Automatic landmarking of cephalograms using active appearance models

Applications of artificial intelligence in dentomaxillofacial imaging: a systematic review

BACKGROUND

Artificial intelligence (AI) technology has been increasingly developed in oral and maxillofacial imaging. The aim of this systematic review was to assess the applications and performance of the developed algorithms in different dentomaxillofacial imaging modalities.

STUDY DESIGN

A systematic search of PubMed and Scopus databases was performed. The search strategy was set as a combination of the following keywords: "Artificial Intelligence," "Machine Learning," "Deep Learning," "Neural Networks," "Head and Neck Imaging," and "Maxillofacial Imaging." Full-text screening and data extraction were independently conducted by two independent reviewers; any mismatch was resolved by discussion. The risk of bias was assessed by one reviewer and validated by another.

RESULTS

The search returned a total of 3,392 articles. After careful evaluation of the titles, abstracts, and full texts, a total number of 194 articles were included. Most studies focused on AI applications for tooth and implant classification and identification, 3-dimensional cephalometric landmark detection, lesion detection (periapical, jaws, and bone), and osteoporosis detection.

CONCLUSION

Despite the AI models' limitations, they showed promising results. Further studies are needed to explore specific applications and real-world scenarios before confidently integrating these models into dental practice.

Artificial Intelligence for Detecting Cephalometric Landmarks: A Systematic Review and Meta-analysis

Using computer vision through artificial intelligence (AI) is one of the main technological advances in dentistry. However, the existing literature on the practical application of AI for detecting cephalometric landmarks of orthodontic interest in digital images is heterogeneous, and there is no consensus regarding accuracy and precision. Thus, this review evaluated the use of artificial intelligence for detecting cephalometric landmarks in digital imaging examinations and compared it to manual annotation of landmarks. An electronic search was performed in nine databases to find studies that analyzed the detection of cephalometric landmarks in digital imaging examinations with AI and manual landmarking. Two reviewers selected the studies, extracted the data, and assessed the risk of bias using QUADAS-2. Random-effects meta-analyses determined the agreement and precision of AI compared to manual detection at a 95% confidence interval. The electronic search located 7410 studies, of which 40 were included. Only three studies presented a low risk of bias for all domains evaluated. The meta-analysis showed AI agreement rates of 79% (95% CI: 76-82%, I2 = 99%) and 90% (95% CI: 87-92%, I2 = 99%) for the thresholds of 2 and 3 mm, respectively, with a mean divergence of 2.05 (95% CI: 1.41-2.69, I2 = 10%) compared to manual landmarking. The menton cephalometric landmark showed the lowest divergence between both methods (SMD, 1.17; 95% CI, 0.82; 1.53; I2 = 0%). Based on very low certainty of evidence, the application of AI was promising for automatically detecting cephalometric landmarks, but further studies should focus on testing its strength and validity in different samples.

Cephalometric Analysis in Orthodontics Using Artificial Intelligence-A Comprehensive Review

Artificial intelligence (AI) is a branch of science concerned with developing programs and computers that can gather data, reason about it, and then translate it into intelligent actions. AI is a broad area that includes reasoning, typical linguistic dispensation, machine learning, and planning. In the area of medicine and dentistry, machine learning is currently the most widely used AI application. This narrative review is aimed at giving an outline of cephalometric analysis in orthodontics using AI. Latest algorithms are developing rapidly, and computational resources are increasing, resulting in increased efficiency, accuracy, and reliability. Current techniques for completely automatic identification of cephalometric landmarks have considerably improved efficiency and growth prospects for their regular use. The primary considerations for effective orthodontic treatment are an accurate diagnosis, exceptional treatment planning, and good prognosis estimation. The main objective of the AI technique is to make dentists' work more precise and accurate. AI is increasingly being used in the area of orthodontic treatment. It has been evidenced to be a time-saving and reliable tool in many ways. AI is a promising tool for facilitating cephalometric tracing in routine clinical practice and analyzing large databases for research purposes.

