Vitale Annalisa, Villa Rossella, Ugga Lorenzo, Romeo Valeria, Stanzione Arnaldo, Cuocolo Renato
Department of Advanced Biomedical Sciences, University of Naples "Federico Ⅱ", Via S. Pansini 5, 80131-Naples, Italy.
Department of Clinical Medicine and Surgery, University of Naples "Federico Ⅱ", Via S. Pansini 5, 80131-Naples, Italy.
Math Biosci Eng. 2021 Feb 19;18(2):1753-1773. doi: 10.3934/mbe.2021091.
Idiopathic Parkinson's Disease (iPD) is a common motor neurodegenerative disorder. It affects more frequently the elderly population, causing a significant emotional burden both for the patient and caregivers, due to the disease-related onset of motor and cognitive disabilities. iPD's clinical hallmark is the onset of cardinal motor symptoms such as bradykinesia, rest tremor, rigidity, and postural instability. However, these symptoms appear when the neurodegenerative process is already in an advanced stage. Furthermore, the greatest challenge is to distinguish iPD from other similar neurodegenerative disorders, "atypical parkinsonisms", such as Multisystem Atrophy, Progressive Supranuclear Palsy and Cortical Basal Degeneration, since they share many phenotypic manifestations, especially in the early stages. The diagnosis of these neurodegenerative motor disorders is essentially clinical. Consequently, the diagnostic accuracy mainly depends on the professional knowledge and experience of the physician. Recent advances in artificial intelligence have made it possible to analyze the large amount of clinical and instrumental information in the medical field. The application machine learning algorithms to the analysis of neuroimaging data appear to be a promising tool for identifying microstructural alterations related to the pathological process in order to explain the onset of symptoms and the spread of the neurodegenerative process. In this context, the search for quantitative biomarkers capable of identifying parkinsonian patients in the prodromal phases of the disease, of correctly distinguishing them from atypical parkinsonisms and of predicting clinical evolution and response to therapy represent the main goal of most current clinical research studies. Our aim was to review the recent literature and describe the current knowledge about the contribution given by machine learning applications to research and clinical management of parkinsonian syndromes.
特发性帕金森病（iPD）是一种常见的运动性神经退行性疾病。它在老年人群中更为常见，由于与疾病相关的运动和认知障碍的出现，给患者和护理人员都带来了巨大的情感负担。iPD的临床标志是出现诸如运动迟缓、静止性震颤、僵硬和姿势不稳等主要运动症状。然而，这些症状在神经退行性过程已经处于晚期时才会出现。此外，最大的挑战是将iPD与其他类似的神经退行性疾病，即“非典型帕金森综合征”，如多系统萎缩、进行性核上性麻痹和皮质基底节变性区分开来，因为它们有许多表型表现，尤其是在早期阶段。这些神经退行性运动障碍的诊断主要依靠临床症状。因此，诊断准确性主要取决于医生的专业知识和经验。人工智能的最新进展使得分析医学领域大量的临床和仪器信息成为可能。将机器学习算法应用于神经影像数据分析似乎是一种很有前景的工具，可用于识别与病理过程相关的微观结构改变，以解释症状的出现和神经退行性过程的扩散。在这种背景下，寻找能够在疾病前驱期识别帕金森病患者、正确区分他们与非典型帕金森综合征并预测临床进展和治疗反应的定量生物标志物，是当前大多数临床研究的主要目标。我们的目的是回顾近期文献，并描述关于机器学习应用对帕金森综合征研究和临床管理的贡献的当前知识。
