{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,19]],"date-time":"2026-07-19T08:15:41Z","timestamp":1784448941133,"version":"3.55.0"},"reference-count":73,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2024,9,20]],"date-time":"2024-09-20T00:00:00Z","timestamp":1726790400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JCM"],"abstract":"<jats:p>Background: Parkinson\u2019s disease (PD) has transitioned from a rare condition in 1817 to the fastest-growing neurological disorder globally. The significant increase in cases from 2.5 million in 1990 to 6.1 million in 2016, coupled with predictions of a further doubling by 2040, underscores an impending healthcare challenge. This escalation aligns with global demographic shifts, including rising life expectancy and a growing global population. The economic impact, notably in the U.S., reached $51.9 billion in 2017, with projections suggesting a 46% increase by 2037, emphasizing the substantial socio-economic implications for both patients and caregivers. Coupled with a worldwide demand for health workers that is expected to rise to 80 million by 2030, we have fertile ground for a pandemic. Methods: Our transdisciplinary research focused on early PD detection through running speech and continuous handwriting analysis, incorporating medical, biomedical engineering, AI, and linguistic expertise. The cohort comprised 30 participants, including 20 PD patients at stages 1\u20134 on the Hoehn and Yahr scale and 10 healthy controls. We employed advanced AI techniques to analyze correlation plots generated from speech and handwriting features, aiming to identify prodromal PD biomarkers. Results: The study revealed distinct speech and handwriting patterns in PD patients compared to controls. Our ParkinsonNet model demonstrated high predictive accuracy, with F1 scores of 95.74% for speech and 96.72% for handwriting analyses. These findings highlight the potential of speech and handwriting as effective early biomarkers for PD. Conclusions: The integration of AI as a decision support system in analyzing speech and handwriting presents a promising approach for early PD detection. This methodology not only offers a novel diagnostic tool but also contributes to the broader understanding of PD\u2019s early manifestations. Further research is required to validate these findings in larger, diverse cohorts and to integrate these tools into clinical practice for timely PD pre-diagnosis and management.<\/jats:p>","DOI":"10.3390\/jcm13185573","type":"journal-article","created":{"date-parts":[[2024,9,20]],"date-time":"2024-09-20T07:38:57Z","timestamp":1726817937000},"page":"5573","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["In Silico Decoding of Parkinson\u2019s: Speech &amp; Writing Analysis"],"prefix":"10.3390","volume":"13","author":[{"given":"Robert Radu","family":"Ile\u0219an","sequence":"first","affiliation":[{"name":"Department of Neurology and Pediatric Neurology, Faculty of Medicine, University of Medicine and Pharmacy \u201cIuliu Hatieganu\u201d Cluj-Napoca, 400012 Cluj-Napoca, Romania"},{"name":"Department of Oral and Maxillofacial Surgery, Lucerne Cantonal Hospital, Spitalstrasse, 6000 Lucerne, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6211-9205","authenticated-orcid":false,"given":"Sebastian-Aurelian","family":"\u0218tef\u0103nig\u0103","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Faculty of Mathematics and Computer Science, West University of Timisoara, 300223 Timisoara, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Radu","family":"Fle\u0219ar","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Faculty of Mathematics and Computer Science, West University of Timisoara, 300223 Timisoara, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michel","family":"Beyer","sequence":"additional","affiliation":[{"name":"Medical Additive Manufacturing Research Group (Swiss MAM), Department of Biomedical Engineering, University of Basel, 4123 Allschwil, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Elena","family":"Ginghin\u0103","sequence":"additional","affiliation":[{"name":"Department of Anglo-American and German Studies, Faculty of Letters and Arts, \u201cLucian Blaga\u201d University of Sibiu, 550024 Sibiu, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ana Sorina","family":"Pe\u0219tean","sequence":"additional","affiliation":[{"name":"Department of Neurology and Pediatric Neurology, Faculty of Medicine, University of Medicine and Pharmacy \u201cIuliu Hatieganu\u201d