Parkinson's Disease

Resting-state electroencephalography based deep-learning for the detection of Parkinson's disease.

Parkinson's disease (PD) is one of the most serious and challenging neurodegenerative disorders to diagnose. Clinical diagnosis on observing motor symptoms is the gold standard, yet by this point nerve cells are degenerated resulting in a lower efficacy of therapeutic treatments. In this study, we introduce a deep-learning approach based on a recently-proposed 20-Layer Convolutional Neural Network...

Robot-assisted gait training with auditory and visual cues in Parkinson's disease: A randomized controlled trial.

BACKGROUND: Robot-assisted gait training (RAGT) may have beneficial effects on Parkinson's disease (PD); however, the evidence to date is inconsistent.

Predicting Motor Responsiveness to Deep Brain Stimulation with Machine Learning.

Deep brain stimulation is a complex movement disorder intervention that requires highly invasive brain surgery. Clinicians struggle to predict how pat...

Antemortem detection of Parkinson's disease pathology in peripheral biopsies using artificial intelligence.

The diagnosis of Parkinson's disease (PD) is challenging at all stages due to variable symptomatology, comorbidities, and mimicking conditions. Postmo...

Robot-assisted Total Mesorectal Excision and Lateral Pelvic Lymph Node Dissection for Locally Advanced Middle-low Rectal Cancer.

Since their approval for clinical use, da Vinci surgical robots have shown great advantages in gastrointestinal surgical operations, especially in com...

PDGNet: Predicting Disease Genes Using a Deep Neural Network With Multi-View Features.

The knowledge of phenotype-genotype associations is crucial for the understanding of disease mechanisms. Numerous studies have focused on developing e...

Deep Learning-Based Neuromelanin MRI Changes of Isolated REM Sleep Behavior Disorder.

BACKGROUND: Isolated REM sleep behavior disorder (iRBD) is considered a prodromal stage of parkinsonism. Neurodegenerative changes in the substantia n...

Data-driven geometric system identification for shape-underactuated dissipative systems.

Modeling system dynamics becomes challenging when the properties of individual system components cannot be directly measured, and often requires ident...

Metaheuristics with Deep Learning-Enabled Parkinson's Disease Diagnosis and Classification Model.

Parkinson's disease (PD) affects the movement of people, including the differences in writing skill, speech, tremor, and stiffness in muscles. It is s...

Identification of an early-stage Parkinson's disease neuromarker using event-related potentials, brain network analytics and machine-learning.

OBJECTIVE: The purpose of this study is to explore the possibility of developing a biomarker that can discriminate early-stage Parkinson's disease fro...

Robotically-induced hallucination triggers subtle changes in brain network transitions.

The perception that someone is nearby, although nobody can be seen or heard, is called presence hallucination (PH). Being a frequent hallucination in ...

Robotics, automation, active electrode arrays, and new devices for cochlear implantation: A contemporary review.

In the last two decades, cochlear implant surgery has evolved into a minimally invasive, hearing preservation surgical technique. The devices used dur...

Deep learning reveals personalized spatial spectral abnormalities of high delta and low alpha bands in EEG of patients with early Parkinson's disease.

Parkinson's disease (PD) is one of the most common neurodegenerative diseases, and early diagnosis is crucial to delay disease progression. The diagno...

Combination of liquid crystal and deep learning reveals distinct signatures of Parkinson's disease-related wild-type α-synuclein and six pathogenic mutants.

α-Synuclein is a central player in Parkinson's disease (PD) pathology. Various point mutations in α-synuclein have been identified to alter the protei...

Current status of robot-assisted minimally invasive esophagectomy: what is the real benefit?

Robot-assisted minimally invasive esophagectomy (RAMIE) for esophageal cancer has been performed increasingly frequently over the last few years. Robo...

Data-driven identification of diagnostically useful extrastriatal signal in dopamine transporter SPECT using explainable AI.

This study used explainable artificial intelligence for data-driven identification of extrastriatal brain regions that can contribute to the interpret...

Fully automated deep learning-based localization and segmentation of the locus coeruleus in aging and Parkinson's disease using neuromelanin-sensitive MRI.

PURPOSE: Development and performance measurement of a fully automated pipeline that localizes and segments the locus coeruleus in so-called neuromelan...

Effect of data leakage in brain MRI classification using 2D convolutional neural networks.

In recent years, 2D convolutional neural networks (CNNs) have been extensively used to diagnose neurological diseases from magnetic resonance imaging ...

Self-paced learning and privileged information based KRR classification algorithm for diagnosis of Parkinson's disease.

Computer aided diagnosis (CAD) methods for Parkinson's disease (PD) can assist clinicians in diagnosis and treatment. Magnetic resonance imaging (MRI)...

Tensegrity Robotics.

Numerous recent advances in robotics have been inspired by the biological principle of tensile integrity-or "tensegrity"-to achieve remarkable feats o...