Potential and impact of artificial intelligence algorithms in dento-maxillofacial radiology

OBJECTIVES

Novel artificial intelligence (AI) learning algorithms in dento-maxillofacial radiology (DMFR) are continuously being developed and improved using advanced convolutional neural networks. This review provides an overview of the potential and impact of AI algorithms in DMFR.

MATERIALS AND METHODS

A narrative review was conducted on the literature on AI algorithms in DMFR.

RESULTS

In the field of DMFR, AI algorithms were mainly proposed for (1) automated detection of dental caries, periapical pathologies, root fracture, periodontal/peri-implant bone loss, and maxillofacial cysts/tumors; (2) classification of mandibular third molars, skeletal malocclusion, and dental implant systems; (3) localization of cephalometric landmarks; and (4) improvement of image quality. Data insufficiency, overfitting, and the lack of interpretability are the main issues in the development and use of image-based AI algorithms. Several strategies have been suggested to address these issues, such as data augmentation, transfer learning, semi-supervised training, few-shot learning, and gradient-weighted class activation mapping.

CONCLUSIONS

Further integration of relevant AI algorithms into one fully automatic end-to-end intelligent system for possible multi-disciplinary applications is very likely to be a field of increased interest in the future.

CLINICAL RELEVANCE

This review provides dental practitioners and researchers with a comprehensive understanding of the current development, performance, issues, and prospects of image-based AI algorithms in DMFR.

Current applications and development of artificial intelligence for digital dental radiography

In the last few years, artificial intelligence (AI) research has been rapidly developing and emerging in the field of dental and maxillofacial radiology. Dental radiography, which is commonly used in daily practices, provides an incredibly rich resource for AI development and attracted many researchers to develop its application for various purposes. This study reviewed the applicability of AI for dental radiography from the current studies. Online searches on PubMed and IEEE Xplore databases, up to December 2020, and subsequent manual searches were performed. Then, we categorized the application of AI according to similarity of the following purposes: diagnosis of dental caries, periapical pathologies, and periodontal bone loss; cyst and tumor classification; cephalometric analysis; screening of osteoporosis; tooth recognition and forensic odontology; dental implant system recognition; and image quality enhancement. Current development of AI methodology in each aforementioned application were subsequently discussed. Although most of the reviewed studies demonstrated a great potential of AI application for dental radiography, further development is still needed before implementation in clinical routine due to several challenges and limitations, such as lack of datasets size justification and unstandardized reporting format. Considering the current limitations and challenges, future AI research in dental radiography should follow standardized reporting formats in order to align the research designs and enhance the impact of AI development globally.

Applications of artificial intelligence and machine learning in orthodontics: a scoping review

INTRODUCTION

This scoping review aims to provide an overview of the existing evidence on the use of artificial intelligence (AI) and machine learning (ML) in orthodontics, its translation into clinical practice, and what limitations do exist that have precluded their envisioned application.

METHODS

A scoping review of the literature was carried out following the PRISMA-ScR guidelines. PubMed was searched until July 2020.

RESULTS

Sixty-two articles fulfilled the inclusion criteria. A total of 43 out of the 62 studies (69.35%) were published this last decade. The majority of these studies were from the USA (11), followed by South Korea (9) and China (7). The number of studies published in non-orthodontic journals (36) was more extensive than in orthodontic journals (26). Artificial Neural Networks (ANNs) were found to be the most commonly utilized AI/ML algorithm (13 studies), followed by Convolutional Neural Networks (CNNs), Support Vector Machine (SVM) (9 studies each), and regression (8 studies). The most commonly studied domains were diagnosis and treatment planning-either broad-based or specific (33), automated anatomic landmark detection and/or analyses (19), assessment of growth and development (4), and evaluation of treatment outcomes (2). The different characteristics and distribution of these studies have been displayed and elucidated upon therein.

CONCLUSION

This scoping review suggests that there has been an exponential increase in the number of studies involving various orthodontic applications of AI and ML. The most commonly studied domains were diagnosis and treatment planning, automated anatomic landmark detection and/or analyses, and growth and development assessment.

