Quick computer aided differential diagnostics based on repetitive finger tapping in Parkinson's disease and atypical parkinsonisms.

Background: Parkinson's disease (PD) is the second most common neurodegenerative disorder whose prevalence rises with age, yet clinical diagnosis is still a challenging task due to similar manifestations of other neurodegenerative movement disorders. In untreated patients or those with unclear responses to medication, correct percentages of early diagnoses go as low as 26%. Technology has been used in various forms to facilitate discerning between persons with PD and healthy individuals, but much less work has been dedicated to separating PD and atypical parkinsonisms. Methods: A wearable system was developed based on inertial sensors that capture the movements of fingers during repetitive finger tapping. A k-nearest-neighbor classifier was used on features extracted from gyroscope recordings for quick aid in differential diagnostics, discerning patients with PD, progressive supranuclear palsy (PSP), multiple system atrophy (MSA) and healthy controls (HC). Results: The overall classification accuracy achieved was 85.18% in the multiclass setup. MSA and HC groups were the easiest to discern (100%), while PSP was the most elusive diagnosis, as some patients were incorrectly assigned to MSA and HC groups. Conclusions: The system shows potential for use as a tool for quick diagnostic aid, and in the era of big data, offers a means of standardization of data collection that could allow scientists to aggregate multi-center data for further research.

Analysis and prevention of ergonomic risk factors among dental students.

INTRODUCTION: Dentistry is a high-risk profession for the development of work-related disorders. Dental students are also exposed to several risk factors. The goal of the study was to determine and localise musculoskeletal pain during dental work, afterwards to measure electromyography signals from the muscles groups mostly affected by the musculoskeletal pain. Study was done in order to provide possible suggestions for the most effective preventive measures of MS pain among dental students. MATERIALS AND METHODS: In order to solve the objectives, the research was realised in two segments. In the first part of the study, specially designed questionnaires were used to determine the frequency of musculoskeletal pain, risk factors and preventive measures among students. The second part of the study included electromyography analyses of muscular activity of students during dental work. Inclinometers also were set up in the purpose of monitoring inclination of the spine. RESULTS: Results of the questionnaire study indicated that pain during work was frequent, 81.8% of all the subjects reported pain during work. The recorded muscle activity of the neck muscles indicated a high ergonomic risk, while the muscle activity of the shoulders and back muscles indicated a medium risk. Work with a back flexion of 20 degrees and more indicates that students are at risk. CONCLUSION: Dental students used to work in unnatural working position. High ergonomic risk occurred in neck muscles. Students should be aware of the potential risks during work and to learn how to prevent it. Regular physical activity is strongly suggested to the students in order to avoid ergonomic problems.

Impact of depression on gait variability in Parkinson's disease.

OBJECTIVE: The goal of this study was to analyze how depression associated with Parkinson's disease (PD) affected gait variability in these patients using a dual-task paradigm. Additionally, the dependency of the executive functions and the impact of depression on gait variability were analyzed. PATIENTS AND METHODS: Three subject groups were included: patients with PD, but no depression (PD-NonDep; 14 patients), patients with both PD and depression (PD-Dep; 16 patients) and healthy controls (HC; 15 subjects). Gait was recorded using the wireless sensors. The participants walked under four conditions: single-task, motor dual- task, cognitive dual-task, and combined dual-task. Variability of stride length, stride duration, and swing time was calculated and analyzed using the statistical methods. RESULTS: Variability of stride duration and stride length were not significantly different between PD-Dep and PD-NonDep patients. The linear mixed model showed that swing time variability was statistically significantly higher in PD-Dep patients compared to controls (p = 0.001). Hamilton Disease Rating Scale scores were significantly correlated with the swing time variability (p = 0.01). Variability of all three parameters of gait was significantly higher while performing combined or cognitive task and this effect was more pronounced in PD-Dep group of patients. CONCLUSIONS: Depression in PD was associated with swing time variability, and this effect was more prominent while performing a dual-task. SIGNIFICANCE: Diagnosing and treating depression might be important for gait improvement and fall reduction in PD patients.

A Demonstration Project for the Utility of Kinect-Based Educational Games to Benefit Motor Skills of Children with ASD.

Motor disorders often accompany autism spectrum disorder (ASD), although they are not included in its diagnostic criteria. Slower motor development is evident in early childhood in this population, making early motor skills intervention advisable. As educational games and modern technology can represent new forms of treatment, this study evaluated four Kinect-based visuo-motor games called Fruits that were specially designed for this research. We sought to test whether children with ASD would show behavior changes during their game play and whether any effects would generalize to another game called Rackets. The study included 10 elementary school children with ASD, aged 9-13 years, who were divided into (a) an experimental group (n = 5) who, in addition to standard treatment, played Fruits once a week for a 5-week period and Rackets both before and after the 5-week period and (b) a control group (n = 5) who received only standard treatment during this period and also played Rackets before and after it. We found significant improvements in gross motor skills and successful generalization of acquired skills among children in the experimental group relative to the control group. The experimental group also showed an increase in positive emotions and a decrease in loss of attention while playing the games. These preliminary findings indicate a motor skill benefit for children with ASD who play Kinect-based educational games, but further research is needed to replicate and expand these findings with larger participant samples.

Artificial intelligence for assisting diagnostics and assessment of Parkinson's disease-A review.

Artificial intelligence, specifically machine learning, has found numerous applications in computer-aided diagnostics, monitoring and management of neurodegenerative movement disorders of parkinsonian type. These tasks are not trivial due to high inter-subject variability and similarity of clinical presentations of different neurodegenerative disorders in the early stages. This paper aims to give a comprehensive, high-level overview of applications of artificial intelligence through machine learning algorithms in kinematic analysis of movement disorders, specifically Parkinson's disease (PD). We surveyed papers published between January 2007 and January 2019, within online databases, including PubMed and Science Direct, with a focus on the most recently published studies. The search encompassed papers dealing with the implementation of machine learning algorithms for diagnosis and assessment of PD using data describing motion of upper and lower extremities. This systematic review presents an overview of 48 relevant studies published in the abovementioned period, which investigate the use of artificial intelligence for diagnostics, therapy assessment and progress prediction in PD based on body kinematics. Different machine learning algorithms showed promising results, particularly for early PD diagnostics. The investigated publications demonstrated the potentials of collecting data from affordable and globally available devices. However, to fully exploit artificial intelligence technologies in the future, more widespread collaboration is advised among medical institutions, clinicians and researchers, to facilitate aligning of data collection protocols, sharing and merging of data sets.

White Matter Changes in Two Leber's Hereditary Optic Neuropathy Pedigrees: 12-Year Follow-Up.

We are presenting two Leber's hereditary optic neuropathy (LHON) pedigrees with abnormal magnetic resonance imaging (MRI) and proton magnetic resonance spectroscopy (H-MRS) findings but without neurological manifestation associated with LHON. The study included 14 LHON patients and 41 asymptomatic family members from 12 genealogically unrelated families. MRI showed white matter involvement and H-MRS exhibited metabolic anomalies within 12 LHON families. Main outcome measures were abnormal MRI and H-MRS findings in two pedigrees. MRI of the proband of the first pedigree showed a single demyelinating lesion in the right cerebellar hemisphere, while the proband of the second family displayed multiple supratentorial and infratentorial lesions, compatible with the demyelinating process, and both the absolute choline (Cho) concentration and Cho/creatinine ratio were increased. MRI and H-MRS profiles of both affected and unaffected mitochondrial DNA mutation carriers suggest more widespread central nervous involvement in LHON. Although even after 12 years our patients did not develop neurological symptoms, MRI could still be used to detect possible changes during the disease progression.