Cluj-Napoca, 400012 Cluj-Napoca, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3268-4921","authenticated-orcid":false,"given":"Martin C.","family":"Hirsch","sequence":"additional","affiliation":[{"name":"Institute for Artificial Intelligence in Medicine, Faculty of Medicine, University Hospital Giessen and Marburg, Philipps-Universit\u00e4t Marburg, Baldingerstra\u00dfe, 35043 Marburg, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1694-7074","authenticated-orcid":false,"given":"L\u0103cr\u0103mioara","family":"Perju-Dumbrav\u0103","sequence":"additional","affiliation":[{"name":"Department of Neurology and Pediatric Neurology, Faculty of Medicine, University of Medicine and Pharmacy \u201cIuliu Hatieganu\u201d Cluj-Napoca, 400012 Cluj-Napoca, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7824-8506","authenticated-orcid":false,"given":"Paul","family":"Farag\u00f3","sequence":"additional","affiliation":[{"name":"Bases of Electronics Department, Faculty of Electronics, Telecommunications and Information Technology, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,9,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"223","DOI":"10.1176\/jnp.14.2.223","article-title":"An Essay on the Shaking Palsy","volume":"14","author":"Parkinson","year":"2002","journal-title":"J. Neuropsychiatry Clin. Neurosci."},{"key":"ref_2","first-page":"S3","article-title":"The Emerging Evidence of the Parkinson Pandemic","volume":"8","author":"Dorsey","year":"2018","journal-title":"J. Park. Dis."},{"key":"ref_3","unstructured":"GBD 2016 Parkinson\u2019s Disease Collaborators (2018). Global, Regional, and National Burden of Parkinson\u2019s Disease, 1990\u20132016: A Systematic Analysis for the Global Burden of Disease Study 2016. Lancet Neur., 17, 939\u2013953."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1001\/jamaneurol.2017.3299","article-title":"The Parkinson Pandemic\u2014A Call to Action","volume":"75","author":"Dorsey","year":"2018","journal-title":"JAMA Neurol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1038\/s41531-020-0117-1","article-title":"Current and Projected Future Economic Burden of Parkinson\u2019s Disease in the U.S","volume":"6","author":"Yang","year":"2020","journal-title":"Npj Park. Dis."},{"key":"ref_6","unstructured":"(2024, March 03). If Design\u2013Porsche Design: Form and Function the Battle. Available online: https:\/\/ifdesign.com\/en\/winner-ranking\/project\/porsche-design-form-and-function-the-battle\/69506."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Crosiers, D., Santens, P., and Chaudhuri, K.R. (2021). Editorial: Prodromal Parkinson\u2019s Disease. Front. Neurol., 11.","DOI":"10.3389\/fneur.2020.634490"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"653","DOI":"10.1002\/mds.23499","article-title":"The Patients\u2019 Perception of Prodromal Symptoms before the Initial Diagnosis of Parkinson\u2019s Disease","volume":"26","author":"Gaenslen","year":"2011","journal-title":"Mov. Disord."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"110","DOI":"10.1002\/mds.870070203","article-title":"When Did Ray Kennedy\u2019s Parkinson\u2019s Disease Begin?","volume":"7","author":"Lees","year":"1992","journal-title":"Mov. Disord."},{"key":"ref_10","unstructured":"(2024, March 03). Parkinson\u2019s Disease: Challenges, Progress, and Promise|National Institute of Neurological Disorders and Stroke, Available online: https:\/\/www.ninds.nih.gov\/current-research\/focus-disorders\/parkinsons-disease-research\/parkinsons-disease-challenges-progress-and-promise."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1016\/j.sleep.2015.07.030","article-title":"Quantitative Assessment of Motor Speech Abnormalities in Idiopathic Rapid Eye Movement Sleep Behaviour Disorder","volume":"19","author":"Rusz","year":"2016","journal-title":"Sleep. Med."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1016\/0022-510X(72)90002-0","article-title":"Micrographia in Parkinson\u2019s Disease","volume":"15","author":"McLennan","year":"1972","journal-title":"J. Neurol. Sci."},{"key":"ref_13","first-page":"6","article-title":"Latin and the Making of the Romance Languages1","volume":"Volume 2","author":"Ledgeway","year":"2013","journal-title":"The Cambridge History of the Romance Languages: Volume 2: Contexts"},{"key":"ref_14","first-page":"163","article-title":"Hypokinetic Dysarthria in Parkinson\u2019s Disease: A Narrative Review","volume":"57","year":"2023","journal-title":"Med. Bull. Sisli Etfal Hosp."