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What is AI-Driven Graft Segmentation in Sinus Lift about?

This study investigates the use of artificial intelligence, specifically the Swin-UPerNet model, for automated segmentation of graft material in 1-stage sinus lift procedures using cone-beam computed tomography (CBCT) images. Results show that Swin-UPerNet significantly outperformed traditional models in accuracy and efficiency, reducing segmentation time from an average of 1390 seconds to just 19.28 seconds. The findings suggest that this AI-driven approach has substantial potential for improving the asses

Author

antian xu

Language

EN