Convolutional neural network for automated tooth segmentation on intraoral scans.

BACKGROUND: Tooth segmentation on intraoral scanned (IOS) data is a prerequisite for clinical applications in digital workflows. Current state-of-the-art methods lack the robustness to handle variability in dental conditions. This study aims to propose and evaluate the performance of a convolutional neural network (CNN) model for automatic tooth segmentation on IOS images. METHODS: A dataset of 761 IOS images (380 upper jaws, 381 lower jaws) was acquired using an intraoral scanner. The inclusion criteria included a full set of permanent teeth, teeth with orthodontic brackets, and partially edentulous dentition. A multi-step 3D U-Net pipeline was designed for automated tooth segmentation on IOS images. The model's performance was assessed in terms of time and accuracy. Additionally, the model was deployed on an online cloud-based platform, where a separate subsample of 18 IOS images was used to test the clinical applicability of the model by comparing three modes of segmentation: automated artificial intelligence-driven (A-AI), refined (R-AI), and semi-automatic (SA) segmentation. RESULTS: The average time for automated segmentation was 31.7 ± 8.1 s per jaw. The CNN model achieved an Intersection over Union (IoU) score of 91%, with the full set of teeth achieving the highest performance and the partially edentulous group scoring the lowest. In terms of clinical applicability, SA took an average of 860.4 s per case, whereas R-AI showed a 2.6-fold decrease in time (328.5 s). Furthermore, R-AI offered higher performance and reliability compared to SA, regardless of the dentition group. CONCLUSIONS: The 3D U-Net pipeline was accurate, efficient, and consistent for automatic tooth segmentation on IOS images. The online cloud-based platform could serve as a viable alternative for IOS segmentation.

Recommendations to minimize tooth root remodeling in patients undergoing maxillary osteotomies.

The purpose of this study was to report root remodeling/resorption percentages of maxillary teeth following the different maxillary osteotomies; i.e. one-piece, two-pieces, three-pieces Le Fort I, surgically assisted rapid palatal expansion (SARPE). The possibility of relationships between root remodeling and various patient- and/or treatment-related factors were further investigated. A total of 110 patients (1075 teeth) who underwent combined orthodontic and orthognathic surgery were studied retrospectively. The sample size was divided into: 30 patients in one-piece Le Fort I group, 30 patients in multi-pieces Le Fort I group, 20 patients in SARPE group and 30 patients in orthodontic group. Preoperative and 1 year postoperative cone beam computed tomography (CBCT) scans were obtained. A validated and automated method for evaluating root remodeling and resorption in three dimensions (3D) was applied. SARPE group showed the highest percentage of root remodeling. Spearman correlation coefficient revealed a positive relationship between maxillary advancement and root remodeling, with more advancement contributing to more root remodeling. On the other hand, the orthodontic group showed a negative correlation with age indicating increased root remodeling in younger patients. Based on the reported results of linear, volumetric and morphological changes of the root after 1 year, clinical recommendations were provided in the form of decision tree flowchart and tables. These recommendations can serve as a valuable resource for surgeons in estimating and managing root remodeling and resorption associated with different maxillary surgical techniques.

Three dimensional assessment of root changes after multi-segments Le Fort I osteotomy.

The primary purpose of this study was to accurately assess linear, volumetric and morphological changes of maxillary teeth roots following multi-segments Le Fort I osteotomy. A secondary objective was to assess whether patient- and/or treatment-related factors might influence root remodeling. A total of 60 patients (590 teeth) who underwent combined orthodontic and orthognathic surgery were studied retrospectively. The multi-segments group included 30 patients who had either 2-segments or 3-segments Le Fort I osteotomy. The other 30 patients underwent one-segment Le Fort I osteotomy. Preoperative, 1 year, and 2 years postoperative cone beam computed tomography (CBCT) scans were obtained. A validated and fully automated method for evaluating root changes in three dimensions (3D) was applied. No statistical significant differences were found between multi-segments and one-segment Le Fort I for linear, volumetric and morphological measurements. The Spearman correlation coefficient revealed a positive relationship between maxillary advancement and root remodeling, with more advancement leading to more root remodeling. This research may allow surgeons to properly assess root remodeling after combined treatment of orthodontics and the different Le Fort I osteotomies.

Automated three-dimensional quantification of external root resorption following combined orthodontic-orthognathic surgical treatment. A validation study.