Clinical applicability of automated cephalometric landmark identification: Part II - Number of images needed to re-learn various quality of images

AIM

To estimate the number of cephalograms needed to re-learn for different quality images, when artificial intelligence (AI) systems are introduced in a clinic.

SETTINGS AND SAMPLE POPULATION

A total of 2385 digital lateral cephalograms (University data [1785]; Clinic F [300]; Clinic N [300]) were used. Using data from the university and clinics F and N, and combined data from clinics F and N, 50 cephalograms were randomly selected to test the system's performance (Test-data O, F, N, FN).

MATERIALS AND METHODS

To examine the recognition ability of landmark positions of the AI system developed in Part I (Original System) for other clinical data, test data F, N and FN were applied to the original system, and success rates were calculated. Then, to determine the approximate number of cephalograms needed to re-learn for different quality images, 85 and 170 cephalograms were randomly selected from each group and used for the re-learning (F85, F170, N85, N170, FN85 and FN170) of the original system. To estimate the number of cephalograms needed for re-learning, we examined the changes in the success rate of the re-trained systems and compared them with the original system. Re-trained systems F85 and F170 were evaluated with test data F, N85 and N170 from test data N, and FN85 and FN170 from test data FN.

RESULTS

For systems using F, N and FN, it was determined that 85, 170 and 85 cephalograms, respectively, were required for re-learning.

CONCLUSIONS

The number of cephalograms needed to re-learn for images of different quality was estimated.

Clinical applicability of automated cephalometric landmark identification: Part I-Patient-related identification errors

OBJECTIVES

To determine whether AI systems that recognize cephalometric landmarks can apply to various patient groups and to examine the patient-related factors associated with identification errors.

SETTING AND SAMPLE POPULATION

The present retrospective cohort study analysed digital lateral cephalograms obtained from 1785 Japanese orthodontic patients. Patients were categorized into eight subgroups according to dental age, cleft lip and/or palate, orthodontic appliance use and overjet.

MATERIALS AND METHODS

An AI system that automatically recognizes anatomic landmarks on lateral cephalograms was used. Thirty cephalograms in each subgroup were randomly selected and used to test the system's performance. The remaining cephalograms were used for system learning. The success rates in landmark recognition were evaluated using confidence ellipses with α = 0.99 for each landmark. The selection of test samples, learning of the system and evaluation of the system were repeated five times for each subgroup. The mean success rate and identification error were calculated. Factors associated with identification errors were examined using a multiple linear regression model.

RESULTS

The success rate and error varied among subgroups, ranging from 85% to 91% and 1.32 mm to 1.50 mm, respectively. Cleft lip and/or palate was found to be a factor associated with greater identification errors, whereas dental age, orthodontic appliances and overjet were not significant factors (all, P < .05).

CONCLUSION

Artificial intelligence systems that recognize cephalometric landmarks could be applied to various patient groups. Patient-oriented errors were found in patients with cleft lip and/or palate.

Machine learning in dental, oral and craniofacial imaging: a review of recent progress

Artificial intelligence has been emerging as an increasingly important aspect of our daily lives and is widely applied in medical science. One major application of artificial intelligence in medical science is medical imaging. As a major component of artificial intelligence, many machine learning models are applied in medical diagnosis and treatment with the advancement of technology and medical imaging facilities. The popularity of convolutional neural network in dental, oral and craniofacial imaging is heightening, as it has been continually applied to a broader spectrum of scientific studies. Our manuscript reviews the fundamental principles and rationales behind machine learning, and summarizes its research progress and its recent applications specifically in dental, oral and craniofacial imaging. It also reviews the problems that remain to be resolved and evaluates the prospect of the future development of this field of scientific study.

How much deep learning is enough for automatic identification to be reliable?

OBJECTIVES

To determine the optimal quantity of learning data needed to develop artificial intelligence (AI) that can automatically identify cephalometric landmarks.

