Abstract
The myotonic dystrophies (DM1 and DM2) are dominantly inherited disorders that cause pathological changes throughout the body and the brain. DM patients have difficulties with memory, attention, executive functioning, social cognition, and visuospatial function. Quantifying and understanding diffusion measures along main brain white matter fiber tracts offer a unique opportunity to reveal new insights into DM development and characterization. In this work, a novel supervised system is proposed, which is based on Tract Profiles sub-band energy information. The proposed system utilizes a Bayesian stacked random forest to diagnose, characterize, and predict DM clinical outcomes. The evaluation data consists of fractional anisotropies calculated for twelve major white matter tracts of 96 healthy controls and 62 DM patients. The proposed system discriminates DM vs. control with 86% accuracy, which is significantly higher than previous works. Additionally, it discovered DM brain biomarkers that are accurate and robust and will be helpful in planning clinical trials and monitoring clinical performance.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
More From: Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.