Automatic landmark identification in cone‐beam computed tomography

Abstract

ObjectiveTo present and validate an open‐source fully automated landmark placement (ALICBCT) tool for cone‐beam computed tomography scans.Materials and MethodsOne hundred and forty‐three large and medium field of view cone‐beam computed tomography (CBCT) were used to train and test a novel approach, called ALICBCT that reformulates landmark detection as a classification problem through a virtual agent placed inside volumetric images. The landmark agents were trained to navigate in a multi‐scale volumetric space to reach the estimated landmark position. The agent movements decision relies on a combination of DenseNet feature network and fully connected layers. For each CBCT, 32 ground truth landmark positions were identified by 2 clinician experts. After validation of the 32 landmarks, new models were trained to identify a total of 119 landmarks that are commonly used in clinical studies for the quantification of changes in bone morphology and tooth position.ResultsOur method achieved a high accuracy with an average of 1.54 ± 0.87 mm error for the 32 landmark positions with rare failures, taking an average of 4.2 second computation time to identify each landmark in one large 3D‐CBCT scan using a conventional GPU.ConclusionThe ALICBCT algorithm is a robust automatic identification tool that has been deployed for clinical and research use as an extension in the 3D Slicer platform allowing continuous updates for increased precision.

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.…”

“…This step assured an even more precise landmark placement, highlighting that continuous human interaction with clinician feedback is essential for improving the accuracy and precision of AI algorithms. 21,22 This study consists of a secondary data analysis. The CBCT scans were originally acquired with the goal of evaluating dehiscences and fenestrations in patients, without the need for an invasive procedure such as flap elevation for direct assessment.…”

“…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.…”