MATERIALS AND METHODS

A total of 2400 cephalograms were collected, and 80 landmarks were manually identified by a human examiner. Of these, 2200 images were chosen as the learning data to train AI. The remaining 200 images were used as the test data. A total of 24 combinations of the quantity of learning data (50, 100, 200, 400, 800, 1600, and 2000) were selected by the random sampling method without replacement, and the number of detecting targets per image (19, 40, and 80) were used in the AI training procedures. The training procedures were repeated four times. A total of 96 different AIs were produced. The accuracy of each AI was evaluated in terms of radial error.

RESULTS

The accuracy of AI increased linearly with the increasing number of learning data sets on a logarithmic scale. It decreased with increasing numbers of detection targets. To estimate the optimal quantity of learning data, a prediction model was built. At least 2300 sets of learning data appeared to be necessary to develop AI as accurate as human examiners.

CONCLUSIONS

A considerably large quantity of learning data was necessary to develop accurate AI. The present study might provide a basis to determine how much learning data would be necessary in developing AI.

Learning-based local-to-global landmark annotation for automatic 3D cephalometry

The annotation of three-dimensional (3D) cephalometric landmarks in 3D computerized tomography (CT) has become an essential part of cephalometric analysis, which is used for diagnosis, surgical planning, and treatment evaluation. The automation of 3D landmarking with high-precision remains challenging due to the limited availability of training data and the high computational burden. This paper addresses these challenges by proposing a hierarchical deep-learning method consisting of four stages: 1) a basic landmark annotator for 3D skull pose normalization, 2) a deep-learning-based coarse-to-fine landmark annotator on the midsagittal plane, 3) a low-dimensional representation of the total number of landmarks using variational autoencoder (VAE), and 4) a local-to-global landmark annotator. The implementation of the VAE allows two-dimensional-image-based 3D morphological feature learning and similarity/dissimilarity representation learning of the concatenated vectors of cephalometric landmarks. The proposed method achieves an average 3D point-to-point error of 3.63 mm for 93 cephalometric landmarks using a small number of training CT datasets. Notably, the VAE captures variations of craniofacial structural characteristics.

The use and performance of artificial intelligence applications in dental and maxillofacial radiology: A systematic review

OBJECTIVES

To investigate the current clinical applications and diagnostic performance of artificial intelligence (AI) in dental and maxillofacial radiology (DMFR).

METHODS

Studies using applications related to DMFR to develop or implement AI models were sought by searching five electronic databases and four selected core journals in the field of DMFR. The customized assessment criteria based on QUADAS-2 were adapted for quality analysis of the studies included.

RESULTS

The initial electronic search yielded 1862 titles, and 50 studies were eventually included. Most studies focused on AI applications for an automated localization of cephalometric landmarks, diagnosis of osteoporosis, classification/segmentation of maxillofacial cysts and/or tumors, and identification of periodontitis/periapical disease. The performance of AI models varies among different algorithms.

CONCLUSION

The AI models proposed in the studies included exhibited wide clinical applications in DMFR. Nevertheless, it is still necessary to further verify the reliability and applicability of the AI models prior to transferring these models into clinical practice.

Comparative Evaluation of Conventional and OnyxCeph™ Dental Software Measurements on Cephalometric Radiography

Objective

Cephalometry can be measured with traditionally conventional analysing methods (hand tracing), as well as using computers. Many dental softwares have been developed for this purpose. The reliability of these programs are often compared with the conventional method. The aim of the present study was to compare the conventional method of manual cephalometric analysis with a computerized one, OnyxCeph ™ (Image Instruments, Chemnitz, Germany) dental software.

Methods

Lateral cephalometric radiographs of 150 patients (75 males and 75 females) age range 12-34 were traced by two methods. Conventional method and computerized (OnyxCeph) cephalometric analysis method. 2 maxillar, 3 mandibular, 2 maxillo-mandibular, 3 vertical, 7 dental and 1 soft tissue parameters; 10 angular, 8 linear totally 18 cephalometric parameters were measured. Intra-class correlation coefficients were performed for both methods to assess the reliability of the measurements.

Results

The results 9 of 18 parameters were found statistically significant. They were Cd-A distance, Cd-Gn distance, Go-Me distance, GoGnSN angle, ANS-Me distance, upper incisor-NA distance, lower incisor-NB distance, lower incisor-NB angle, overbite distance.

