SCIENTISTS in Taiwan and the Ateneo de Manila University have developed an Artificial Intelligence tool that detects with high accuracy a sinus infection that may affect the brain.
They say it will revolutionize dentistry. It detects with high accuracy a sinus infection that may affect the brain.
Along with a scientist at the Ateneo Laboratory for Intelligent Visual Environments (ALIVE) they have come up with a deep learning assistant able to identify tooth and sinus structures in dental X-rays with an accuracy of 98.2 percent.
Dr. Patricia Angela R. Abu, head of ALIVE, and colleagues at Taiwan’s Chang Gung Memorial Hospital, National Cheng Kung University, Chung Yuan Christian University, and Ming Chi University of Technology published their findings in the scientific journal Bioengineering.
In AI, deep learning is a type of machine learning that simulates the behavior of the human brain. It recognizes complex pictures, text, sounds, and other data to produce accurate information.
Using a sophisticated object detection algorithm, the AI tool is specifically trained to help diagnose odontogenic sinusitis quickly and more accurately.
Odontogenic sinusitis is a condition that is often misdiagnosed as general sinusitis and, if left unchecked, could spread infection to the face, eyes, and even the brain.
Odontogenic sinusitis, caused by infections or complications related to the upper teeth, is notoriously difficult to diagnose. Its symptoms – nasal congestion, foul-smelling nasal discharge, and occasional tooth pain – are nearly identical to those of general sinusitis.
To make matters worse, only about a third of patients experience noticeable dental pain, meaning the condition is frequently overlooked by general practitioners.
Traditional diagnosis requires collaboration between dentists and otolaryngologists, or ENT doctors specialize in diagnosing and treating conditions related to the ears, nose, and throat. While this collaboration is not to be blamed, it often leads to delayed treatment.
By training deep learning models on dental panoramic radiograph images, the researchers found a way to detect key anatomical relationships – such as the proximity of tooth roots to sinuses – with unprecedented accuracy, says Timothy James M. Dimacali of Ateneo.
They used the YOLO 11n deep learning model, achieving an impressive 98.2 percent accuracy that outperforms traditional detection methods.
YOLO (short for You Only Look Once) is a state-of-the-art object detection algorithm known for its speed and accuracy. The YOLO 11n model, an improved version, is optimized for medical imaging tasks, enabling it to identify teeth and sinus structures with high precision in a single pass through the image.
Unlike conventional diagnostic methods, which require multiple steps and expert interpretation, YOLO 11n rapidly pinpoints the affected areas in real time, making it an invaluable tool for dental professionals.
Beyond accuracy, the AI-driven approach also offers practical benefits. It minimizes patient exposure to radiation by reducing the need for CT scans, which are currently the gold standard for diagnosing odontogenic sinusitis.
The YOLO “dental assistant” also provides a cost-effective screening tool, particularly useful in resource-limited areas where advanced imaging technology may not be available. And by flagging potential cases early, the system allows for prompt intervention, preventing complications and reducing the burden on healthcare providers.
This breakthrough highlights AI’s growing role in medical diagnostics, bridging gaps where human expertise alone may fall short, the Ateneo University said in a statement. “With further validation, this technology could become a standard tool in dental and ENT clinics, ensuring that more patients receive timely and accurate diagnoses.”
Dr. Patricia Angela R. Abu, head of ALIVE, and colleagues at Taiwan’s Chang Gung Memorial Hospital, National Cheng Kung University, Chung Yuan Christian University, and Ming Chi University of Technology published their findings in the scientific journal Bioengineering.
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Dr. Joseph D. Lim, Ed. D., is the former Associate Dean of the College of Dentistry, University of the East; former Dean, College of Dentistry, National University; Past President and Honorary Fellow of the Asian Oral Implant Academy; Honorary Fellow of the Japan College of Oral Implantologists; Honorary Life Member of the Thai Association of Dental Implantology; and Founding Chairman of the Philippine College of Oral Implantologists. For questions on dental health, e-mail jdlim2008@gmail.com or text 0917-8591515.
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Dr. Kenneth Lester Lim, BS-MMG, DDM, MSc-OI, graduated Doctor of Dental Medicine, University of the Philippines, College of Dentistry, Manila, 2011; Bachelor of Science in Marketing Management, De la Salle University, Manila, 2002; and Master of Science (MSc.) in Oral Implantology, Goethe University, Frankfurt, Germany, 2019. He is an Associate Professor; Fellow, International Congress of Oral Implantologists; and Fellow, Philippine College of Oral Implantologists. For questions on dental health, email limdentalcenter@gmail.com/PN
