Evaluation of facial alveolar bone thickness and fenestration of the maxillary premolars

Nalbantoğlu, Ahmet Mert; Yanık, Deniz

doi:10.1016/j.archoralbio.2022.105522

Cited by 1 publication

(1 citation statement)

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“…24,28 For the posterior maxilla, a ridge height of 10 mm was chosen to differentiate the need for sinus augmentation because a ridge height of <10 mm was reported as insufficient to provide adequate implant stabilization. 11 Because the implant survival rate reduced significantly with transcrestal sinus augmentation when the ridge height was <5 mm, 29 a ridge height of 5 mm was set as the cut-off value for classes B and C. Furthermore, due to a high incidence of fenestration defects on the posterior maxillary region, [30][31][32] a subclass f of the maxilla was used to specify the fenestration defect.…”

Section: Model Accuracy Based On the Number Of Training Datasetsmentioning

confidence: 99%

Deep learning for the identification of ridge deficiency around dental implants

Lin,

Wang,

et al. 2023

Clin Implant Dent Rel Res

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ObjectivesThis 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).Materials and methodsSingle slices crossing the central long‐axis of 630 mandibular and 845 maxillary virtually placed implants (4–5 mm diameter, 10 mm length) in 412 patients were used. The ridges were classified based on the intraoral bone‐implant support and sinus floor location. The slices were either preprocessed by alveolar ridge homogenizing prior to DL (preprocessed) or left unpreprocessed. A convolutional neural network with ResNet‐50 architecture was employed for DL.ResultsThe model achieved an accuracy of >98.5% on the unpreprocessed image slices and was found to be superior to the accuracy observed on the preprocessed slices. On the mandible, model accuracy was 98.91 ± 1.45%, and F1 score, a measure of a model's accuracy in binary classification tasks, was lowest (97.30%) on the ridge with a combined horizontal‐vertical defect. On the maxilla, model accuracy was 98.82 ± 1.11%, and the ridge presenting an implant collar‐sinus floor distance of 5–10 mm with a dehiscence defect had the lowest F1 score (95.86%). To achieve >90% model accuracy, ≥441 mandibular slices or ≥592 maxillary slices were required.ConclusionsThe ridge deficiency around dental implants can be identified using DL from CBCT image slices without the need for preprocessed homogenization. The model will be further strengthened by implementing more clinical expertise in dental implant treatment planning and incorporating multiple slices to classify 3‐dimensional implant‐ridge relationships.

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