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1007\/s00702-017-1676-0","article-title":"Speech Disorders in Parkinson\u2019s Disease: Early Diagnostics and Effects of Medication and Brain Stimulation","volume":"124","author":"Brabenec","year":"2017","journal-title":"J. Neural Transm."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1038\/s41531-021-00243-1","article-title":"Distinct Patterns of Speech Disorder in Early-Onset and Late-Onset de-Novo Parkinson\u2019s Disease","volume":"7","author":"Rusz","year":"2021","journal-title":"Npj Park. Dis."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"G\u00f3mez-Vilda, P., Mekyska, J., Ferr\u00e1ndez, J.M., Palacios-Alonso, D., G\u00f3mez-Rodellar, A., Rodellar-Biarge, V., Galaz, Z., Smekal, Z., Eliasova, I., and Kostalova, M. (2017). Parkinson Disease Detection from Speech Articulation Neuromechanics. Front. Neuroinform., 11.","DOI":"10.3389\/fninf.2017.00056"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Boschi, V., Catrical\u00e0, E., Consonni, M., Chesi, C., Moro, A., and Cappa, S.F. (2017). Connected Speech in Neurodegenerative Language Disorders: A Review. Front. Psychol., 8.","DOI":"10.3389\/fpsyg.2017.00269"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Al-Hameed, S., Benaissa, M., Christensen, H., Mirheidari, B., Blackburn, D., and Reuber, M. (2019). A New Diagnostic Approach for the Identification of Patients with Neurodegenerative Cognitive Complaints. PLoS ONE, 14.","DOI":"10.1371\/journal.pone.0217388"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"e199","DOI":"10.1016\/j.jvoice.2010.04.007","article-title":"Intonation and Speech Rate in Parkinson\u2019s Disease: General and Dynamic Aspects and Responsiveness to Levodopa Admission","volume":"25","author":"Skodda","year":"2011","journal-title":"J. Voice"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1573","DOI":"10.1109\/TBME.2021.3116935","article-title":"Parkinson\u2019s Disease Detection Based on Running Speech Data From Phone Calls","volume":"69","author":"Laganas","year":"2022","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Pah, N.D., Motin, M.A., and Kumar, D.K. (2022). Phonemes Based Detection of Parkinson\u2019s Disease for Telehealth Applications. Sci. Rep., 12.","DOI":"10.1038\/s41598-022-13865-z"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1016\/j.neucom.2015.02.085","article-title":"Robust and Complex Approach of Pathological Speech Signal Analysis","volume":"167","author":"Mekyska","year":"2015","journal-title":"Neurocomputing"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.bandl.2016.11.005","article-title":"Distinct Patterns of Imprecise Consonant Articulation among Parkinson\u2019s Disease, Progressive Supranuclear Palsy and Multiple System Atrophy","volume":"165","author":"Tykalova","year":"2017","journal-title":"Brain Lang."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1007\/s13755-021-00162-8","article-title":"An Algorithm for Parkinson\u2019s Disease Speech Classification Based on Isolated Words Analysis","volume":"9","author":"Amato","year":"2021","journal-title":"Health Inf. Sci. Syst."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Orozco-Arroyave, J.R., H\u00f6nig, F., Arias-Londo\u00f1o, J.D., Vargas-Bonilla, J.F., Skodda, S., Rusz, J., and N\u00f6th, E. (2015). Voiced\/Unvoiced Transitions in Speech as a Potential Bio-Marker to Detect Parkinson\u2019s Disease. Interspeech 2015, ISCA.","DOI":"10.21437\/Interspeech.2015-34"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"439","DOI":"10.1016\/j.jneuroling.2004.06.001","article-title":"Acoustic Characteristics of Parkinsonian Speech: A Potential Biomarker of Early Disease Progression and Treatment","volume":"17","author":"Harel","year":"2004","journal-title":"J. Neurolinguist."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"350","DOI":"10.1121\/1.3514381","article-title":"Quantitative Acoustic Measurements for Characterization of Speech and Voice Disorders in Early Untreated Parkinson\u2019s Disease","volume":"129","author":"Rusz","year":"2011","journal-title":"J. Acoust. Soc. Am."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1016\/j.cmpb.2015.12.011","article-title":"Prosodic Analysis of Neutral, Stress-Modified and Rhymed Speech in Patients with Parkinson\u2019s Disease","volume":"127","author":"Galaz","year":"2016","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"209","DOI":"10.4103\/2228-7477.128302","article-title":"Assessment of Hypernasality for Children with Cleft Palate Based on Cepstrum Analysis","volume":"3","author":"Akafi","year":"2013","journal-title":"J. Med. Signals Sens."