{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,7]],"date-time":"2026-10-07T23:15:04Z","timestamp":1791414904447,"version":"4.3.3"},"reference-count":50,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2024,9,4]],"date-time":"2024-09-04T00:00:00Z","timestamp":1725408000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Med."],"abstract":"<jats:sec><jats:title>Introduction<\/jats:title><jats:p>Parkinson\u2019s disease (PD) is a neurodegenerative illness that impairs normal human movement. The primary cause of PD is the deficiency of dopamine in the human brain. PD also leads to several other challenges, including insomnia, eating disturbances, excessive sleepiness, fluctuations in blood pressure, sexual dysfunction, and other issues.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>The suggested system is an extremely promising technological strategy that may help medical professionals provide accurate and unbiased disease diagnoses. This is accomplished by utilizing significant and unique traits taken from spiral drawings connected to Parkinson\u2019s disease. While PD cannot be cured, early administration of drugs may significantly improve the condition of a patient with PD. An expeditious and accurate clinical classification of PD ensures that efficacious therapeutic interventions can commence promptly, potentially impeding the advancement of the disease and enhancing the quality of life for both patients and their caregivers. Transfer learning models have been applied to diagnose PD by analyzing important and distinctive characteristics extracted from hand-drawn spirals. The studies were carried out in conjunction with a comparison analysis employing 102 spiral drawings. This work enhances current research by analyzing the effectiveness of transfer learning models, including VGG19, InceptionV3, ResNet50v2, and DenseNet169, for identifying PD using hand-drawn spirals.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Transfer machine learning models demonstrate highly encouraging outcomes in providing a precise and reliable classification of PD. Actual results demonstrate that the InceptionV3 model achieved a high accuracy of 89% when learning from spiral drawing images and had a superior receiver operating characteristic (ROC) curve value of 95%.<\/jats:p><\/jats:sec><jats:sec><jats:title>Discussion<\/jats:title><jats:p>The comparison results suggest that PD identification using these models is currently at the forefront of PD research. The dataset will be enlarged, transfer learning strategies will be investigated, and the system\u2019s integration into a comprehensive Parkinson\u2019s monitoring and evaluation platform will be looked into as future research areas. The results of this study could lead to a better quality of life for Parkinson\u2019s sufferers, individualized treatment, and an early classification.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fmed.2024.1453743","type":"journal-article","created":{"date-parts":[[2024,9,4]],"date-time":"2024-09-04T18:00:28Z","timestamp":1725472828000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":29,"title":["Utilizing deep learning models in an intelligent spiral drawing classification system for Parkinson\u2019s disease classification"],"prefix":"10.3389","volume":"11","author":[{"given":"Nesren","family":"Farhah","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2024,9,4]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"a008862","DOI":"10.1101\/cshperspect.a008862","article-title":"The history of Parkinson's disease: early clinical descriptions and neurological therapies","volume":"1","author":"Goetz","year":"2011","journal-title":"Pers Med"},{"key":"ref2","doi-asserted-by":"publisher","first-page":"719167","DOI":"10.1155\/2012\/719167","article-title":"Confirmatory factor analysis of the motor unified Parkinson's disease rating scale","volume":"2012","author":"Vassar","year":"2012","journal-title":"Parkinsons Dis"},{"key":"ref3","year":"2023"},{"key":"ref4","doi-asserted-by":"publisher","first-page":"S21","DOI":"10.1212\/WNL.54.11.21A","article-title":"Prevalence of Parkinson's disease in Europe: a collaborative study of population-based cohorts: neurologic diseases in the elderly research group","volume":"54","author":"de Rijk","year":"2000","journal-title":"Neurology"},{"key":"ref5","doi-asserted-by":"publisher","first-page":"5049","DOI":"10.1007\/s13369-016-2206-3","article-title":"A machine learning system for the diagnosis of Parkinson's disease from speech signals and its application to multiple speech signal types","volume":"41","author":"Cant\u00fcrk","year":"2016","journal-title":"Arab J Sci Eng"},{"key":"ref6","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1016\/j.pneurobio.2006.11.009","article-title":"Advances in the treatment of Parkinson's disease","volume":"81","author":"Singh","year":"2007","journal-title":"Prog Neurobiol"},{"key":"ref7","first-page":"1","article-title":"Application of multi-layer (perceptron) artificial neural network in the diagnosis system: a systematic review","author":"Rana","year":"2018"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2012.06563","article-title":"Exploring facial expressions and affective domains for Parkinson detection","author":"Gomez-Gomez","year":"2020","journal-title":"arXiv"},{"key":"ref9","doi-asserted-by":"publisher","first-page":"2862","DOI":"10.1002\/mds.28751","article-title":"Cognitive determinants of dysarthria in Parkinson's disease: an automated machine learning approach","volume":"36","author":"Garc\u00eda","year":"2021","journal-title":"Mov Disord"},{"key":"ref10","doi-asserted-by":"publisher","first-page":"6850","DOI":"10.3934\/math.2024334shu","article-title":"Modeling and diagnosis Parkinson disease by using hand drawing: deep learning model","volume":"9","author":"Aldhyani","year":"2024","journal-title":"AIMS Mathematics"},{"key":"ref11","doi-asserted-by":"publisher","first-page":"1","DOI":"10.57197\/JDR-2024-0001","article-title":"Developing system-based voice features for detecting Parkinson\u2019s disease using machine learning algorithms","volume":"3","author":"Al-Nefaie","year":"2024","journal-title":"JDR"},{"key":"ref12","doi-asserted-by":"publisher","first-page":"566","DOI":"10.1212\/WNL.0000000000002350","article-title":"Accuracy of clinical diagnosis of Parkinson disease: a systematic review and meta-analysis","volume":"86","author":"Rizzo","year":"2016","journal-title":"Neurology"},{"key":"ref13","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.artmed.2013.05.001","article-title":"Clinical decision support systems: need for evidence, need for evaluation","volume":"59","author":"Ammenwerth","year":"2013","journal-title":"Artif Intell Med"},{"key":"ref14","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1016\/j.artmed.2004.07.007","article-title":"Do physicians value decision support? 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