Farhah Nesren
Department of Health Informatics, College of Health Sciences, Saudi Electronic University, Riyadh, Saudi Arabia.
Front Med (Lausanne). 2024 Sep 4;11:1453743. doi: 10.3389/fmed.2024.1453743. eCollection 2024.
Parkinson's 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.
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's 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.
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%.
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's integration into a comprehensive Parkinson's 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's sufferers, individualized treatment, and an early classification.
帕金森病（PD）是一种损害人类正常运动的神经退行性疾病。PD的主要病因是人类大脑中多巴胺缺乏。PD还会引发其他一些问题，包括失眠、饮食紊乱、过度嗜睡、血压波动、性功能障碍及其他问题。
所建议的系统是一种极具前景的技术策略，可帮助医学专业人员提供准确且无偏差的疾病诊断。这是通过利用从与帕金森病相关的螺旋图中提取的重要且独特的特征来实现的。虽然PD无法治愈，但早期给药可显著改善PD患者的病情。对PD进行快速准确的临床分类可确保及时开始有效的治疗干预，有可能阻止疾病进展并提高患者及其护理人员的生活质量。迁移学习模型已被应用于通过分析从手绘螺旋图中提取的重要且独特的特征来诊断PD。这些研究是结合对102张螺旋图进行的比较分析开展的。这项工作通过分析迁移学习模型（包括VGG19、InceptionV3、ResNet50v2和DenseNet169）利用手绘螺旋图识别PD的有效性，加强了当前的研究。
迁移机器学习模型在对PD进行精确可靠的分类方面显示出非常令人鼓舞的结果。实际结果表明，InceptionV3模型在从螺旋图图像学习时达到了89%的高精度，并且具有95%的卓越受试者工作特征（ROC）曲线值。
比较结果表明，使用这些模型进行PD识别目前处于PD研究的前沿。未来的研究领域将包括扩大数据集、研究迁移学习策略以及将该系统集成到一个全面的帕金森病监测和评估平台中。这项研究的结果可能会为帕金森病患者带来更高的生活质量、个性化治疗和早期分类。
