Diagnostic Value of 3D Imaging in Clinical Orthodontics

Palomo, Juan Martín; El, Hakan; Stefanović, Neda; Bazina, Mohamed

doi:10.1007/978-3-030-00722-5_7

Cited by 3 publications

(1 citation statement)

References 152 publications

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“…This type of combined segmentation is not possible with the available semi-automatic segmentation software programs, where each structure has a different threshold requiring manual adjustment separately by the operator [ 17 ]. Clinically, this integrated segmentation could be a valuable tool in clinical orthodontics and maxillofacial surgical procedures, such as implant planning, bone grafting, and orthognathic and reconstructive surgery [ 18 – 21 ], where visualization of MVP and its relationship with surrounding structures is a necessity for reaching an accurate diagnosis and patient-specific treatment planning.…”

Section: Discussionmentioning

confidence: 99%

Three-dimensional maxillary virtual patient creation by convolutional neural network-based segmentation on cone-beam computed tomography images

Reis

Morgan

Nomidis³

et al. 2022

Clin Oral Invest

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Objective To qualitatively and quantitatively assess integrated segmentation of three convolutional neural network (CNN) models for the creation of a maxillary virtual patient (MVP) from cone-beam computed tomography (CBCT) images. Materials and methods A dataset of 40 CBCT scans acquired with different scanning parameters was selected. Three previously validated individual CNN models were integrated to achieve a combined segmentation of maxillary complex, maxillary sinuses, and upper dentition. Two experts performed a qualitative assessment, scoring-integrated segmentations from 0 to 10 based on the number of required refinements. Furthermore, experts executed refinements, allowing performance comparison between integrated automated segmentation (AS) and refined segmentation (RS) models. Inter-observer consistency of the refinements and the time needed to create a full-resolution automatic segmentation were calculated. Results From the dataset, 85% scored 7–10, and 15% were within 3–6. The average time required for automated segmentation was 1.7 min. Performance metrics indicated an excellent overlap between automatic and refined segmentation with a dice similarity coefficient (DSC) of 99.3%. High inter-observer consistency of refinements was observed, with a 95% Hausdorff distance (HD) of 0.045 mm. Conclusion The integrated CNN models proved to be fast, accurate, and consistent along with a strong interobserver consistency in creating the MVP. Clinical relevance The automated segmentation of these structures simultaneously could act as a valuable tool in clinical orthodontics, implant rehabilitation, and any oral or maxillofacial surgical procedures, where visualization of MVP and its relationship with surrounding structures is a necessity for reaching an accurate diagnosis and patient-specific treatment planning.

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Section: Discussionmentioning

confidence: 99%

Three-dimensional maxillary virtual patient creation by convolutional neural network-based segmentation on cone-beam computed tomography images

Reis

Morgan

Nomidis³

et al. 2022

Clin Oral Invest

View full text Add to dashboard Cite

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Evolution of the Orthodontic Diagnosis in the Age of Artificial Intelligence

Retrouvey

Panayi

Tsolakis

2021

3D Diagnosis and Treatment Planning in Orthodontics

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Deep learning driven segmentation of maxillary impacted canine on cone beam computed tomography images

Swaity,

Elgarba,

Morgan

et al. 2024

Sci Rep

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The process of creating virtual models of dentomaxillofacial structures through three-dimensional segmentation is a crucial component of most digital dental workflows. This process is typically performed using manual or semi-automated approaches, which can be time-consuming and subject to observer bias. The aim of this study was to train and assess the performance of a convolutional neural network (CNN)-based online cloud platform for automated segmentation of maxillary impacted canine on CBCT image. A total of 100 CBCT images with maxillary canine impactions were randomly allocated into two groups: a training set (n = 50) and a testing set (n = 50). The training set was used to train the CNN model and the testing set was employed to evaluate the model performance. Both tasks were performed on an online cloud-based platform, ‘Virtual patient creator’ (Relu, Leuven, Belgium). The performance was assessed using voxel- and surface-based comparison between automated and semi-automated ground truth segmentations. In addition, the time required for segmentation was also calculated. The automated tool showed high performance for segmenting impacted canines with a dice similarity coefficient of 0.99 ± 0.02. Moreover, it was 24 times faster than semi-automated approach. The proposed CNN model achieved fast, consistent, and precise segmentation of maxillary impacted canines.

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Diagnostic Value of 3D Imaging in Clinical Orthodontics

Cited by 3 publications

References 152 publications

Three-dimensional maxillary virtual patient creation by convolutional neural network-based segmentation on cone-beam computed tomography images

Three-dimensional maxillary virtual patient creation by convolutional neural network-based segmentation on cone-beam computed tomography images

Evolution of the Orthodontic Diagnosis in the Age of Artificial Intelligence

Deep learning driven segmentation of maxillary impacted canine on cone beam computed tomography images

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