step settings. [Internet]. [cited 2021 Feb 24]. Available from: https://www.fon.hum.uva.nl/praat/manual/Time_step_settings___.html.