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These findings could help facilitate the development of remote screening and monitoring tools for PD. In this study, 2759 telephone-quality voice recordings from 1483 PD and 15\u2009321 recordings from 8300 HC participants were analyzed. To account for variations in phonetic backgrounds, data were acquired from seven countries. A statistical framework for analyzing voice was developed, whereby 307 dysphonia measures that quantify different properties of voice impairment, such as breathiness, roughness, monopitch, hoarse voice quality, and exaggerated vocal tremor, were computed. Feature selection algorithms were used to identify robust parsimonious feature subsets, which were used in combination with a random forests (RFs) classifier to accurately distinguish PD from HC. The best tenfold cross-validation performance was obtained using Gram-Schmidt orthogonalization and RF, leading to mean sensitivity of 64.90% (standard deviation, SD, 2.90%) and mean specificity of 67.96% (SD 2.90%). This large scale study is a step forward toward assessing the development of a reliable, cost-effective, and practical clinical decision support tool for screening the population at large for PD using telephone-quality voice.<\/jats:p>","DOI":"10.1121\/1.5100272","type":"journal-article","created":{"date-parts":[[2019,5,9]],"date-time":"2019-05-09T15:16:17Z","timestamp":1557414977000},"page":"2871-2884","update-policy":"https:\/\/doi.org\/10.1063\/aip-crossmark-policy-page","source":"Crossref","is-referenced-by-count":73,"title":["Developing a large scale population screening tool for the assessment of Parkinson's disease using telephone-quality voice"],"prefix":"10.1121","volume":"145","author":[{"given":"Siddharth","family":"Arora","sequence":"first","affiliation":[{"name":"Somerville College, University of Oxford 1 , Oxford, OX2 6HD, United Kingdom"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ladan","family":"Baghai-Ravary","sequence":"additional","affiliation":[{"name":"Aculab PLC 2 , Milton Keynes, MK1 1PT, United Kingdom"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Athanasios","family":"Tsanas","sequence":"additional","affiliation":[{"name":"Usher Institute of Population Health Sciences and Informatics, Medical School, University of Edinburgh 3 , Edinburgh, EH16 4UX, United Kingdom"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"231","published-online":{"date-parts":[[2019,5,9]]},"reference":[{"key":"2023071921075646200_c1","doi-asserted-by":"publisher","first-page":"e1528","DOI":"10.1212\/WNL.0000000000006366","article-title":"Smartphone motor testing to distinguish idiopathic REM sleep behavior disorder, controls, and PD","volume":"91","year":"2018","journal-title":"Neurology"},{"key":"2023071921075646200_c2","doi-asserted-by":"publisher","first-page":"650","DOI":"10.1016\/j.parkreldis.2015.02.026","article-title":"Detecting and monitoring the symptoms of Parkinson's disease using smartphones: A pilot study","volume":"21","year":"2015","journal-title":"Parkinsonism Relat Disord."},{"key":"2023071921075646200_c3","doi-asserted-by":"publisher","first-page":"503","DOI":"10.3233\/JPD-181389","article-title":"Investigating voice as a biomarker for leucine-rich repeat kinase 2-associated Parkinson's disease","volume":"8","year":"2018","journal-title":"J. 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