The Ateneo Laboratory for Intelligent Visual Environments (ALIVE) collaborated with international researchers to create a groundbreaking deep learning model. This model significantly improves the accuracy of diagnosing dental issues. It identifies tooth and sinus structures in X-rays with 98.2% accuracy.
This technology is a major advancement in dental diagnostics. Odontogenic sinusitis is notoriously difficult to diagnose. Symptoms mimic common sinusitis. This often leads to delayed or incorrect treatment. The AI model helps address this challenge.
The system uses the YOLO 11n deep learning model. This quickly and accurately detects key anatomical relationships. It determines the proximity of tooth roots to sinuses. This is crucial in diagnosing odontogenic sinusitis. The YOLO 11n model is faster and more accurate than traditional methods.
The AI model offers several advantages. It reduces the need for radiation-heavy CT scans. This minimizes patient exposure. The system also provides cost-effective screening. This is especially beneficial in areas with limited resources. Early detection enables prompt treatment and prevents complications.
This development highlights AI’s expanding role in medicine. It addresses limitations in human expertise. With further validation, this technology could become a standard tool. It will improve diagnostic accuracy in dental and ENT clinics worldwide. The research, led by ALIVE head Dr. Patricia Angela R. Abu, was published in Bioengineering. This breakthrough will likely improve patient care globally.
