{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,11]],"date-time":"2026-11-11T23:05:51Z","timestamp":1794438351134,"version":"4.9.1"},"reference-count":50,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,12,3]],"date-time":"2024-12-03T00:00:00Z","timestamp":1733184000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2024,12,3]],"date-time":"2024-12-03T00:00:00Z","timestamp":1733184000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"DOI":"10.1038\/s41598-024-81824-x","type":"journal-article","created":{"date-parts":[[2024,12,3]],"date-time":"2024-12-03T16:28:18Z","timestamp":1733243298000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":30,"title":["Transformer-based transfer learning on self-reported voice recordings for Parkinson\u2019s disease diagnosis"],"prefix":"10.1038","volume":"14","author":[{"given":"Ilias","family":"Tougui","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mehdi","family":"Zakroum","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ouassim","family":"Karrakchou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mounir","family":"Ghogho","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,12,3]]},"reference":[{"key":"81824_CR1","first-page":"1","volume":"3","author":"W Poewe","year":"2017","unstructured":"Poewe, W. et al. Parkinson disease. Nat. Rev. Dis. Prim. 3, 1\u201321 (2017).","journal-title":"Nat. Rev. Dis. Prim."},{"key":"81824_CR2","doi-asserted-by":"publisher","first-page":"585","DOI":"10.1016\/j.nrleng.2014.10.016","volume":"31","author":"K Berganzo","year":"2016","unstructured":"Berganzo, K. et al. Motor and non-motor symptoms of Parkinson\u2019s disease and their impact on quality of life and on different clinical subgroups. Neurologia (English Edition) 31, 585\u2013591 (2016).","journal-title":"Neurologia (English Edition)"},{"key":"81824_CR3","doi-asserted-by":"publisher","first-page":"566","DOI":"10.1212\/WNL.0000000000002350","volume":"86","author":"G Rizzo","year":"2016","unstructured":"Rizzo, G. et al. Accuracy of clinical diagnosis of Parkinson disease: A systematic review and meta-analysis. Neurology 86, 566\u2013576 (2016).","journal-title":"Neurology"},{"key":"81824_CR4","doi-asserted-by":"publisher","first-page":"108","DOI":"10.1038\/s41531-023-00523-y","volume":"9","author":"C Pearson","year":"2023","unstructured":"Pearson, C. et al. Care access and utilization among medicare beneficiaries living with Parkinson\u2019s disease. NPJ Parkinson\u2019s Dis. 9, 108 (2023).","journal-title":"NPJ Parkinson\u2019s Dis."},{"key":"81824_CR5","doi-asserted-by":"publisher","unstructured":"Dorsey, E. R. et al. The emerging evidence of the parkinson pandemic. J Parkinsons Dis. 8(s1), S3\u2013S8. https:\/\/doi.org\/10.3233\/JPD-181474 (2018).","DOI":"10.3233\/JPD-181474"},{"key":"81824_CR6","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1038\/s41531-020-0117-1","volume":"6","author":"W Yang","year":"2020","unstructured":"Yang, W. et al. Current and projected future economic burden of Parkinson\u2019s disease in the US. NPJ Parkinson\u2019s Dis. 6, 15 (2020).","journal-title":"NPJ Parkinson\u2019s Dis."},{"key":"81824_CR7","doi-asserted-by":"publisher","first-page":"35219","DOI":"10.1109\/ACCESS.2022.3162844","volume":"10","author":"H Mughal","year":"2022","unstructured":"Mughal, H., Javed, A. R., Rizwan, M., Almadhor, A. S. & Kryvinska, N. Parkinson\u2019s disease management via wearable sensors: A systematic review. IEEE Access 10, 35219\u201335237 (2022).","journal-title":"IEEE Access"},{"key":"81824_CR8","doi-asserted-by":"publisher","first-page":"65","DOI":"10.3233\/JPD-160878","volume":"7","author":"H Hasan","year":"2017","unstructured":"Hasan, H., Athauda, D. S., Foltynie, T. & Noyce, A. J. Technologies assessing limb bradykinesia in Parkinson\u2019s disease. J. Parkinsons Dis. 7, 65\u201377 (2017).","journal-title":"J. Parkinsons Dis."