Ruogu Fang is a tenured associate professor and Pruitt Family Endowed Faculty Fellow in the J. Crayton Pruitt Family Department of Biomedical Engineering. Her research revolves around the integration of artificial intelligence (AI) and deep learning with the intricacies of the human brain. Her research encompasses two principal themes: AI-empowered precision brain health and brain/bio-inspired AI. Her work involves addressing compelling questions, such as using machine learning techniques to quantify brain dynamics, facilitating early Alzheimer's disease diagnosis through novel imagery, predicting personalized treatment outcomes, designing precision interventions, and leveraging principles from neuroscience to develop the next generation of AI. Fang's current research is also rooted in the confluence of AI and multimodal medical image analysis. At the heart of her work is the Smart Medical Informatics Learning and Evaluation (SMILE) lab, where she is tirelessly dedicated to the creation of groundbreaking brain and neuroscience-inspired medical AI and deep learning models. The primary objective of these models is to comprehend, diagnose, and treat brain disorders, all while navigating the complexities of extensive and intricate datasets.
Areas of Expertise (10)
Medical Image Analysis
Big Medical Data
Neurodegenerative Disease Diagnosis
Big Data Analytics
Media Appearances (4)
Saluting the trailblazers: Academy of Science, Engineering and Medicine of Florida names honorees from UF
UF Powering the New Engineer online
Dr. Fang was recognized for her pioneering contributions in using medical AI and deep learning models to diagnose, predict and treat brain diseases that include Alzheimer’s and Depression. Her dedication to mentoring diverse, transdisciplinary, next-gen researchers has also won her far and wide praise among her peers and students.
UF Health Cancer Center hosts 3rd annual AI Day
UF Health online
This week, the UF Health Cancer Center hosted its 3rd Annual AI Day in Cancer Research, drawing speakers and attendees from a range of disciplines, such as engineering, public health, data science and radiology, to learn more about the role of AI in cancer research.
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.
Texture and motion aware perception in-loop filter for AV1Journal of Visual Communication and Image Representation
Tianqi Liu, et. al
Lossy compression introduces artifacts, and many conventional in-loop filters have been adopted in the AV1 standard to reduce these artifacts. Researchers have explored deep learning-based filters to remove artifacts in the compression loop. However, the high computational complexity of CNN-based filters remains a challenge.
Precise and Rapid Whole-Head Segmentation from Magnetic Resonance Images of Older Adults using Deep LearningImaging Neuroscience
Skylar E. Stolte, et. al
Whole-head segmentation from Magnetic Resonance Images (MRI) establishes the foundation for individualized computational models using finite element method (FEM). This foundation paves the path for computer-aided solutions in fields, particularly in non-invasive brain stimulation. Most current automatic head segmentation tools are developed using healthy young adults. Thus, they may neglect the older population that is more prone to age-related structural decline such as brain atrophy.
Ethnic disparity in diagnosing asymptomatic bacterial vaginosis using machine learningnpj Digital Medicine - Nature
Cameron Celeste, et. al
While machine learning (ML) has shown great promise in medical diagnostics, a major challenge is that ML models do not always perform equally well among ethnic groups. This is alarming for women’s health, as there are already existing health disparities that vary by ethnicity. Bacterial Vaginosis (BV) is a common vaginal syndrome among women of reproductive age and has clear diagnostic differences among ethnic groups