Researchers at the University of Toronto and the University of Putura America have developed an advanced machine learning method to use the brain imaging technology of brain activity during the tracking rest period to improve the detection of Parkinson’s disease. This study was led by Dr. Gabriel Solana-Lavalle and his colleagues. It applied for causal forest machine learning algorithms to analyze the brain activity mode, providing a highly accurate method for identifying Parkinson’s disease. The worst brain area. The results of the research were published in the journal of Tag scanning.
Dr. Solana-Lavalle and Michael Cusimano, Dr. Thomas Steeves, Roberto Rosas-Romero, and Dr. Pascal Tyrrell designed a machine learning model to process brain scanning information to accurately classify Parkinson’s patients. “Our method focuses on reducing unique combinations of unnecessary data, and at the same time ensure that we can still clearly understand which brain areas are affected by Parkinson’s disease” Dr. Solan-Lavalle explained.
The research team analyzed the data from the Parkinson’s Progress Progress Plan and additional control data from another public database. The database collected brain scan data from each research site. They deal with more than 200 individual brain scans, apply causal forests and packaging features to select algorithms to filter out noise and unnecessary information, and focus on the most closely related brain areas with Parkinson’s disease. Instrument performance.
In order to manage the quality of the data and the changes in the capture conditions, the team uses advanced data processing technology, including video alignment and standardization. “This data-driven method provides an interpreted insight in brain areas closely related to Parkinson’s disease, which can help clinicians better understand the progress of the disease and develop personalized treatment,” Dr. Solana-Lavalle added.
Studies have found that compared with the health control group, Parkinson’s specific brain areas have shown significant changes. The causal forest algorithm ranks these areas based on the correlation of these areas, so that statistical tools can be used to visualize and explain the activation model of Parkinson’s disease and non -affected groups. This method is effective for different people, showing high accuracy to both men and women.
The potential of this method is not limited to the diagnosis, but it can also understand how Parkinson’s disease affects different brain areas. This method also determines the correlation between the activation of certain brain areas and the movement part of the UPDRS. UPDRS is a clinical assessment tool that measures various sports functions.
This research laid the foundation for the future research of machine learning models to improve other neurodegenerative diseases. By emphasizing explanatory and performance, this method can help clinicians diagnose Parkinson’s disease and understand their different impacts on patients.
The study represents major progress in applying machine learning to medical image and neurodegenerative disease testing. Looking forward to the future, Dr. Sorana Lawal and his team plans to expand their methods and include long-term research into it, hoping to track the progress of Parkinson’s disease over time.
Journal reference
Solana-Lavalle, G., Cusimano, MD, Steeves, T., Rosas-Romero, R. and Tyrrell, Pn (2024). “Analysis of causal forest machine learning of Parkinson’s disease in static functional magnetic vibrations.” Fleece scanning. Number number:
About the author
Gabriel Soran-Lawal Get a doctorate degree. He obtained a doctorate degree in smart systems at the University of Pu Era, Mexico in 2023. In 2022, the Institute of Medical Sciences at the University of Toronto visited graduate students. He is currently cooperating with industrial partners to carry out a project aimed at developing and implementing medical imaging signal processing innovative technologies.
Pascar TyrierHe is an outstanding data scientist, and is also the director and associate professor at the Department of Medicine Imaging at the University of Toronto. He founded the MIDATA data science project and worked at the Institute of Medical Science and the Department of Statistics. His research uses innovative artificial intelligence to medical image analysis to improve health results. Pascal is also a continuous entrepreneur who has rich experience in computer software, medical equipment and agricultural technology.
Professor Roberto Rosas-Romelo He obtained a PhD in motor engineering from the University of Washington. He is a professor at the Department of Electric and Computer Engineering of the University of Putura America (Mexico). He is a guest professor at the Department of Radiology at the Department of Diagnostics at Yale University. He has twice become a Fubwreter scholar, as a student of Washington University and a guest professor at Yale University. His research interests include signal processing, computer vision, mode recognition, machine learning and medical image analysis. His research has been applied to ultrasound image segmentation, forest fire detection in video signals, microorphosis testing in the bottom image of the bottom of the eyes to auxiliary diagnostic diabetic retinopathy, based on brain wave prediction seizures, deafness testing in newborn crying, newborn crying, Detecting micro -calcification on breast X -ray, detecting Parkinson’s disease through analysis sound, and classification of magnetic vibration angiography to assist Parkinson’s disease diagnosis and classify skin burns in color images.
Michael D. Kusmano He is a professor of neurosurgery and public health science at the University of Toronto. As Canada, Canada, the first skilled underground surgeon, who had been trained in formal training, in 1993, he developed the global bilateral full -visual mirror surgery. “Bottom Surgery Manual” and more than 450 published publishing products. He is not only one of the most important and popular neurosurgeons in the country, but also an internationally recognized expert in traumatic brain injury. His work has helped change the public’s understanding of brain shocks, and to make the changes in policies and policies, Contribution. His highly cooperative work also emphasizes the importance of the quality of life quality and the importance of using the latest advanced data analysis in his career in his career, especially in terms of measurement, artificial intelligence and geography. Dr. Cusimano founded the St. Michael Hospital’s injury prevention research office, served as the director of the country, and then served as the vice president of the Think FIRST National Investigation Prevention Foundation for more than ten years. The scientific consultant of brain shocks, as well as academicians of the Canadian Academy of Health, admits his contribution of his opponent and the impact on domestic and foreign public policies. He has a doctorate degree in education, promoting the development of medical surgery education and evaluation models, and has been committed to educating the general public and a generation of doctors and neurosurgeons who contribute to the field today. He is an indispensable advocate of brain health and brain injury prevention.
