The Ateneo Laboratory for Intelligent Visual Environments (ALIVE) and international researchers have developed a deep learning model that aims to revolutionize dentistry, with the capability to identify tooth and sinus structures in dental X-rays with an accuracy of 98.2%.
Sample dental X-rays or dental panoramic radiographs (DPRs) as seen by the YOLO 11n deep learning model, which is able to identify tooth structures with up to 98.2% accuracy. CREDIT: Pei-Yi Wu et al., 2025
Notoriously difficult to diagnose
Using a sophisticated object detection algorithm, the system was specifically trained to help quickly and more accurately detect odontogenic sinusitis—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 ordinary 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, often leading to delayed treatment.
