FlyBy CNN: a 3D surface segmentation framework

Automated artificial intelligence‐based three‐dimensional comparison of orthodontic treatment outcomes with and without piezocision surgery

“…In this study, we evaluated the tooth movement of all lower teeth after correcting mild to moderate malocclusion, while the literature mainly reports different evaluations and methodologies, such as the levelling of only mandibular anterior teeth, 27 assessment of severe maxillary malocclusion, 6 evaluation of maxillary canine distalization, 14 en-masse retraction utilizing miniscrews, 35 transversal tooth movement of mandibular lateral segments, 15 second molar protraction and upper canine retraction. 36 The automated AI-based dental tools used in this study are accurate 8,[19][20][21][22][23][24]26,31,32 and facilitate the assessment and quantification of tooth movement, reducing the time needed by clinicians and researchers to analyse imaging processes and evaluations by at least 90%. It is important to note that while commercial companies such as Relu, 37 Diagnocat 38 and Materialise, 39 as well as previous studies, have demonstrated similar applications, most of their tools are not integrated into the same platform and are not easily accessible due to cost and code unavailability.…”

“…The automated AI‐based dental tools used in this study are accurate 8,19–24,26,31,32 and facilitate the assessment and quantification of tooth movement, reducing the time needed by clinicians and researchers to analyse imaging processes and evaluations by at least 90%. It is important to note that while commercial companies such as Relu, 37 Diagnocat 38 and Materialise, 39 as well as previous studies, have demonstrated similar applications, most of their tools are not integrated into the same platform and are not easily accessible due to cost and code unavailability.…”

“…The image pre‐processing included T1 CBCT orientation, 15 T2 registration, 29 as well as T1 and T2 IOS registration to the CBCT scans 30 with validated semi‐automated tools (Figure 1) and completely automated tools (Figure 2). 8,19–24,26,31,32 …”

“…The image pre-processing included T1 CBCT orientation, 15 T2 registration, 29 as well as T1 and T2 IOS registration to the CBCT scans 30 with validated semi-automated tools (Figure 1) and completely automated tools (Figure 2). 8,[19][20][21][22][23][24]26,31,32 The T1 CBCTs orientation was performed positioning the mandibular 3D model as follows: The lower border of the mandible, mesial surface of first molars and midline were aligned, respectively, with the axial, coronal and sagittal axis. 15 Subsequently, the T2 CBCT images were manually approximated to the T1 images and a voxel-based registration was performed.…”

“…The integration of these image modalities may optimize its diagnostic value, granting researchers and clinicians a comprehensive and enhanced analysis of the roots, root canals and crowns, 12,[17][18][19][20] also aiding in critical tooth position assessments. Additionally, the advent of innovative artificial intelligence (AI)-based tools [19][20][21][22][23][24][25][26] have facilitated merging root canals and crown segmentations, as well as automatic placement of landmarks and quantification of tooth movement.…”