In a study from Oxford University, researchers found that by using a combination of wearable sensor data and machine learning algorithms the progression of Parkinson's disease can be monitored more accurately than in traditional clinical observation. Monitoring movement data collected by sensor technology may not only improve predictions about disease progression but also allows for more precise diagnoses. Parkinson's disease is a neurological condition that affects motor control and movement. Although there is currently no cure, early intervention can help delay the progression of the disease in patients. Diagnosing and tracking the progression of Parkinson's disease currently involves a neurologist using the Movement Disorder Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS) to assess the patient's motor symptoms by assigning scores to the performance of specific movements.
This appendix contains the following sections: Proofs: Section A contains proofs of theoretical results presented in the main paper. Semi-Synthetic Experiment: Additional Details: Section B contains additional details and experimental results for the semi-synthetic experiment presented in Section 4. Diabetes Experiment: Additional Details: Section C contains additional details and experimental results for the real-data diabetes experiment presented in Section 5. Additional Real-Data Experiment: Parkinson's: Section D contains an additional realdata experiment, on a medical dataset of patients with Parkinson's disease. Most of our experiments were run on CPUs, with only the TARNet baseline run on a GEForce GTX GPU. We estimate the compute time to be on the order of 100 hours. Based on the definition of Y(π(x)), we can write it as follows, using the fact that Y is binary. Meanwhile, E[Y | x] is a function of x alone, and so the conditional expectation is equivalent if we condition on additional information E[E[Y | x] | x] = E[E[Y | x] | X = x, A = a, X S ] as long as this conditional expectation is well-defined, which it will be wherever p(x | A = a, X S) > 0. The partially maximized population objective from Equation (6) is equivalent (up to a factor of 2) to a weighted sum of the absolute value of each agent's conditional relative agent bias. First, we will prove the following lemma: Lemma 1.
Drosophila that represents one of the models of neurodegeneration used in the lab to screen for things (both chemically and genetically) that regulate mitophagy. A new study, published in the journal PLOS Biology, suggests that the language used by researchers in describing their results can be utilized to uncover new treatments for Parkinson's disease. The study, led by Angus McQuibban of the University of Toronto in Canada, utilized AI to find an existing anti-cholesterol medication that has the capability to enhance the disposal of mitochondria, which are cellular components responsible for energy production and are affected in Parkinson's disease. The full pathogenic pathway leading to Parkinson's disease (PD) is unknown, but one clear contributor is mitochondrial dysfunction and the inability to dispose of defective mitochondria, a process called mitophagy. At least five genes implicated in PD are linked to impaired mitophagy, either directly or indirectly, and so the authors sought compounds that could enhance the mitophagy process.
Parkinson's disease is a progressive nervous system disorder that affects movement and muscle control. Lithuanian researchers from Kaunas University of Technology (KTU) utilized AI to identify the early signs of Parkinson's disease using voice data. The diagnosis of Parkinson's disease has shaken many lives, with over 10 million people currently living with the condition. Although there is no cure, early detection of symptoms can lead to better management of the disease. As the disease progresses, changes in speech can occur alongside other symptoms.
Lithuanian researcher from Kaunas University of Technology (KTU), Rytis Maskeliunas, together with colleagues from the Lithuanian University of Health Sciences (LSMU), tried to identify early symptoms of Parkinson's disease using voice data. Parkinson's disease is usually associated with loss of motor function – hand tremors, muscle stiffness, or balance problems. According to Maskeliunas, a researcher at KTU's Department of Multimedia Engineering, as motor activity decreases, so does the function of the vocal cords, diaphragm, and lungs: "Changes in speech often occur even earlier than motor function disorders, which is why the altered speech might be the first sign of the disease." According to Professor Virgilijus Ulozas, at the Department of Ear, Nose, and Throat at the LSMU Faculty of Medicine, patients with early-stage of Parkinson's disease, might speak in a quieter manner, which can also be monotonous, less expressive, slower, and more fragmented, and this is very difficult to notice by ear. As the disease progresses, hoarseness, stuttering, slurred pronunciation of words, and loss of pauses between words can become more apparent.
A machine-learning tool that combines genetic and clinical data can distinguish, with a high level of accuracy, between fast- and slow-progressing Parkinson's patients, a study reported. The tool could be used to predict those patients who are more likely to progress over the short term -- and who could be the target population for clinical trials of therapies aiming to slow disease progression. "If clinicians are able to enroll in trials only those patients predicted to progress, they can get much faster results and move this field along more quickly," Ali Torkamani, PhD, the study's senior author and an assistant professor of molecular and experimental medicine at the Scripps Research Translational Institute, said in an institute press release. "Right now, [Parkinson's] clinical trials are large and tend to take two to three years. We're hoping to empower smaller trials that are on the order of a one-year time frame," Torkamani added.
Parkinson's disease is a progressive disorder that affects the nervous system and the parts of the body controlled by the nerves. Symptoms are also not that sound to be noticeable. Signs of stiffening, tremors, and slowing of movements may be signs of Parkinson's disease. But there is no ascertain way to tell whether a person has Parkinson's disease or not because there are no such diagnostics methods available to diagnose this disorder. But what if we use machine learning to predict whether a person suffers from Parkinson's disease or not?
Researchers from the University of Tsukuba and IBM Research find that automatic analysis of patients' drawings can help differentiate between Alzheimer's disease and dementia with Lewy bodies Tsukuba, Japan--The two most common neurodegenerative dementias are Alzheimer's disease (AD) and dementia with Lewy bodies (DLB). There is often an overlap of symptoms across these two diseases, which can make diagnoses difficult. Although biomarkers in cerebrospinal fluid sampling and neuroimaging are the most well-validated diagnostic biomarkers, they can be invasive, time-consuming, and expensive. Researchers in Japan have discovered that the characteristics of patients' drawing process can discriminate between patients with AD and DLB, offering a cheap, non-invasive, and quick screening tool. Recently, an analysis of drawing tests has been shown to be useful for the identification of AD as well as Parkinson's disease, another form of Lewy body spectrum disorders.
Researchers at MIT have developed an AI system that can diagnose Parkinson's disease and track its progression, simply by monitoring someone's breathing patterns as they sleep. The device looks like an internet router and can be mounted on the wall in a bedroom. It emits radio waves and then a neural network analyzes the reflected waves to assess breathing patterns. Crucially, the technology may be able to assist in diagnosing Parkinson's disease much earlier than many conventional techniques and it is highly convenient and non-invasive compared with traditional diagnostics. It may also be particularly beneficial in testing new treatments for Parkinson's as a non-invasive method to monitor disease progression.
New results from the PPMI Data Modeling Core reveal the power of digital health technologies to remotely detect motor symptoms of Parkinson's Disease Brain research and advocacy non-profit Cohen Veterans Bioscience (CVB) announces the publication of results from its digital health research program analyzing data from the Parkinson's Progression Markers Initiative (PPMI) to detect the presence or absence of Parkinson's disease (PD). PD is one of the most common and fastest growing neurological disorders that results in a progressive decline in both motor and non-motor (e.g., cognition and mood) symptoms. Since there are currently no objective biomarkers in PD, diagnosis is complicated and typically involves clinically administered subjective questionnaires to assess severity of symptoms, potentially leading to symptoms being undetected or misclassified. Sensor technology has shown promise in aiding in detection and classification of diseases like PD but have very limited validation in real-world settings. As part of the Parkinson's Progression Markers Initiative (PPMI) study cohort, investigators collected data passively and continuously using the Verily Study Watch in a subject's natural environment.