},{"key":"81824_CR9","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0246528","volume":"16","author":"M Sica","year":"2021","unstructured":"Sica, M. et al. Continuous home monitoring of Parkinson\u2019s disease using inertial sensors: A systematic review. PLoS ONE 16, e0246528 (2021).","journal-title":"PLoS ONE"},{"key":"81824_CR10","doi-asserted-by":"publisher","first-page":"1919","DOI":"10.3390\/electronics9111919","volume":"9","author":"B Li","year":"2020","unstructured":"Li, B. et al. Improved deep learning technique to detect freezing of gait in Parkinson\u2019s disease based on wearable sensors. Electronics 9, 1919 (2020).","journal-title":"Electronics"},{"key":"81824_CR11","doi-asserted-by":"publisher","first-page":"2613","DOI":"10.3390\/s22072613","volume":"22","author":"L Mesin","year":"2022","unstructured":"Mesin, L. et al. A multi-modal analysis of the freezing of gait phenomenon in Parkinson\u2019s disease. Sensors 22, 2613 (2022).","journal-title":"Sensors"},{"key":"81824_CR12","doi-asserted-by":"publisher","first-page":"151","DOI":"10.3389\/fnagi.2015.00151","volume":"7","author":"N-H Ko","year":"2015","unstructured":"Ko, N.-H., Laine, C. M., Fisher, B. E. & Valero-Cuevas, F. J. Force variability during dexterous manipulation in individuals with mild to moderate Parkinson\u2019s disease. Frontiers Aging Neurosci. 7, 151 (2015).","journal-title":"Frontiers Aging Neurosci."},{"key":"81824_CR13","doi-asserted-by":"publisher","first-page":"3491","DOI":"10.1109\/TBME.2020.2988942","volume":"67","author":"OY Ch\u00e9n","year":"2020","unstructured":"Ch\u00e9n, O. Y. et al. Building a machine-learning framework to remotely assess Parkinson\u2019s disease using smartphones. IEEE Trans. Biomed. Eng. 67, 3491\u20133500 (2020).","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"81824_CR14","doi-asserted-by":"publisher","first-page":"409","DOI":"10.1038\/s41582-020-0370-2","volume":"16","author":"CG Canning","year":"2020","unstructured":"Canning, C. G. et al. Virtual reality in research and rehabilitation of gait and balance in Parkinson disease. Nat. Rev. Neurol. 16, 409\u2013425 (2020).","journal-title":"Nat. Rev. Neurol."},{"key":"81824_CR15","doi-asserted-by":"publisher","first-page":"1573","DOI":"10.1109\/TBME.2021.3116935","volume":"69","author":"C Laganas","year":"2021","unstructured":"Laganas, C. et al. Parkinson\u2019s disease detection based on running speech data from phone calls. IEEE Trans. Biomed. Eng. 69, 1573\u20131584 (2021).","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"81824_CR16","doi-asserted-by":"publisher","first-page":"1272","DOI":"10.1002\/mds.26642","volume":"31","author":"AJ Espay","year":"2016","unstructured":"Espay, A. J. et al. Technology in Parkinson\u2019s disease: Challenges and opportunities. Mov. Disord. 31, 1272\u20131282 (2016).","journal-title":"Mov. Disord."},{"key":"81824_CR17","doi-asserted-by":"publisher","DOI":"10.1038\/sdata.2016.11","author":"BM Bot","year":"2016","unstructured":"Bot, B. M. et al. The mpower study, Parkinson disease mobile data collected using researchkit. Sci. Data[SPACE]https:\/\/doi.org\/10.1038\/sdata.2016.11 (2016).","journal-title":"Sci. Data"},{"key":"81824_CR18","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuri.2021.100028","volume":"2","author":"A Sujith","year":"2022","unstructured":"Sujith, A., Sajja, G. S., Mahalakshmi, V., Nuhmani, S. & Prasanalakshmi, B. Systematic review of smart health monitoring using deep learning and artificial intelligence. Neurosci. Inform. 