Novel AI-based tool for primary tooth segmentation on CBCT using convolutional neural networks: A validation study.

BACKGROUND: Primary teeth segmentation on cone beam computed tomography (CBCT) scans is essential for paediatric treatment planning. Conventional methods, however, are time-consuming and necessitate advanced expertise. AIM: The aim of this study was to validate an artificial intelligence (AI) cloud-based platform for automated segmentation (AS) of primary teeth on CBCT. Its accuracy, time efficiency, and consistency were compared with manual segmentation (MS). DESIGN: A dataset comprising 402 primary teeth (37 CBCT scans) was retrospectively retrieved from two CBCT devices. Primary teeth were manually segmented using a cloud-based platform representing the ground truth, whereas AS was performed on the same platform. To assess the AI tool's performance, voxel- and surface-based metrics were employed to compare MS and AS methods. Additionally, segmentation time was recorded for each method, and intra-class correlation coefficient (ICC) assessed consistency between them. RESULTS: AS revealed high performance in segmenting primary teeth with high accuracy (98 ± 1%) and dice similarity coefficient (DSC; 95 ± 2%). Moreover, it was 35 times faster than the manual approach with an average time of 24 s. Both MS and AS demonstrated excellent consistency (ICC = 0.99 and 1, respectively). CONCLUSION: The platform demonstrated expert-level accuracy, and time-efficient and consistent segmentation of primary teeth on CBCT scans, serving treatment planning in children.

Artificial intelligence serving pre-surgical digital implant planning: A scoping review.

OBJECTIVES: To conduct a scoping review focusing on artificial intelligence (AI) applications in presurgical dental implant planning. Additionally, to assess the automation degree of clinically available pre-surgical implant planning software. DATA AND SOURCES: A systematic electronic literature search was performed in five databases (PubMed, Embase, Web of Science, Cochrane Library, and Scopus), along with exploring gray literature web-based resources until November 2023. English-language studies on AI-driven tools for digital implant planning were included based on an independent evaluation by two reviewers. An assessment of automation steps in dental implant planning software available on the market up to November 2023 was also performed. STUDY SELECTION AND RESULTS: From an initial 1,732 studies, 47 met eligibility criteria. Within this subset, 39 studies focused on AI networks for anatomical landmark-based segmentation, creating virtual patients. Eight studies were dedicated to AI networks for virtual implant placement. Additionally, a total of 12 commonly available implant planning software applications were identified and assessed for their level of automation in pre-surgical digital implant workflows. Notably, only six of these featured at least one fully automated step in the planning software, with none possessing a fully automated implant planning protocol. CONCLUSIONS: AI plays a crucial role in achieving accurate, time-efficient, and consistent segmentation of anatomical landmarks, serving the process of virtual patient creation. Additionally, currently available systems for virtual implant placement demonstrate different degrees of automation. It is important to highlight that, as of now, full automation of this process has not been documented nor scientifically validated. CLINICAL SIGNIFICANCE: Scientific and clinical validation of AI applications for presurgical dental implant planning is currently scarce. The present review allows the clinician to identify AI-based automation in presurgical dental implant planning and assess the potential underlying scientific validation.

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

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.

Deep learning-based segmentation of dental implants on cone-beam computed tomography images: A validation study.

OBJECTIVES: To train and validate a cloud-based convolutional neural network (CNN) model for automated segmentation (AS) of dental implant and attached prosthetic crown on cone-beam computed tomography (CBCT) images. METHODS: A total dataset of 280 maxillomandibular jawbone CBCT scans was acquired from patients who underwent implant placement with or without coronal restoration. The dataset was randomly divided into three subsets: training set (n = 225), validation set (n = 25) and testing set (n = 30). A CNN model was developed and trained using expert-based semi-automated segmentation (SS) of the implant and attached prosthetic crown as the ground truth. The performance of AS was assessed by comparing with SS and manually corrected automated segmentation referred to as refined-automated segmentation (R-AS). Evaluation metrics included timing, voxel-wise comparison based on confusion matrix and 3D surface differences. RESULTS: The average time required for AS was 60 times faster (<30 s) than the SS approach. The CNN model was highly effective in segmenting dental implants both with and without coronal restoration, achieving a high dice similarity coefficient score of 0.92±0.02 and 0.91±0.03, respectively. Moreover, the root mean square deviation values were also found to be low (implant only: 0.08±0.09 mm, implant+restoration: 0.11±0.07 mm) when compared with R-AS, implying high AI segmentation accuracy. CONCLUSIONS: The proposed cloud-based deep learning tool demonstrated high performance and time-efficient segmentation of implants on CBCT images. CLINICAL SIGNIFICANCE: AI-based segmentation of implants and prosthetic crowns can minimize the negative impact of artifacts and enhance the generalizability of creating dental virtual models. Furthermore, incorporating the suggested tool into existing CNN models specialized for segmenting anatomical structures can improve pre-surgical planning for implants and post-operative assessment of peri­implant bone levels.

Evaluation of Retention and Attachment Wear of CAD/CAM Versus Conventional Implant-Assisted Overdenture Frameworks.

Previous studies have demonstrated problems with implant-supported overdenture attachments, such as denture base fracture and retention loss of the attachment's nylon insert. In this study, three implants were surgically inserted at the anterior mandibular region of 16 completely edentulous men (mean age: 50 years), divided into two groups depending on the received mandibular complete overdenture: a conventional metal-reinforced framework with prefabricated metal housing (Group I) or a CAD/CAM metal-reinforced framework with custom metal housing (Group II). At 3 months (prostheses loading), 6 months, and 12 months after implant placement, the retention of the mandibular dentures and wear of O-ring attachments were evaluated. Data were collected, tabulated, and statistically analyzed using Student t test. Statistically significant differences were found between the two groups and within the same groups during the evaluation period (P < .05). Attachment housing incorporated within a CAD/CAM implant overdenture can be a better alternative to the manufacturer's metal housing, as it diminishes retention loss and attachment wear over time, thus increasing patient satisfaction and chewing efficiency.

Periodontal diseases and potential risk factors in Egyptian adult population-Results from a national cross-sectional study.

BACKGROUND: Despite the interdependence of general and periodontal health, there is paucity of national representative data on the prevalence of periodontal diseases and their associated risk factors in Egyptian population. This cross-sectional study, thus, aimed to assess the prevalence of periodontitis and tooth loss among Egyptian adults and investigate the association between potential risk factors and periodontal diseases. METHODS: A total of 5,954 adults aged ≥ 20 years were included in this study as a subsample from Egypt's national oral health survey. Periodontitis was diagnosed with Community Periodontal Index 'CPI' scores ≥3 and tooth loss not due to caries was included in the analysis. Socio-demographic data and information on behavioral factors and history of diabetes were gathered in a face-to-face interview. Logistic regression was done to interpret the impact of potential predictors on the incidence of the two selected outcome variables. RESULTS: The overall prevalence of periodontitis was 26% and regression analysis revealed that higher odds of periodontitis existed among illiterate participants (OR = 1.74; 95% CI: 1.40-2.17), smokers (OR = 1.93; 95% CI: 1.69-2.20) and rural residents (OR = 1.16; 95% CI: 1.03-1.30). On the other hand, old age, frequency of dental attendance and history of diabetes were the main predictive factors for tooth loss. CONCLUSIONS: Among Egyptian adults, periodontal diseases were strongly associated with a multitude of modifiable and non-modifiable risk factors and inequalities in distribution of periodontal treatment needs were determined mainly by age, gender, level of education and residency location.