Conclusion

Despite some discrepancies in measured values between hand-tracing cephalometric analysis method and the OnyxCeph cephalometric analysis method, statistical differences were minimal and only Cd-A, Cd-Gn, Go-Me, ANS-Me, GoGnSN° were clinically important for cephalometric analysis OnyxCeph was evaluated as an efficient method to replace conventional method.

Automatic 3D cephalometric annotation system using shadowed 2D image-based machine learning

This paper presents a new approach to automatic three-dimensional (3D) cephalometric annotation for diagnosis, surgical planning, and treatment evaluation. There has long been considerable demand for automated cephalometric landmarking, since manual landmarking requires considerable time and experience as well as objectivity and scrupulous error avoidance. Due to the inherent limitation of two-dimensional (2D) cephalometry and the 3D nature of surgical simulation, there is a trend away from current 2D to 3D cephalometry. Deep learning approaches to cephalometric landmarking seem highly promising, but there exist serious difficulties in handling high dimensional 3D CT data, dimension referring to the number of voxels. To address this issue of dimensionality, this paper proposes a shadowed 2D image-based machine learning method which uses multiple shadowed 2D images with various lighting and view directions to capture 3D geometric cues. The proposed method using VGG-net was trained and tested using 2700 shadowed 2D images and corresponding manual landmarkings. Test data evaluation shows that our method achieved an average point-to-point error of 1.5 mm for the seven major landmarks.

Automatic Analysis of Lateral Cephalograms Based on Multiresolution Decision Tree Regression Voting

Cephalometric analysis is a standard tool for assessment and prediction of craniofacial growth, orthodontic diagnosis, and oral-maxillofacial treatment planning. The aim of this study is to develop a fully automatic system of cephalometric analysis, including cephalometric landmark detection and cephalometric measurement in lateral cephalograms for malformation classification and assessment of dental growth and soft tissue profile. First, a novel method of multiscale decision tree regression voting using SIFT-based patch features is proposed for automatic landmark detection in lateral cephalometric radiographs. Then, some clinical measurements are calculated by using the detected landmark positions. Finally, two databases are tested in this study: one is the benchmark database of 300 lateral cephalograms from 2015 ISBI Challenge, and the other is our own database of 165 lateral cephalograms. Experimental results show that the performance of our proposed method is satisfactory for landmark detection and measurement analysis in lateral cephalograms.

Three-Dimensional Cephalometric Analysis of Orbital Morphology Modification for Midface Correction Surgery

OBJECTIVES

The aim of this study was to create an evidence-based three-dimensional cephalometric analysis of orbits in order to perform time-efficient measurements of postoperative orbital morphology changes.

MATERIALS AND METHODS

The authors used 23 (11 bilateral and 1 unilateral) anatomical landmarks. Based on these, 6 planes, 12 angular and 16 linear measurements were determined. A three dimensional analysis was performed twice by two observers on pre and post-operative computed tomography scans of six patients who had undergone midface advancement. The mean, minimal and maximal difference, as well as standard deviation (SD) and intraclass correlation coefficient (ICC) for the inter- and intra-observer landmark selection reliability were calculated. Additionally, the mean, minimal, maximal difference and standard deviation between pre- and post-operative angular and linear measurements were calculated to examine a connection between the established measurements and any morphological change.

RESULTS

The inter and intra-examiner accuracy of all landmarks for three axes was >0.9 ICC. Despite excellent inter and intra-examiner agreement (<2.49 mm ± 2.05 mm SD) for the landmark selection, linear and angular measurements showed a mismatch, the mean SD for angular measurements was found to be 8.2° and the linear 3.04 mm.

DISCUSSION

The possible causes of linear and angular measurement discrepancies are discussed and the future direction for the development of three-dimensional cephalometric analysis of orbits proposed.

