A recently developed artificial intelligence (AI)-powered diagnostic system may improve the accuracy of identifying and classifying odontogenic sinusitis through dental x-rays, according to a news release dated March 31.
Additionally, this system may reduce patients' radiation exposure by decreasing the reliance on computed tomography (CT) scans for diagnosing odontogenic sinusitis, according to the study.
"The proposed system demonstrates strong potential for clinical applications, offering reliable support for early diagnosis and paving the way for broader intelligent dental diagnostic platforms," wrote the authors, led by Pei-Yi Wu of the Taoyuan Chang Gung Memorial Hospital in Taiwan (Bioengineering, January 30, 2025, Vol. 12:2, 134).
For this study, researchers trained deep-learning models using dental panoramic radiograph (DPR) images, enabling them to detect key anatomical features. YOLO (You Only Look Once) is an object-detection algorithm recognized for its speed and precision, according to the story.
The YOLO 11n variant is specially optimized for medical imaging, allowing it to identify teeth and sinus structures in a single scan with high accuracy. Unlike traditional approaches that require multiple steps and expert interpretation, YOLO 11n processes images in real time, quickly pinpointing affected areas.
Sample dental panoramic radiographs as seen by the YOLO 11n deep learning model, which can identify tooth structures with up to 98.2% accuracy. Image and caption courtesy of Pei-Yi Wu et al. Licensed under CC BY-NC-ND.
In addition to its diagnostic precision, this AI-based system offers several practical advantages. It reduces the need for CT scans, currently the preferred diagnostic method. It also serves as a cost-effective screening tool, especially in settings where access to advanced imaging is limited.
By identifying potential cases early, the system enables faster treatment and helps ease the workload on healthcare providers, according to the story.
"This study developed an AI-assisted diagnostic system that significantly improves the accuracy of detecting and classifying odontogenic sinusitis using dental panoramic radiographs," the authors wrote.