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"e2530","DOI":"10.7717\/peerj.2530","article-title":"Hypernasality Associated with Basal Ganglia Dysfunction: Evidence from Parkinson\u2019s Disease and Huntington\u2019s Disease","volume":"4","author":"Rusz","year":"2016","journal-title":"PeerJ"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"621","DOI":"10.1109\/TBME.2006.889191","article-title":"Acoustic Analysis and Detection of Hypernasality Using a Group Delay Function","volume":"54","author":"Vijayalakshmi","year":"2007","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1007\/s13278-022-00905-9","article-title":"Speech Processing for Early Parkinson\u2019s Disease Diagnosis: Machine Learning and Deep Learning-Based Approach","volume":"12","author":"Khaskhoussy","year":"2022","journal-title":"Soc. Netw. Anal. Min."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"981","DOI":"10.1007\/s10772-023-10068-3","article-title":"Identification of Parkinson\u2019s Disease from Speech Signal Using Machine Learning Approach","volume":"26","author":"Nayak","year":"2023","journal-title":"Int. J. Speech Technol."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Iyer, A., Kemp, A., Rahmatallah, Y., Pillai, L., Glover, A., Prior, F., Larson-Prior, L., and Virmani, T. (2023). A Machine Learning Method to Process Voice Samples for Identification of Parkinson\u2019s Disease. Sci. Rep., 13.","DOI":"10.1038\/s41598-023-47568-w"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"115540","DOI":"10.1109\/ACCESS.2019.2936564","article-title":"Deep Learning-Based Parkinson\u2019s Disease Classification Using Vocal Feature Sets","volume":"7","author":"Gunduz","year":"2019","journal-title":"IEEE Access"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/LSENS.2023.3311670","article-title":"Time-Frequency Analysis of Speech Signal Using Wavelet Synchrosqueezing Transform for Automatic Detection of Parkinson\u2019s Disease","volume":"7","author":"Warule","year":"2023","journal-title":"IEEE Sens. Lett."},{"key":"ref_38","first-page":"426","article-title":"Parkinson\u2019s Disease Recognition from Speech Signal Using Discrete Wavelet Transform, Delta, Delta-Delta, and K-Nearest Neighbor","volume":"Volume 713","author":"Kacprzyk","year":"2023","journal-title":"International Conference on Advanced Intelligent Systems for Sustainable Development"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1007\/978-3-031-48573-2_21","article-title":"Voice-Based Detection of Parkinson\u2019s Disease Using Empirical Mode Decomposition, IMFCC, MFCC, and Deep Learning","volume":"Volume 838","author":"Farhaoui","year":"2024","journal-title":"Artificial Intelligence, Data Science and Applications"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Nouhaila, B., Taoufiq, B.D., and Benayad, N. (2022). An Intelligent Approach Based on the Combination of the Discrete Wavelet Transform, Delta Delta MFCC for Parkinson\u2019s Disease Diagnosis. Int. J. Adv. Comput. Sci. Appl., 13.","DOI":"10.14569\/IJACSA.2022.0130466"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Drissi, T.B., Zayrit, S., Nsiri, B., and Ammoummou, A. (2019). Diagnosis of Parkinson\u2019s Disease Based on Wavelet Transform and Mel Frequency Cepstral Coefficients. Int. J. Adv. Comput. Sci. Appl. (IJACSA), 10.","DOI":"10.14569\/IJACSA.2019.0100315"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"806","DOI":"10.1002\/mdc3.12552","article-title":"Handwriting Analysis in Parkinson\u2019s Disease: Current Status and Future Directions","volume":"4","author":"Thomas","year":"2017","journal-title":"Mov. Disord. Clin. Pract."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Avanzino, L., Pelosin, E., Martino, D., and Abbruzzese, G. (2013). Motor Timing Deficits in Sequential Movements in Parkinson Disease Are Related to Action Planning: A Motor Imagery Study. PLoS ONE, 8.","DOI":"10.1371\/journal.pone.0075454"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Moetesum, M., Siddiqi, I., Javed, F., and Masroor, U. (2020). Dynamic Handwriting Analysis for Parkinson\u2019s Disease Identification Using C-BiGRU Model. Proceedings of the 2020 17th International Conference on Frontiers in Handwriting Recognition (ICFHR), Dortmund, Germany, 8\u201310 September 2020, IEEE.","DOI":"10.1109\/ICFHR2020.2020.00031"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"9561","DOI":"10.1007\/s00521-022-07185-6","article-title":"A Survey of Visual and Procedural