Development of a deep learning model for automatic localization of radiographic markers of proposed dental implant site locations

Alsomali, Mona; Alghamdi, Shatha; Alotaibi, Shahad; Alfadda, Sara A.; Altwaijry, Najwa; Al-Turaiki, Isra; Al-Ekrish, Asma’a Abdurrahman

doi:10.1016/j.sdentj.2022.01.002

Cited by 8 publications

(2 citation statements)

References 13 publications

(14 reference statements)

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“…The model shows potential as a time-saving tool for automated dental implant planning. Additionally, Alsomali et al 29 developed a deep-learning model that could automatically identify the precise location of gutta-percha (GP) markers on cone beam computed tomography (CBCT) images. These markers are utilized to designate potential dental implant sites.…”

Section: Discussionmentioning

confidence: 99%

Deep learning-based approach for 3D bone segmentation and prediction of missing tooth region for dental implant planning

Al-Asali,

Alqutaibi,

Al-Sarem

et al. 2024

Sci Rep

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Recent studies have shown that dental implants have high long-term survival rates, indicating their effectiveness compared to other treatments. However, there is still a concern regarding treatment failure. Deep learning methods, specifically U-Net models, have been effectively applied to analyze medical and dental images. This study aims to utilize U-Net models to segment bone in regions where teeth are missing in cone-beam computerized tomography (CBCT) scans and predict the positions of implants. The proposed models were applied to a CBCT dataset of Taibah University Dental Hospital (TUDH) patients between 2018 and 2023. They were evaluated using different performance metrics and validated by a domain expert. The experimental results demonstrated outstanding performance in terms of dice, precision, and recall for bone segmentation (0.93, 0.94, and 0.93, respectively) with a low volume error (0.01). The proposed models offer promising automated dental implant planning for dental implantologists.

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Section: Discussionmentioning

confidence: 99%

Deep learning-based approach for 3D bone segmentation and prediction of missing tooth region for dental implant planning

Al-Asali,

Alqutaibi,

Al-Sarem

et al. 2024

Sci Rep

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“…There are other studies that used CBCT images in deep learning for dental applications. Alsomali et al [3] presented a model for automatic localization of the fiducial markers in CBCT images, but the model was not considered accurate enough to be used during implant planning. The authors suggested that the inaccuracy was due to the fact that the model was trained on axial sections only, which did not relate the fiducial markers to the underlying bone.…”

Section: Introductionmentioning

confidence: 99%

Automatic Placement of Simulated Dental Implants Within CBCT Images in Optimum Positions: A Deep Learning Model

Alotaibi

Alsomali

Alghamdi

et al. 2023

Preprint

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The increasing use of three-dimensional scans in dentistry motivates the use of artificial intelligence systems in several clinical scopes. One of these scopes is implant dentistry, which is a complex process with multiple criteria of success. The dentist analyzes cone-beam computed tomography (CBCT) images and formulates a detailed implant treatment plan by specifying the length, diameter, position, and angulation of implants while considering the prosthodontic treatment plan, bone morphology, and position of adjacent vital anatomical structures. A supervised deep learning model was built to determine the optimum implant position, angulation, diameter, and length as dictated by prosthetic and anatomical requirements. The main goal of building this model was to facilitate the planning procedure and reduce the planning time of implant treatment, especially in multiple implant cases. This study had two phases; the first was detecting and placing a bounding box around the fiducial markers and adjacent bone within the CBCT images by training YOLOv3 model. The second phase trained six deep regression models to extract the apical and occlusal coordinates (X, Y, Z) of the implants; each model predicted one target value. In addition, two deep classification models were developed for predicting the implants’ intra-osseous length and intra-osseous diameter. After testing these models, YOLOv3 in the first phase showed 46.78\% average precision; in the regression model, the mean absolute error ranged from 11.931 to 15.954, and the classification of the intra-osseous diameter showed 76\% accuracy, and the intra-osseous length showed 59\% accuracy. A subjective assessment by an expert prosthodontist found the results to be promising regarding selection of implant size, and mesiodistal placement of the implants. However, angulation and vertical and bucco-lingual position of the implants was unacceptable. Thus, further investigations and model training are required to improve the accuracy of the output.

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Synergy between artificial intelligence and precision medicine for computer-assisted oral and maxillofacial surgical planning

2022

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Development of a deep learning model for automatic localization of radiographic markers of proposed dental implant site locations

Cited by 8 publications

References 13 publications

Deep learning-based approach for 3D bone segmentation and prediction of missing tooth region for dental implant planning

Deep learning-based approach for 3D bone segmentation and prediction of missing tooth region for dental implant planning

Automatic Placement of Simulated Dental Implants Within CBCT Images in Optimum Positions: A Deep Learning Model

Synergy between artificial intelligence and precision medicine for computer-assisted oral and maxillofacial surgical planning

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