2, 100028 (2022).","journal-title":"Neurosci. Inform."},{"key":"81824_CR19","doi-asserted-by":"publisher","first-page":"4653923","DOI":"10.1155\/2022\/4653923","volume":"2022","author":"F Sabry","year":"2022","unstructured":"Sabry, F., Eltaras, T., Labda, W., Alzoubi, K. & Malluhi, Q. Machine learning for healthcare wearable devices: The big picture. J. Healthc. Eng. 2022, 4653923 (2022).","journal-title":"J. Healthc. Eng."},{"key":"81824_CR20","doi-asserted-by":"publisher","first-page":"719","DOI":"10.1002\/cpt.1781","volume":"107","author":"L Omberg","year":"2020","unstructured":"Omberg, L., Chaibub Neto, E. & Mangravite, L. M. Data science approaches for effective use of mobile device-based collection of real-world data. Clin. Pharmacol. Therap. 107, 719\u2013721. https:\/\/doi.org\/10.1002\/cpt.1781 (2020).","journal-title":"Clin. Pharmacol. Therap."},{"key":"81824_CR21","unstructured":"Vaswani, A. et al. Attention is all you need. In Advances in Neural Information Processing Systems Vol. 30 (eds Guyon, I. et al.) (Curran Associates Inc., London, 2017)."},{"key":"81824_CR22","doi-asserted-by":"publisher","first-page":"131","DOI":"10.1155\/1999\/327643","volume":"11","author":"AK Ho","year":"1998","unstructured":"Ho, A. K., Iansek, R., Marigliani, C., Bradshaw, J. L. & Gates, S. Speech impairment in a large sample of patients with Parkinson\u2019s disease. Behav. Neurol. 11, 131\u2013137 (1998).","journal-title":"Behav. Neurol."},{"key":"81824_CR23","doi-asserted-by":"publisher","first-page":"38","DOI":"10.1016\/j.bandl.2018.08.002","volume":"185","author":"KM Smith","year":"2018","unstructured":"Smith, K. M. & Caplan, D. N. Communication impairment in Parkinson\u2019s disease: Impact of motor and cognitive symptoms on speech and language. Brain Lang. 185, 38\u201346 (2018).","journal-title":"Brain Lang."},{"key":"81824_CR24","doi-asserted-by":"publisher","first-page":"297","DOI":"10.1586\/14737175.8.2.297","volume":"8","author":"LO Ramig","year":"2008","unstructured":"Ramig, L. O., Fox, C. & Sapir, S. Speech treatment for Parkinson\u2019s disease. Expert Rev. Neurother. 8, 297\u2013309 (2008).","journal-title":"Expert Rev. Neurother."},{"key":"81824_CR25","doi-asserted-by":"crossref","unstructured":"Lamba, R., Gulati, T. & Jain, A. Automated Parkinson\u2019s disease diagnosis system using transfer learning techniques. In Emergent Converging Technologies and Biomedical Systems: Select Proceedings of ETBS 2021, 183\u2013196 (Springer, 2022).","DOI":"10.1007\/978-981-16-8774-7_16"},{"key":"81824_CR26","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2021.115013","volume":"178","author":"O Karaman","year":"2021","unstructured":"Karaman, O., \u00c7ak\u0131n, H., Alhudhaif, A. & Polat, K. Robust automated Parkinson disease detection based on voice signals with transfer learning. Expert Syst. Appl. 178, 115013 (2021).","journal-title":"Expert Syst. Appl."},{"key":"81824_CR27","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2021\/5552743","volume":"2021","author":"Y Wang","year":"2021","unstructured":"Wang, Y., Nazir, S. & Shafiq, M. An overview on analyzing deep learning and transfer learning approaches for health monitoring. Comput. Math. Methods Med. 2021, 1\u201310. https:\/\/doi.org\/10.1155\/2021\/5552743 (2021).","journal-title":"Comput. Math. Methods Med."},{"key":"81824_CR28","unstructured":"O\u2019Shea, K. & Nash, R. An introduction to convolutional neural networks. 1511.08458 (2015)."},{"key":"81824_CR29","doi-asserted-by":"crossref","unstructured":"Akila, B. & Nayahi, J. J.