OBJECTIVE: Three-dimensional (3D) quantitative assessment of external root resorption (ERR) following combined orthodontic-orthognathic surgical treatment is vital for ensuring an optimal long-term tooth prognosis. In this era, lack of evidence exists applying automated 3D approaches for assessing ERR. Therefore, this study aimed to validate a protocol for 3D quantification of ERR on cone-beam computed tomography (CBCT) images following combined orthodontic-orthognathic surgical treatment. MATERIAL AND METHODS: Twenty patients who underwent combined orthodontic-orthognathic surgical treatment were recruited. Each patient had CBCT scans acquired with NewTom VGi evo (NewTom) at three time-points i.e., 4-weeks prior to surgery (T0), 1-week (T1) and 1-year after surgery (T2). Patients were divided into two groups, group A (surgical Le Fort I osteotomy group: 10 patients) and group B (orthodontic group without maxillary surgical intervention: 10 patients). Root resorption was assessed by measuring length and volumetric changes of maxillary premolar to premolar teeth (central and lateral incisors, canines, 1st and 2nd premolars= 10 teeth) at T0-T1 and T0-T2 time intervals in both groups. The protocol consisted of convolutional neural network based segmentation followed by surface-based superimposition and automated 3D analysis. RESULTS: The intra-observer intra-class correlation coefficient (ICC) was found to be excellent (1.0) with an average error of 0 mm and 0 mm3 for assessing root length and volume, respectively. The entire protocol took 56.8 ± 7 s for quantifying ERR. Both group of patients showed negligible changes in length and volumetric ratio at T0-T1 time-interval. Furthermore, group A had lower ERR ratio with decreased root volume and length compared to group B at T0-T2 time-interval. CONCLUSIONS: The proposed protocol was found to be time efficient, accurate and reliable for 3D quantification of ERR on CBCT images. It could act as a viable automated option for assessing ERR. CLINICAL SIGNIFICANCE: The automated protocol could provide a time efficient method to allow a reliable and accurate 3D follow up root resorption after orthognathic and orthodontic treatment procedures. These new insights could allow clinicians to implement strategies for minimizing the risk of root resorption and to further enhance treatment predictability.

Impact of orthognathic surgery on root resorption: A systematic review.

OBJECTIVE: This systematic review was performed to assess the potential influence of orthognathic surgery on root resorption (RR). MATERIAL AND METHODS: An electronic search was conducted using PubMed, Web of Science, Cochrane Central and Embase for articles published up to April 2022. Following inclusion and exclusion criteria, a total of six articles were selected that reported on RR following orthognathic surgery. Risk of bias assessment was performed according to the ROBINS-1 and ROB-2 tools. RESULTS: The design of five studies was retrospective and one randomized clinical trial was included, with a follow-up period ranging between six months and ten years. The assessment methodologies mostly relied on two-dimensional imaging modalities where only one study used cone-beam computed tomography (CBCT) for objective quantification via linear measurements. The percentage of teeth affected by RR varied between approximately 1 and 36%, where surgically assisted rapid maxillary expansion (SARME) and Le Fort I osteotomy showed the highest percentage of RR followed by bilateral sagittal split osteotomy. CONCLUSIONS: The present data tend to indicate that specific orthognathic procedures such as SARME and Le Fort I osteotomy may induce or reinforce RR. Yet, considering lack of evidence related to objective quantification of RR following orthodontic and/or orthognathic treatment, further CBCT-based prospective studies are required for an improved understanding of RR following different surgical procedures.

A novel deep learning system for multi-class tooth segmentation and classification on cone beam computed tomography. A validation study.

OBJECTIVES: Automatic tooth segmentation and classification from cone beam computed tomography (CBCT) have become an integral component of the digital dental workflows. Therefore, the aim of this study was to develop and validate a deep learning approach for an automatic tooth segmentation and classification from CBCT images. METHODS: A dataset of 186 CBCT scans was acquired from two CBCT machines with different acquisition settings. An artificial intelligence (AI) framework was built to segment and classify teeth. Teeth were segmented in a three-step approach with each step consisting of a 3D U-Net and step 2 included classification. The dataset was divided into training set (140 scans) to train the model based on ground-truth segmented teeth, validation set (35 scans) to test the model performance and test set (11 scans) to evaluate the model performance compared to ground-truth. Different evaluation metrics were used such as precision, recall rate and time. RESULTS: The AI framework correctly segmented teeth with optimal precision (0.98±0.02) and recall (0.83±0.05). The difference between the AI model and ground-truth was 0.56±0.38 mm based on 95% Hausdorff distance confirming the high performance of AI compared to ground-truth. Furthermore, segmentation of all the teeth within a scan was more than 1800 times faster for AI compared to that of an expert. Teeth classification also performed optimally with a recall rate of 98.5% and precision of 97.9%. CONCLUSIONS: The proposed 3D U-Net based AI framework is an accurate and time-efficient deep learning system for automatic tooth segmentation and classification without expert refinement. CLINICAL SIGNIFICANCE: The proposed system might enable potential future applications for diagnostics and treatment planning in the field of digital dentistry, while reducing clinical workload.