Handwriting Analysis for Neuropsychological Assessment","volume":"34","author":"Moetesum","year":"2022","journal-title":"Neural Comput. Applic"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"579","DOI":"10.1007\/978-3-319-00846-2_143","article-title":"Evaluation of Different Handwriting Teaching Methods by Kinematic Analysis","volume":"Volume 41","year":"2014","journal-title":"Proceedings of the XIII Mediterranean Conference on Medical and Biological Engineering and Computing 2013, Seville, Spain, 25\u201328 September 2013"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Cascarano, G.D., Loconsole, C., Brunetti, A., Lattarulo, A., Buongiorno, D., Losavio, G., Sciascio, E.D., and Bevilacqua, V. (2019). Biometric Handwriting Analysis to Support Parkinson\u2019s Disease Assessment and Grading. BMC Med. Inform. Decis. Mak., 19.","DOI":"10.1186\/s12911-019-0989-3"},{"key":"ref_48","unstructured":"(2024, March 03). A Novel Computer Vision Approach to Kinematic Analysis of Handwriting for Accessible Assessment of Neurodegenerative Diseases. Teknos. Available online: https:\/\/www.teknos.org\/home\/2022\/2\/20\/a-novel-computer-vision-approach-to-kinematic-analysis-of-handwriting-for-accessible-assessment-of-neurodegenerative-diseases."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Babushkin, V., Alsuradi, H., Jamil, M.H., Al-Khalil, M.O., and Eid, M. (2023). Assessing Handwriting Task Difficulty Levels through Kinematic Features: A Deep-Learning Approach. Front. Robot. AI, 10.","DOI":"10.3389\/frobt.2023.1193388"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Zham, P., Raghav, S., Kempster, P., Poosapadi Arjunan, S., Wong, K., Nagao, K.J., and Kumar, D.K. (2019). A Kinematic Study of Progressive Micrographia in Parkinson\u2019s Disease. Front. Neurol., 10.","DOI":"10.3389\/fneur.2019.00403"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"493","DOI":"10.1142\/S2196888821500238","article-title":"Parkinson\u2019s Disease Diagnostics Based on the Analysis of Digital Sentence Writing Test","volume":"08","author":"Toomela","year":"2021","journal-title":"Vietnam. J. Comp. Sci."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Vessio, G. (2019). Dynamic Handwriting Analysis for Neurodegenerative Disease Assessment: A Literary Review. Appl. Sci., 9.","DOI":"10.3390\/app9214666"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"2357","DOI":"10.1007\/s00415-013-6996-x","article-title":"Handwriting as an Objective Tool for Parkinson\u2019s Disease Diagnosis","volume":"260","author":"Rosenblum","year":"2013","journal-title":"J. Neurol."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"940","DOI":"10.1007\/s12559-020-09755-z","article-title":"Handwriting Biometrics: Applications and Future Trends in e-Security and e-Health","volume":"12","author":"Fierrez","year":"2020","journal-title":"Cogn. Comput."},{"key":"ref_55","first-page":"960","article-title":"Kinematic and Pressure Features of Handwriting and Drawing: Preliminary Results between Patients with Mild Cognitive Impairment, Alzheimer Disease and Healthy Controls","volume":"14","year":"2017","journal-title":"CAR"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"1464","DOI":"10.1002\/mds.27802","article-title":"Update of the MDS Research Criteria for Prodromal Parkinson\u2019s Disease","volume":"34","author":"Heinzel","year":"2019","journal-title":"Mov. Disord."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"693","DOI":"10.1007\/s12553-021-00555-5","article-title":"Artificial Intelligence in Disease Diagnostics: A Critical Review and Classification on the Current State of Research Guiding Future Direction","volume":"11","author":"Mirbabaie","year":"2021","journal-title":"Health Technol."},{"key":"ref_58","first-page":"17","article-title":"Parkinson\u2019s Disease Detection Using ResNet50 with Transfer Learning","volume":"11","author":"Jahan","year":"2021","journal-title":"Int. J. Comput. Vis. Signal Process."