[Incisor repositioning: a new approach in orthodontics]

Lower incisors axis has a "key" position in different cephalometric analysis. However, several critics are directed towards the cephalometric profile and cephalometric landmarks (point, line and angle). The published norms and the cephalometric standards recommended for the optimal positioning of incisors could only be used as general clinical guidelines. Incisor repositioning to achieve optimal facial aesthetics requires taking into consideration the hard and soft tissues of the face, the profile, the muscular dynamics as well as the facial growth. In this work, we propose a new approach of incisor repositioning taking into account the variability of periodontal, functional and aesthetic factors.

Evaluation and Comparison of Anatomical Landmark Detection Methods for Cephalometric X-Ray Images: A Grand Challenge

Cephalometric analysis is an essential clinical and research tool in orthodontics for the orthodontic analysis and treatment planning. This paper presents the evaluation of the methods submitted to the Automatic Cephalometric X-Ray Landmark Detection Challenge, held at the IEEE International Symposium on Biomedical Imaging 2014 with an on-site competition. The challenge was set to explore and compare automatic landmark detection methods in application to cephalometric X-ray images. Methods were evaluated on a common database including cephalograms of 300 patients aged six to 60 years, collected from the Dental Department, Tri-Service General Hospital, Taiwan, and manually marked anatomical landmarks as the ground truth data, generated by two experienced medical doctors. Quantitative evaluation was performed to compare the results of a representative selection of current methods submitted to the challenge. Experimental results show that three methods are able to achieve detection rates greater than 80% using the 4 mm precision range, but only one method achieves a detection rate greater than 70% using the 2 mm precision range, which is the acceptable precision range in clinical practice. The study provides insights into the performance of different landmark detection approaches under real-world conditions and highlights achievements and limitations of current image analysis techniques.

A preliminary study for determination of three-dimensional root apex position of the maxillary teeth using camera calibration technology

OBJECTIVES

To propose a novel method for determining the three-dimensional (3D) root apex position of maxillary teeth using a two-dimensional (2D) panoramic radiograph image and a 3D virtual maxillary cast model.

METHODS

The subjects were 10 adult orthodontic patients treated with non-extraction. The multiple camera matrices were used to define transformative relationships between tooth images of the 2D panoramic radiographs and the 3D virtual maxillary cast models. After construction of the root apex-specific projective (RASP) models, overdetermined equations were used to calculate the 3D root apex position with a direct linear transformation algorithm and the known 2D co-ordinates of the root apex in the panoramic radiograph. For verification of the estimated 3D root apex position, the RASP and 3D-CT models were superimposed using a best-fit method. Then, the values of estimation error (EE; mean, standard deviation, minimum error and maximum error) between the two models were calculated.

RESULTS

The intraclass correlation coefficient values exhibited good reliability for the landmark identification. The mean EE of all root apices of maxillary teeth was 1.88 mm. The EE values, in descending order, were as follows: canine, 2.30 mm; first premolar, 1.93 mm; second premolar, 1.91 mm; first molar, 1.83 mm; second molar, 1.82 mm; lateral incisor, 1.80 mm; and central incisor, 1.53 mm.

CONCLUSIONS

Camera calibration technology allows reliable determination of the 3D root apex position of maxillary teeth without the need for 3D-CT scan or tooth templates.

Automatic craniofacial structure detection on cephalometric images

Anatomical structure tracing on cephalograms is a significant way to obtain cephalometric analysis. Cephalometric analysis is divided in two categories, manual and automatic approaches. The manual approach is limited in accuracy and repeatability due to differences in inter- and intra-personal marking. In this paper, we have attempted to develop and test a novel method for automatic localization of craniofacial structures based on the detected edges in the region of interest. Before edge detection of the particular region, the region was filtered by adaptive non local filter for noise removal by keeping the edge information undisturbed. According to the gray-scale feature at the different regions of the cephalograms, modified Canny edge detection algorithm for obtaining tissue contour was proposed. With the application of morphological opening and edge linking approaches, an improved bidirectional contour tracing methodology was proposed by an interactive selection of the starting edge pixels, the tracking process searches repetitively for an edge pixel at the neighborhood of previously searched edge pixel to segment images, and then craniofacial structures are obtained. The effectiveness of the algorithm is demonstrated by the preliminary experimental results obtained with the proposed method.