帕金森病早期预测的人工智能方法。

Anjali Gond, Adarsh Kumar, Anmol Kumar, Swatantra K S Kushwaha
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引用次数: 0

摘要

帕金森病(PD)是一种进行性神经退行性疾病,影响运动和非运动功能,主要是由于黑质中多巴胺能神经元的逐渐丧失。传统的诊断方法在很大程度上依赖于临床症状评估,这往往导致发现和治疗的延误。然而,近年来,人工智能(AI),特别是机器学习(ML)和深度学习(DL),已经成为PD诊断和管理的突破性技术。这篇综述探讨了人工智能驱动技术在早期疾病检测、持续监测和个性化治疗策略发展中的新兴作用。先进的人工智能应用,包括医学成像分析、语音模式识别、步态评估和数字生物标志物识别,在提高诊断准确性和患者护理方面显示出巨大的潜力。此外,人工智能驱动的远程医疗解决方案实现了远程和实时疾病监测,解决了与可及性和早期干预相关的挑战。尽管取得了这些有希望的进展,但仍然存在一些障碍,例如对数据隐私的担忧,人工智能模型的可解释性,以及在临床实施之前需要严格验证。预计到2030年,帕金森病病例将大幅增加,进一步的研究和跨学科合作对于完善人工智能技术并确保其在医疗实践中的可靠性至关重要。通过弥合技术与神经病学之间的差距,人工智能有可能彻底改变帕金森病的管理,为精准医疗和更好的患者治疗结果铺平道路。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Artificial Intelligence Approaches for Early Prediction of Parkinson's Disease.

Parkinson's disease (PD) is a progressive neurodegenerative disorder that affects both motor and non-motor functions, primarily due to the gradual loss of dopaminergic neurons in the substantia nigra. Traditional diagnostic methods largely depend on clinical symptom evaluation, which often leads to delays in detection and treatment. However, in recent years, artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL), have emerged as groundbreaking techniques for the diagnosis and management of PD. This review explores the emergent role of AI-driven techniques in early disease detection, continuous monitoring, and the development of personalized treatment strategies. Advanced AI applications, including medical imaging analysis, speech pattern recognition, gait assessment, and the identification of digital biomarkers, have shown remarkable potential in improving diagnostic accuracy and patient care. Additionally, AI-driven telemedicine solutions enable remote and real-time disease monitoring, addressing challenges related to accessibility and early intervention. Despite these promising advancements, several hurdles remain, such as concerns over data privacy, the interpretability of AI models, and the need for rigorous validation before clinical implementation. With PD cases expected to rise significantly by 2030, further research and interdisciplinary collaboration are crucial to refining AI technologies and ensuring their reliability in medical practice. By bridging the gap between technology and neurology, AI has the potential to revolutionize PD management, paving the way for precision medicine and better patient outcomes.

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