More accurate diagnoses. Earlier treatment. Care tailored to you.
At UFCD, AI helps our clinicians detect disease earlier, plan treatment more precisely and tailor care to each patient. The tools being tapped into support clinical judgment by providing clearer, more reliable information, so patients receive the most accurate and consistent care.
AI-Assisted Radiology
Detecting cavities and bone loss earlier
AI-enhanced radiographs apply color-coded annotations that highlight decay and quantify bone loss in real time. Cleared by the U.S. Food and Drug Administration (FDA), the Overjet Caries Assist module helps dentists spot early-stage issues that are notoriously difficult to identify.
In clinical validation studies conducted at UFCD, this technology detects:
91% of dentinal cavities
69% of early enamel lesions
This heightened diagnostic sensitivity enables earlier intervention, allowing clinicians to deliver conservative, preventative care before invasive procedures are required.
In the News:
- “Students hone their dental AI skills while helping their patients” (2024, UF News)
Predictive Smile Design
Getting a more natural match the first time
When using Computer-Aided Design/Computer-Aided Manufacturing (CAD/CAM) software to craft ceramic restorations, predicting the exact final color is a long-standing challenge due to the complex interaction between the ceramic and the underlying tooth.
To address this, UFCD researchers have developed machine-learning regression models to accurately predict the final color of a veneer before it is milled.
By actively accounting for highly specific variables, including the underlying substrate (tooth) shade, ceramic material thickness and translucency, this computational approach is designed to replace subjective visual color matching with objective mathematical evidence.
This AI-driven research will empower clinicians to take the guesswork out of cosmetic dentistry, ensuring more consistent, natural-looking results for patients the very first time. The UFCD research behind this innovation in restorative dentistry focused on accounting for how multiple interacting factors influence a restoration’s final color before fabrication.
Context:
UFCD research in AI and dental materials focuses on translating laboratory findings into clinical tools.
- “Rocha Presents AI in Dental Materials” (UFCD News, 2021)
Data-Informed Orthodontic Planning
Helping identify the right time for treatment
One of the biggest questions in orthodontic care is timing. Starting treatment too early can lead to unnecessary procedures, while waiting too long can make problems harder to correct later.
At UFCD, researchers from the Karanth Ortho Innovation Lab are using artificial intelligence to make these decisions more precise and consistent by analyzing thousands of past cases to better understand how treatment timing affects real patients over time.
How the technology works
Natural language processing (NLP)
AI can analyze detailed orthodontic clinical notes, extracting key diagnostic and treatment information that would otherwise be difficult to study at scale.
Machine learning models
These structured datasets are used to identify patterns across thousands of cases, helping determine which treatments are most effective and when they tend to succeed.
Growth-based assessment
Machine learning models also analyze imaging data to assess skeletal maturity, helping clinicians better understand a child’s stage of development.
Why this matters for your child
By combining insights from past patient outcomes ranging multiple decades with information about a child’s current growth, this approach helps orthodontists:
- determine the most effective time to begin treatment
- reduce unnecessary or premature interventions
- support more consistent, evidence-based decisions
This leads to a more personalized treatment plan that aligns with how your child is actually growing and developing.
This work was presented at UFCD’s 2026 Spring Synergy symposium, where researchers demonstrated how AI can turn decades of orthodontic records into data that improves patient care.
AI to detect early signs of root damage
Monitoring treatment to protect your child’s teeth
Root resorption is a known but difficult-to-detect risk during orthodontic treatment, where the structure of a tooth’s root can gradually break down. In many cases, these changes are subtle and may not become visible until significant damage has already occurred.
In collaboration with UF’s Research Computing department, researchers from Karanth’s Ortho Innovation Lab have developed an AI-enabled tool called QuantRoot to address this challenge.
Using advanced imaging from cone-beam computed tomography (CBCT), the system analyzes 3D scans of teeth and measures changes in root structure with a high level of precision.
Unlike traditional assessments, which rely on visual estimation, QuantRoot can quantify root resorption as actual volume loss, giving clinicians a clearer and more objective picture of what is happening beneath the surface.
In collaboration with UFCD’s Center for Dental Biomaterials, a machine learning model was also developed to classify the severity of root resorption.
This allows orthodontists to:
- detect early signs of root resorption sooner
- monitor subtle changes throughout treatment
- adjust treatment plans before damage progresses
Personalized Pain Management
Understanding your unique pain experience
Pain presents uniquely in every patient due to interacting biological and psychosocial factors.
UFCD researchers use artificial intelligence to analyze routine brain scans and identify the specific brain signals, or neurobiological markers, of a patient’s pain. Clinicians use this data to develop targeted care plans aimed at the specific causes of pain-related disability.
The link between chronic pain and brain health
Using an AI model called DeepBrainNet, a convolutional neural network pre-trained on more than 11,000 diverse brain scans to estimate biological age, researchers analyze MRI scans to calculate a patient’s “biological brain age”.
Studies show that individuals with high-impact chronic pain often have brains that appear older than their chronological age. This brain-age metric functions as an early indicator for potential physical and cognitive decline.
Taking control: The power of pain management
Treating and managing pain can slow this aging process and protect long-term brain health. Research shows that older adults who actively seek pain relief, whether through medications or home remedies like a heating pad, have younger-appearing brains compared to those who leave their pain untreated.
Pioneering treatments and lifestyle medicine
UFCD is actively finding ways to reverse the negative effects of pain through lifestyle medicine and novel therapies:
- The UCOPE Trial: Researchers are currently testing whether an easy-to-use intranasal oxytocin spray can act as a non-addictive pain reliever while physically reversing the accelerated brain aging induced by chronic pain.
- The PANDA-1 Study: Researchers are investigating how nutrition and anti-inflammatory diets (measured by the Dietary Inflammatory Index) might protect the brain, prevent biological aging and mitigate cognitive decline caused by chronic pain.
In the News:
“Dr. Cruz-Almeida and collaborators shows chronic pain might accelerate brain aging” (2019)
Expanding Access to Personalized Care
Ensuring no patient is left behind