OBJECTIVES: To investigate the accuracy and reliability of deep learning in automatic graft material segmentation after maxillary sinus augmentation (SA) from cone-beam computed tomography (CBCT) images.
Clinical implant dentistry and related research
38151900
OBJECTIVES: This study aimed to use a deep learning (DL) approach for the automatic identification of the ridge deficiency around dental implants based on an image slice from cone-beam computerized tomography (CBCT).
Clinical implant dentistry and related research
39267298
OBJECTIVES: To reveal the force profiles recorded by haptic autonomous robotic force feedback during the transcrestal sinus floor elevation (TSFE) process, providing a reference for the surgery strategy during TSFE.
Clinical implant dentistry and related research
39686517
OBJECTIVES: Accurate assessment of postoperative bone graft material changes after the 1-stage sinus lift is crucial for evaluating long-term implant survival. However, traditional manual labeling and segmentation of cone-beam computed tomography (CB...