Automatic Three-Dimensional Cephalometric Annotation System Using Three-Dimensional Convolutional Neural Networks

Background: Three-dimensional (3D) cephalometric analysis using computerized tomography data has been rapidly adopted for dysmorphosis and anthropometry. Several different approaches to automatic 3D annotation have been proposed to overcome the limitations of traditional cephalometry. The purpose of this study was to evaluate the accuracy of our newly-developed system using a deep learning algorithm for automatic 3D cephalometric annotation. Methods: To overcome current technical limitations, some measures were developed to directly annotate 3D human skull data. Our deep learning-based model system mainly consisted of a 3D convolutional neural network and image data resampling. Results: The discrepancies between the referenced and predicted coordinate values in three axes and in 3D distance were calculated to evaluate system accuracy. Our new model system yielded prediction errors of 3.26, 3.18, and 4.81 mm (for three axes) and 7.61 mm (for 3D). Moreover, there was no difference among the landmarks of the three groups, including the midsagittal plane, horizontal plane, and mandible (p>0.05). Conclusion: A new 3D convolutional neural network-based automatic annotation system for 3D cephalometry was developed. The strategies used to implement the system were detailed and measurement results were evaluated for accuracy. Further development of this system is planned for full clinical application of automatic 3D cephalometric annotation

Accuracy of automated 3D cephalometric landmarks by deep learning algorithms: systematic review and meta-analysis

Objectives The aim of the present systematic review and meta-analysis is to assess the accuracy of automated landmarking using deep learning in comparison with manual tracing for cephalometric analysis of 3D medical images. Methods PubMed/Medline, IEEE Xplore, Scopus and ArXiv electronic databases were searched. Selection criteria were: ex vivo and in vivo volumetric data images suitable for 3D landmarking (Problem), a minimum of five automated landmarking performed by deep learning method (Intervention), manual landmarking (Comparison), and mean accuracy, in mm, between manual and automated landmarking (Outcome). QUADAS-2 was adapted for quality analysis. Meta-analysis was performed on studies that reported as outcome mean values and standard deviation of the difference (error) between manual and automated landmarking. Linear regression plots were used to analyze correlations between mean accuracy and year of publication. Results The initial electronic screening yielded 252 papers published between 2020 and 2022. A total of 15 studies were included for the qualitative synthesis, whereas 11 studies were used for the meta-analysis. Overall random effect model revealed a mean value of 2.44 mm, with a high heterogeneity (I-2 = 98.13%, tau(2) = 1.018, p-value < 0.001); risk of bias was high due to the presence of issues for several domains per study. Meta-regression indicated a significant relation between mean error and year of publication (p value = 0.012). Conclusion Deep learning algorithms showed an excellent accuracy for automated 3D cephalometric landmarking. In the last two years promising algorithms have been developed and improvements in landmarks annotation accuracy have been done

Fully automated landmarking and facial segmentation on 3D photographs

Three-dimensional facial stereophotogrammetry provides a detailed representation of craniofacial soft tissue without the use of ionizing radiation. While manual annotation of landmarks serves as the current gold standard for cephalometric analysis, it is a time-consuming process and is prone to human error. The aim in this study was to develop and evaluate an automated cephalometric annotation method using a deep learning-based approach. Ten landmarks were manually annotated on 2897 3D facial photographs by a single observer. The automated landmarking workflow involved two successive DiffusionNet models and additional algorithms for facial segmentation. The dataset was randomly divided into a training and test dataset. The training dataset was used to train the deep learning networks, whereas the test dataset was used to evaluate the performance of the automated workflow. The precision of the workflow was evaluated by calculating the Euclidean distances between the automated and manual landmarks and compared to the intra-observer and inter-observer variability of manual annotation and the semi-automated landmarking method. The workflow was successful in 98.6% of all test cases. The deep learning-based landmarking method achieved precise and consistent landmark annotation. The mean precision of 1.69 (+/-1.15) mm was comparable to the inter-observer variability (1.31 +/-0.91 mm) of manual annotation. The Euclidean distance between the automated and manual landmarks was within 2 mm in 69%. Automated landmark annotation on 3D photographs was achieved with the DiffusionNet-based approach. The proposed method allows quantitative analysis of large datasets and may be used in diagnosis, follow-up, and virtual surgical planning.Comment: 13 pages, 4 figures, 7 tables, repository https://github.com/rumc3dlab/3dlandmarkdetection

Artificial intelligence in dentistry, orthodontics and orthognathic surgery: A literature review

Artificial intelligence is the ability of machines to work like humans. The concept initially began with the advent of mathematical models which gave calculated outputs based on inputs fed into the system. This was later modified with the introduction of various algorithms which can either give output based on overall data analysis or by selection of information within previous data. It is steadily becoming a favoured mode of treatment due to its efficiency and ability to manage complex conditions in all specialities. In dentistry, artificial intelligence has also popularised over the past few decades. They have been found useful for diagnosis in restorative dentistry, oral pathology and oral surgery. In orthodontics, they have been utilised for diagnosis, assessment of treatment needs, cephalometrics, treatment planning and orthognathic surgeries etc. The current literature review was planned to highlight the uses of artificial intelligence in dentistry, specifically in orthodontics and orthognathic surgery