\u00a0V. Parkinson classification neural network with mass algorithm for processing speech signals. Neural Comput. Appl. 1\u201317 (2024).","DOI":"10.1007\/s00521-024-09596-z"},{"key":"81824_CR30","doi-asserted-by":"publisher","first-page":"556","DOI":"10.1016\/j.bbe.2022.04.002","volume":"42","author":"C Quan","year":"2022","unstructured":"Quan, C., Ren, K., Luo, Z., Chen, Z. & Ling, Y. End-to-end deep learning approach for Parkinson\u2019s disease detection from speech signals. Biocybern. Biomed. Eng. 42, 556\u2013574 (2022).","journal-title":"Biocybern. Biomed. Eng."},{"key":"81824_CR31","doi-asserted-by":"publisher","first-page":"295","DOI":"10.3390\/bioengineering11030295","volume":"11","author":"HS Malekroodi","year":"2024","unstructured":"Malekroodi, H. S., Madusanka, N., Lee, B.-I. & Yi, M. Leveraging deep learning for fine-grained categorization of Parkinson\u2019s disease progression levels through analysis of vocal acoustic patterns. Bioengineering 11, 295 (2024).","journal-title":"Bioengineering"},{"key":"81824_CR32","doi-asserted-by":"crossref","unstructured":"Zhang, H., Wang, A., Li, D. & Xu, W. Deepvoice: A voiceprint-based mobile health framework for Parkinson\u2019s disease identification. In 2018 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI), 214\u2013217 (IEEE, 2018).","DOI":"10.1109\/BHI.2018.8333407"},{"key":"81824_CR33","doi-asserted-by":"crossref","unstructured":"Wroge, T.\u00a0J. et al. Parkinson\u2019s disease diagnosis using machine learning and voice. In 2018 IEEE Signal Processing in Medicine and Biology Symposium (SPMB) 1\u20137 (IEEE, 2018).","DOI":"10.1109\/SPMB.2018.8615607"},{"key":"81824_CR34","doi-asserted-by":"crossref","unstructured":"Schwab, P. & Karlen, W. Phonemd: Learning to diagnose parkinson\u2019s disease from smartphone data. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 33, 1118\u20131125 (2019).","DOI":"10.1609\/aaai.v33i01.33011118"},{"key":"81824_CR35","doi-asserted-by":"crossref","unstructured":"Li, W., Zhu, W., Dorsey, E.\u00a0R. & Luo, J. Predicting Parkinson\u2019s disease with multimodal irregularly collected longitudinal smartphone data. In 2020 IEEE International Conference on Data Mining (ICDM) 1106\u20131111 (IEEE, 2020).","DOI":"10.1109\/ICDM50108.2020.00133"},{"key":"81824_CR36","doi-asserted-by":"crossref","unstructured":"Zhang, H., Deng, K., Li, H., Albin, R.\u00a0L. & Guan, Y. Deep learning identifies digital biomarkers for self-reported parkinson\u2019s disease. Patterns 1 (2020).","DOI":"10.1016\/j.patter.2020.100042"},{"key":"81824_CR37","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-021-00414-7","author":"SK Sieberts","year":"2021","unstructured":"Sieberts, S. K. et al. Crowdsourcing digital health measures to predict Parkinson\u2019s disease severity: The Parkinson\u2019s disease digital biomarker dream challenge. NPJ Digit. Med.[SPACE]https:\/\/doi.org\/10.1038\/s41746-021-00414-7 (2021).","journal-title":"NPJ Digit. Med."},{"key":"81824_CR38","doi-asserted-by":"publisher","first-page":"58","DOI":"10.1038\/s42003-022-03002-x","volume":"5","author":"K Deng","year":"2022","unstructured":"Deng, K. et al. Heterogeneous digital biomarker integration out-performs patient self-reports in predicting Parkinson\u2019s disease. Commun. Biol. 5, 58 (2022).","journal-title":"Commun. Biol."},{"key":"81824_CR39","doi-asserted-by":"publisher","DOI":"10.1038\/sdata.2016.11","author":"BM Bot","year":"2016","unstructured":"Bot, B. M. et al. mpower public researcher portal, the voice activity data. Synapse[SPACE]https:\/\/doi.org\/10.1038\/sdata.2016.11 (2016).","journal-title":"Synapse"},{"key":"81824_CR40","doi-asserted-by":"publisher","unstructured":"Huang, G. et al. Densely Connected Convolutional Networks. In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2261\u20132269. https:\/\/doi.org\/10.1109\/CVPR.2017.243 (Honolulu, HI, USA, 2017).","DOI":"10.1109\/CVPR.2017.243"},{"key":"81824_CR41","doi-asserted-by":"publisher","unstructured":"He, K. et al. Deep Residual Learning for Image Recognition. