Скачать или смотреть Impacted Teeth Detection Using Pixel-Based Segmentation | AI Models Comparison on Panoramic X-rays

🦷 Comparative Analysis of Pixel-Based Segmentation Models for Impacted Teeth Detection on OPGs

In this video, we present a comparative study of deep learning segmentation models designed to detect impacted teeth accurately on panoramic dental radiographs (OPGs). Using pixel-based image segmentation techniques, this research highlights the strengths and weaknesses of different AI models in dental diagnostics.

🔍 What’s Covered:

U-Net, DeepLab, and other pixel-wise segmentation models

Dataset preparation and annotation for dental radiographs

Model performance metrics: accuracy, IoU, Dice coefficient

Clinical implications for automated dental diagnostics

Future potential in dental imaging and radiology AI

This is a valuable resource for AI researchers, dental professionals, radiologists, and students exploring the intersection of machine learning and medical imaging.

👍 Like, 🔔 Subscribe, and Share to support the future of AI in healthcare and dentistry!

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