Background: Parkinson’s disease (PD) is the second most common neurodegenerative disorder after Alzheimer’s disease and gender differences have been described on several aspects of PD. In the present commentary, we aimed to collect and discuss the currently available evidence on gender differences in PD regarding biomarkers, genetic factors, motor and non-motor symptoms, therapeutic management (including pharmacological and surgical treatment) as well as preclinical studies. Methods: A systematic literature review was performed by searching the Pubmed and Scopus databases with the search strings “biomarkers”, “deep brain stimulation”, “female”, “gender”, “genetic”, “levodopa”, “men”, “male”, “motor symptoms”, “non-motor symptoms”, “Parkinson disease”, “sex”, “surgery”, and “women”. Results: The present review confirms the existence of differences between men and women in Parkinson Disease, pointing out new information regarding evidence from animal models, genetic factors, biomarkers, clinical features and pharmacological and surgical treatment. Conclusions: The overall goal is to acquire new informations about sex and gender differences in Parkinson Disease, in order to develop tailored intervetions.

Background: Parkinson’s disease is the second most frequent neurodegenerative disorder. Its diagnosis is challenging and mainly relies on clinical aspects. At present, no biomarker is available to obtain a diagnosis of certainty in vivo. Objective: The present review aims at describing machine learning algorithms as they have been variably applied to different aspects of Parkinson’s disease diagnosis and characterization. Methods: A systematic search was conducted on PubMed in December 2019, resulting in 230 publications obtained with the following search query: “Machine Learning” “AND” “Parkinson Disease”. Results: The obtained publications were divided into 6 categories, based on different application fields: “Gait Analysis - Motor Evaluation”, “Upper Limb Motor and Tremor Evaluation”, “Handwriting and typing evaluation”, “Speech and Phonation evaluation”, “Neuroimaging and Nuclear Medicine evaluation”, “Metabolomics application”, after excluding the papers of general topic. As a result, a total of 166 articles were analyzed after elimination of papers written in languages other than English or not directly related to the selected topics. Conclusion: Machine learning algorithms are computer-based statistical approaches that can be trained and are able to find common patterns from big amounts of data. The machine learning approaches can help clinicians in classifying patients according to several variables at the same time.

Parkinson’s disease (PD) is the second most common movement disorder in the world. It is characterized by motor and non-motor symptoms that have a profound impact on the independence and quality of life of people affected by the disease, which increases caregivers’ burdens. The use of the quantitative gait data of people with PD and deep learning (DL) approaches based on gait are emerging as increasingly promising methods to support and aid clinical decision making, with the aim of providing a quantitative and objective diagnosis, as well as an additional tool for disease monitoring. This will allow for the early detection of the disease, assessment of progression, and implementation of therapeutic interventions. In this paper, the authors provide a systematic review of emerging DL techniques recently proposed for the analysis of PD by using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The Scopus, PubMed, and Web of Science databases were searched across an interval of six years (between 2018, when the first article was published, and 2023). A total of 25 articles were included in this review, which reports studies on the movement analysis of PD patients using both wearable and non-wearable sensors. Additionally, these studies employed DL networks for classification, diagnosis, and monitoring purposes. The authors demonstrate that there is a wide employment in the field of PD of convolutional neural networks for analyzing signals from wearable sensors and pose estimation networks for motion analysis from videos. In addition, the authors discuss current difficulties and highlight future solutions for PD monitoring and disease progression.

The aim of this study was to determine a gait pattern, i.e., a subset of spatial and temporal parameters, through a supervised machine learning (ML) approach, which could be used to reliably distinguish Parkinson’s Disease (PD) patients with and without mild cognitive impairment (MCI). Thus, 80 PD patients underwent gait analysis and spatial–temporal parameters were acquired in three different conditions (normal gait, motor dual task and cognitive dual task). Statistical analysis was performed to investigate the data and, then, five ML algorithms and the wrapper method were implemented: Decision Tree (DT), Random Forest (RF), Naïve Bayes (NB), Support Vector Machine (SVM) and K-Nearest Neighbour (KNN). First, the algorithms for classifying PD patients with MCI were trained and validated on an internal dataset (sixty patients) and, then, the performance was tested by using an external dataset (twenty patients). Specificity, sensitivity, precision, accuracy and area under the receiver operating characteristic curve were calculated. SVM and RF showed the best performance and detected MCI with an accuracy of over 80.0%. The key features emerging from this study are stance phase, mean velocity, step length and cycle length; moreover, the major number of features selected by the wrapper belonged to the cognitive dual task, thus, supporting the close relationship between gait dysfunction and MCI in PD.

Introduction: Depression is one of the most disabling neuropsychiatric manifestations of Parkinson’s disease (PD) and requires proper screening and diagnosis because it affects the overall prognosis and quality of life of patients. This study aimed to assess the psychometric and diagnostic properties of the Beck Depression Inventory-II (BDI-II) in an Italian PD cohort. Materials and methods: Fifty consecutive outpatients with PD underwent the Italian version of the BDI-II and other questionnaires to evaluate anxiety and apathetic symptoms. Patients’ caregivers completed the depression/dysphoria domain of the Neuropsychiatric Inventory (NPI-D). We evaluated the internal consistency, convergent and divergent validity, and factorial structure of BDI-II. Sensitivity, specificity, positive and negative predictive values, and likelihood ratios were computed using ROC analyses, and an optimal cutoff was defined using the Youden index. Results: The BDI-II proved to be internally consistent (Cronbach’s α = 0.840) and substantially met the bi-factorial structure. Regarding construct validity, the BDI-II was substantially related to anxiety measures, but not to apathy. With the combination of the NPI-D and anxiety score used as the gold standard, the BDI-II overall showed good accuracy (AUC = 0.859) with adequate sensitivity (75%) and specificity (87%). The optimal cutoff point was defined at 14.50. Conclusions: We provide evidence of the psychometric and diagnostic properties of the Italian version of the BDI-II as a screening tool for depression in patients with PD. The BDI-II was found to be reliable and valid for the measurement of depression in patients with PD; therefore, it is available for use in clinical research and practice.