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 770\u2013778. https:\/\/doi.org\/10.1109\/CVPR.2016.90 (Las Vegas, NV, USA, 2016).","DOI":"10.1109\/CVPR.2016.90"},{"key":"81824_CR42","doi-asserted-by":"publisher","unstructured":"Gong, Y., Chung, Y.-A. & Glass, J. AST: Audio Spectrogram Transformer. In Proceedings of Interspeech 2021 571\u2013575. https:\/\/doi.org\/10.21437\/Interspeech.2021-698 (2021).","DOI":"10.21437\/Interspeech.2021-698"},{"key":"81824_CR43","unstructured":"Dosovitskiy, A. et al. An image is worth 16x16 words: Transformers for image recognition at scale. arXiv:2010.11929 (2021)"},{"key":"81824_CR44","doi-asserted-by":"publisher","first-page":"99","DOI":"10.1038\/s41746-019-0178-x","volume":"2","author":"E Chaibub Neto","year":"2019","unstructured":"Chaibub Neto, E. et al. Detecting the impact of subject characteristics on machine learning-based diagnostic applications. NPJ Digit. Med. 2, 99 (2019).","journal-title":"NPJ Digit. Med."},{"key":"81824_CR45","doi-asserted-by":"publisher","DOI":"10.1038\/sdata.2016.11","author":"BM Bot","year":"2016","unstructured":"Bot, B. M. et al. mpower public researcher portal, the demographic survey. Synapse[SPACE]https:\/\/doi.org\/10.1038\/sdata.2016.11 (2016).","journal-title":"Synapse"},{"key":"81824_CR46","doi-asserted-by":"publisher","DOI":"10.2196\/mhealth.6521","volume":"5","author":"M Doerr","year":"2017","unstructured":"Doerr, M. et al. Formative evaluation of participant experience with mobile econsent in the app-mediated parkinson mpower study: A mixed methods study. JMIR Mhealth Uhealth 5, e6521 (2017).","journal-title":"JMIR Mhealth Uhealth"},{"key":"81824_CR47","doi-asserted-by":"publisher","first-page":"480","DOI":"10.1038\/s41587-021-00974-9","volume":"40","author":"L Omberg","year":"2022","unstructured":"Omberg, L. et al. Remote smartphone monitoring of Parkinson\u2019s disease and individual response to therapy. Nat. Biotechnol. 40, 480\u2013487 (2022).","journal-title":"Nat. Biotechnol."},{"key":"81824_CR48","doi-asserted-by":"publisher","unstructured":"Deng, L. & O\u2019Shaughnessy, D. Speech processing https:\/\/doi.org\/10.1201\/9781482276237 (2003).","DOI":"10.1201\/9781482276237"},{"key":"81824_CR49","doi-asserted-by":"crossref","unstructured":"Kirkland, E. J. Bilinear Interpolation, 261\u2013263 (Springer, US, 2010).","DOI":"10.1007\/978-1-4419-6533-2_12"},{"key":"81824_CR50","doi-asserted-by":"publisher","unstructured":"Gemmeke, J.\u00a0F. et al. Audio set: An ontology and human-labeled dataset for audio events. In 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), https:\/\/doi.org\/10.1109\/icassp.2017.7952261 (IEEE, 2017).","DOI":"10.1109\/icassp.2017.7952261"}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-024-81824-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-024-81824-x","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-024-81824-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,3]],"date-time":"2024-12-03T17:02:46Z","timestamp":1733245366000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-024-81824-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,3]]},"references-count":50,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["81824"],"URL":"https:\/\/doi.org\/10.1038\/s41598-024-81824-x","relation":{},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,3]]},"assertion":[{"value":"14 August 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 November 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 December 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"30131"}}