Three-Dimensional Cephalometric Landmarking and Frankfort Horizontal Plane Construction: Reproducibility of Conventional and Novel Landmarks

In some dentofacial deformity patients, especially patients undergoing surgical orthodontic treatments, Computed Tomography (CT) scans are useful to assess complex asymmetry or to plan orthognathic surgery. This assessment would be made easier for orthodontists and surgeons with a three-dimensional (3D) cephalometric analysis, which would require the localization of landmarks and the construction of reference planes. The objectives of this study were to assess manual landmarking repeatability and reproducibility (R&R) of a set of 3D landmarks and to evaluate R&R of vertical cephalometric measurements using two Frankfort Horizontal (FH) planes as references for horizontal 3D imaging reorientation. Thirty-three landmarks, divided into “conventional”, “foraminal” and “dental”, were manually located twice by three experienced operators on 20 randomly-selected CT scans of orthognathic surgery patients. R&R confidence intervals (CI) of each landmark in the -x, -y and -z directions were computed according to the ISO 5725 standard. These landmarks were then used to construct 2 FH planes: a conventional FH plane (orbitale left, porion right and left) and a newly proposed FH plane (midinternal acoustic foramen, orbitale right and left). R&R of vertical cephalometric measurements were computed using these 2 FH planes as horizontal references for CT reorientation. Landmarks showing a 95% CI of repeatability and/or reproducibility > 2 mm were found exclusively in the “conventional” landmarks group. Vertical measurements showed excellent R&R (95% CI < 1 mm) with either FH plane as horizontal reference. However, the 2 FH planes were not found to be parallel (absolute angular difference of 2.41°, SD 1.27°). Overall, “dental” and “foraminal” landmarks were more reliable than the “conventional” landmarks. Despite the poor reliability of the landmarks orbitale and porion, the construction of the conventional FH plane provided a reliable horizontal reference for 3D craniofacial CT scan reorientation

Two-Stream Regression Network for Dental Implant Position Prediction

In implant prosthesis treatment, the design of surgical guide requires lots of manual labors and is prone to subjective variations. When deep learning based methods has started to be applied to address this problem, the space between teeth are various and some of them might present similar texture characteristic with the actual implant region. Both problems make a big challenge for the implant position prediction. In this paper, we develop a two-stream implant position regression framework (TSIPR), which consists of an implant region detector (IRD) and a multi-scale patch embedding regression network (MSPENet), to address this issue. For the training of IRD, we extend the original annotation to provide additional supervisory information, which contains much more rich characteristic and do not introduce extra labeling costs. A multi-scale patch embedding module is designed for the MSPENet to adaptively extract features from the images with various tooth spacing. The global-local feature interaction block is designed to build the encoder of MSPENet, which combines the transformer and convolution for enriched feature representation. During inference, the RoI mask extracted from the IRD is used to refine the prediction results of the MSPENet. Extensive experiments on a dental implant dataset through five-fold cross-validation demonstrated that the proposed TSIPR achieves superior performance than existing methods

Cephalometric landmark detection: Artificial intelligence vs human examination

Magister Scientiae Dentium - MSc(Dent)Cephalometric landmark detection is important for accurate diagnosis and treatment planning. The most common cause of random errors, in both computer-aided cephalometry and manual cephalometric analysis, is inconsistency in landmark detection. These methods are time-consuming. As a result, attempts have been made to automate cephalometric analysis, to improve the accuracy and precision of landmark detection whilst also minimizing errors caused by clinician subjectivity.This mini-thesis aimed to determine the precision of two cephalometric landmark identification methods, namely an artificial intelligence programme (BoneFinder®) and a computer-assisted examination software (Dolphin ImagingTM)

Machine learning methods as an aid in planning orthodontic treatment on the example of Cone-Beam Computed Tomography analysis: a literature review

Convolutional neural networks (CNNs) are used in many areas of computer vision, such as object tracking and recognition, security, military, and biomedical image analysis. In this work, we describe the current methods, the architectures of deep convolutional neural networks used in CBCT. Literature from 2000-2020 from the PubMed database, Google Scholar, was analyzed. Account has been taken of publications in English that describe architectures of deep convolutional neural networks used in CBCT. The results of the reviewed studies indicate that deep learning methods employed in orthodontics can be far superior in comparison to other high-performing algorithms

3D cephalometric landmark detection by multiple stage deep reinforcement learning

The lengthy time needed for manual landmarking has delayed the widespread adoption of three-dimensional (3D) cephalometry. We here propose an automatic 3D cephalometric annotation system based on multi-stage deep reinforcement learning (DRL) and volume-rendered imaging. This system considers geometrical characteristics of landmarks and simulates the sequential decision process underlying human professional landmarking patterns. It consists mainly of constructing an appropriate two-dimensional cutaway or 3D model view, then implementing single-stage DRL with gradient-based boundary estimation or multi-stage DRL to dictate the 3D coordinates of target landmarks. This system clearly shows sufficient detection accuracy and stability for direct clinical applications, with a low level of detection error and low inter-individual variation (1.96 ± 0.78 mm). Our system, moreover, requires no additional steps of segmentation and 3D mesh-object construction for landmark detection. We believe these system features will enable fast-track cephalometric analysis and planning and expect it to achieve greater accuracy as larger CT datasets become available for training and testing.ope