Dentists are often the first health professionals to see children with arthritis because the temporomandibular joints (TMJ) that enable chewing, speaking and swallowing are usually the first — and sometimes the only — joints affected.
Early diagnosis can help prevent pain, swelling, stiffness, reduced range of motion and even permanent structural damage that can lead to growth problems, facial deformities and dysfunction.
Now, a ¾ÅÐãÖ±²¥ team is working to improve screening for the disease and make that diagnosis quicker and safer by combining hand-held ultrasound and artificial intelligence.
There are significant downsides to the current methods of diagnosis for pediatric TMJ arthritis: Cone-beam computed tomography (CBCT) uses potentially harmful radiation, and magnetic resonance imaging (MRI) is expensive, time-consuming and may require sedation.
Ultrasound is much safer and cheaper, according to principal investigator , an assistant professor of oral and maxillofacial radiology in the Mike Petryk School of Dentistry.
“Ultrasound has no radiation and is portable, but it’s not always an easy image to see,” Almeida says. “That’s why we are incorporating AI — to improve the image itself and its interpretation — so we can bring the technology to clinicians as a chairside screening tool.”
Almeida is one of six U of A researchers to receive support from the competition, which awarded a total of $9.9 million to support digital and data-enabled health innovations in the province.
Juvenile idiopathic arthritis is the most common rheumatic disease among children, affecting as many as 25,000 Canadians, according to the . More than 90 per cent of children with juvenile arthritis have it in their temporomandibular joints.
Almeida is working with a team of radiologists, oral surgeons, pediatric rheumatologists and biomedical engineers at the U of A and the University of Calgary.
They will evaluate the performance of hand-held ultrasound in imaging TMJ arthritis, investigate whether AI can create 3D images to improve that performance further, and develop AI models that automatically identify TMJ changes in the images. Almeida will supervise an undergraduate and three graduate students in doing the research.
Ultrasound hasn’t been used traditionally in dentistry, but Almeida has been working in the field for eight years, with a team of researchers including dentistry professor and former chair and radiology and diagnostic imaging professor , both experts in ultrasound innovations.
In earlier research, Almeida designed an using artificial intelligence to see the joint clearly and , gaining insights that will be put to use in this project. She hopes that once signs of arthritis are identified in a patient using her method, the child would be referred to a pediatric rheumatologist who would use MRI to confirm the diagnosis.
Almeida is a member of the and the U of A’s AI & Health Hub.
“Undiagnosed TMJ arthritis can lead to a child not being able to bite properly, it impacts breathing and can even lead to other conditions such as sleep apnea,” Almeida says. “With this multidisciplinary work, our primary goal is to improve the early screening for this disease and have a positive impact on the overall quality of life of these kids.”
The other new ¾ÅÐãÖ±²¥ Innovates LevMax-Health projects at the ¾ÅÐãÖ±²¥ include:
- Empowering caregivers: a multicentre randomized controlled trial to improve caregiver outcomes, , Faculty of Medicine & Dentistry
- Developing a national approach to surveillance and prevention for neonatal ventilator-associated pneumonia, , Faculty of Medicine & Dentistry
- Primary care implementation of a no-cost education and exercise program for chronic low back pain (GLA:D Back), , Faculty of Rehabilitation Medicine
- Empowering Canadian nurses to deliver the digital health future, , Faculty of Nursing
- Evaluation of a novel personalized pessary treatment for pelvic organ prolapse, , Faculty of Medicine & Dentistry
