Ruogu Fang is an assistant professor in the J. Crayton Pruitt Family Department of Biomedical Engineering in the Herbert Wertheim College of Engineering. Ruogu's research spans data, brain and health. She focuses on questions such as how to evaluate brain health via mining the big medical data. She also explores how to make medical imaging higher quality and lower risk for the broad population. Her current research is rooted in the big medical data and brain dynamics understanding. Ruogu's SMILE lab aims to develop innovative computational models to understand, diagnose and treat brain disorders in big and complex data.
Areas of Expertise (10)
Medical Image Analysis
Big Medical Data
Neurodegenerative Disease Diagnosis
Big Data Analytics
Media Appearances (4)
Artificial Intelligence Prevents Dementia?
Artificial intelligence, or AI, allows machines to work more efficiently and solve problems faster. AI is all the buzz in the healthcare industry right now. It’s already in the operating room with robot-assisted surgeries and behind the scenes safeguarding your private health records. And now, AI may also help to prevent some diseases, dementia.
UF study shows artificial intelligence’s potential to predict dementia
UF Health online
New research published today shows that a form of artificial intelligence combined with MRI scans of the brain has the potential to predict whether people with a specific type of early memory loss will go on to develop Alzheimer’s disease or other form of dementia.
Our eyes may provide early warning signs of Alzheimer’s and Parkinson’s
The Washington Post print
Forget the soul — it turns out the eyes may be the best window to the brain. Changes to the retina may foreshadow Alzheimer’s and Parkinson’s diseases, and researchers say a picture of your eye could assess your future risk of neurodegenerative disease.
Scientists Are Looking Into The Eyes Of Patients To Diagnose Parkinson’s Disease
With artificial intelligence (AI), researchers have moved toward diagnosing Parkinson's disease with, essentially, an eye exam. This relatively cheap and non-invasive method could eventually lead to earlier and more accessible diagnoses.
CADA: Multi-scale Collaborative Adversarial Domain Adaptation for unsupervised optic disc and cup segmentationNeurocomputing
Peng Liu, et al.
Recently, deep neural networks have demonstrated comparable and even better performance than board-certified ophthalmologists in well-annotated datasets. However, the diversity of retinal imaging devices poses a significant challenge: domain shift, which leads to performance degradation when applying the deep learning models trained on one domain to new testing domains. In this paper, we propose a domain adaptation framework comprising of multi-scale inputs along with multiple domain adaptors applied hierarchically in both feature and output spaces.
Unraveling somatotopic organization in the human brain using machine learning and adaptive supervoxel-based parcellationsNeuroImage
Kyle B. See, et al.
In addition to the well-established somatotopy in the pre- and post-central gyrus, there is now strong evidence that somatotopic organization is evident across other regions in the sensorimotor network. This raises several experimental questions: To what extent is activity in the sensorimotor network effector-dependent and effector-independent? How important is the sensorimotor cortex when predicting the motor effector? Is there redundancy in the distributed somatotopically organized network such that removing one region has little impact on classification accuracy?
Ensemble Machine Learning for Alzheimer’s disease Classification from Retinal VasculatureBiomedical Engineering Society Annual Meeting
Hely Lin and Ruogu Fang
Alzheimer’s disease (AD) causes progressive irreversible cognitive decline and is the leading cause of dementia. Therefore, a timely diagnosis is imperative to maximize neurological preservation. However, current treatments are either too costly or limited in availability. In this project, we explored using retinal vasculature as a potential biomarker for early AD diagnosis.