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Anila, M., and Gera, P. (2022). Parkinson\u2019s Disease Identification Using Deep Neural Network with RESNET50. IJACSA, 13.","DOI":"10.14569\/IJACSA.2022.0131156"},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Jahan, N., Nesa, A., and Layek, M.A. (2021). Parkinson\u2019s Disease Detection Using CNN Architectures withTransfer Learning. Proceedings of the 2021 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES), Chennai, India, 24\u201325 September 2021, IEEE.","DOI":"10.1109\/ICSES52305.2021.9633872"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"106700","DOI":"10.1016\/j.engappai.2023.106700","article-title":"Detection of Parkinson\u2019s Disease Based on Spectrograms of Voice Recordings and Extreme Learning Machine Random Weight Neural Networks","volume":"125","author":"Guatelli","year":"2023","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"536","DOI":"10.1002\/mdc3.12476","article-title":"Differences in MDS\u2013UPDRS Scores Based on Hoehn and Yahr Stage and Disease Duration","volume":"4","author":"Skorvanek","year":"2017","journal-title":"Mov. Disord. Clin. Pract."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"2129","DOI":"10.1002\/mds.22340","article-title":"Movement Disorder Society-sponsored Revision of the Unified Parkinson\u2019s Disease Rating Scale (MDS-UPDRS): Scale Presentation and Clinimetric Testing Results","volume":"23","author":"Goetz","year":"2008","journal-title":"Mov. Disord."},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Farag\u00f3, P., \u0218tef\u0103nig\u0103, S.-A., Cordo\u0219, C.-G., Mih\u0103il\u0103, L.-I., Hintea, S., Pe\u0219tean, A.-S., Beyer, M., Perju-Dumbrav\u0103, L., and Ile\u0219an, R.R. (2023). CNN-Based Identification of Parkinson\u2019s Disease from Continuous Speech in Noisy Environments. Bioengineering, 10.","DOI":"10.3390\/bioengineering10050531"},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Nackaerts, E., Heremans, E., Smits-Engelsman, B.C.M., Broeder, S., Vandenberghe, W., Bergmans, B., and Nieuwboer, A. (2017). Validity and Reliability of a New Tool to Evaluate Handwriting Difficulties in Parkinson\u2019s Disease. PLoS ONE, 12.","DOI":"10.1371\/journal.pone.0173157"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"240","DOI":"10.1016\/j.ridd.2017.11.013","article-title":"Development of the Handwriting Legibility Scale (HLS): A Preliminary Examination of Reliability and Validity","volume":"72","author":"Barnett","year":"2018","journal-title":"Res. Dev. Disabil."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"105756","DOI":"10.1016\/j.jecp.2023.105756","article-title":"Handwriting Legibility and Fluency and Their Patterns of Concurrent Relations with Spelling, Graphomotor, and Selective Attention Skills","volume":"236","author":"Downing","year":"2023","journal-title":"J. Exp. Child. Psychol."},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Sitarz, M. (2022). Extending F1 Metric, Probabilistic Approach. arXiv.","DOI":"10.54364\/AAIML.2023.1161"},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Hicks, S.A., Str\u00fcmke, I., Thambawita, V., Hammou, M., Riegler, M.A., Halvorsen, P., and Parasa, S. (2022). On Evaluation Metrics for Medical Applications of Artificial Intelligence. Sci. Rep., 12.","DOI":"10.1038\/s41598-022-09954-8"},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"4927","DOI":"10.1007\/s10072-021-05601-8","article-title":"\u201cParkinson\u2019s Disease\u201d on the Way to Progressive Supranuclear Palsy: A Review on PSP-Parkinsonism","volume":"42","author":"Borsek","year":"2021","journal-title":"Neurol. Sci."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"147","DOI":"10.5603\/pjnns.98678","article-title":"Significance of Dysautonomia in Parkinson\u2019s Disease and Atypical Parkinsonisms","volume":"58","author":"Alster","year":"2024","journal-title":"Neurol. I Neurochir. Pol."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"210","DOI":"10.1016\/j.sleep.2017.05.012","article-title":"The Influence of Sleep Disordered Breathing in REM Sleep Behavior Disorder","volume":"37","author":"Bugalho","year":"2017","journal-title":"Sleep. Med."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"451","DOI":"10.1007\/s13167-011-0096-8","article-title":"Idiopathic REM Sleep Behavior Disorder as a Long-Term Predictor of Neurodegenerative Disorders","volume":"2","author":"Fulda","year":"2011","journal-title":"EPMA J."}],"container-title":["Journal of Clinical Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2077-0383\/13\/18\/5573\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:00:35Z","timestamp":1760112035000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2077-0383\/13\/18\/5573"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,20]]},"references-count":73,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2024,9]]}},"alternative-id":["jcm13185573"],"URL":"https:\/\/doi.org\/10.3390\/jcm13185573","relation":{},"ISSN":["2077-0383"],"issn-type":[{"value":"2077-0383","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,9,20]]}}}