Cephalometric Landmark Detection Research Articles

A two-stage regression framework for automated cephalometric landmark detection incorporating semantically fused anatomical features and multi-head refinement loss

Accurate identification and precise localization of cephalometric landmarks provide clinicians with essential insights into craniofacial deformities, aiding in the assessment of treatment strategies for improved patient outcomes. The current methodologies heavily depend on the utilization of multiple CNNs for predicting landmark coordinates, which makes them computationally burdensome and unsuitable for translation to clinical applications. To overcome this limitation, we propose a novel, end-to-end trainable, two-stage regression framework for cephalometric landmark detection. In the initial stage, a single neural network is employed to estimate the locations of all landmarks simultaneously, enabling the identification of potential landmark regions. In the second stage, a semantic fusion block leverages the in-network multi-resolution feature hierarchy to produce high-level semantically rich features. These feature maps are then cropped based on coarsely detected landmark locations and concurrent refinement loss is used to fine-tune and refine the landmark locations. The proposed framework demonstrates the potential for enhancing clinical workflow and treatment outcomes in orthodontics. This is achieved through the utilization of a single CNN backbone augmented with multi-resolution semantically fused anatomical features, which effectively enhances representation learning in a computationally efficient manner. The performance of the proposed framework is evaluated on two publicly available anatomical landmark datasets. The experimental results demonstrate that our framework achieves a state-of-the-art detection accuracy of 87.17% within the clinically accepted range of 2 mm. The source code and the pre-trained weights are made publicly available at this https://github.com/manwaarkhd/CEPHMark-Net, promoting reproducibility and enabling further advancements.

Can artificial intelligence-driven cephalometric analysis replace manual tracing? A systematic review and meta-analysis.

This systematic review and meta-analysis aimed to investigate the accuracy and efficiency of artificial intelligence (AI)-driven automated landmark detection for cephalometric analysis on two-dimensional (2D) lateral cephalograms and three-dimensional (3D) cone-beam computed tomographic (CBCT) images. An electronic search was conducted in the following databases: PubMed, Web of Science, Embase, and grey literature with search timeline extending up to January 2024. Studies that employed AI for 2D or 3D cephalometric landmark detection were included. The selection of studies, data extraction, and quality assessment of the included studies were performed independently by two reviewers. The risk of bias was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 tool. A meta-analysis was conducted to evaluate the accuracy of the 2D landmarks identification based on both mean radial error and standard error. Following the removal of duplicates, title and abstract screening, and full-text reading, 34 publications were selected. Amongst these, 27 studies evaluated the accuracy of AI-driven automated landmarking on 2D lateral cephalograms, while 7 studies involved 3D-CBCT images. A meta-analysis, based on the success detection rate of landmark placement on 2D images, revealed that the error was below the clinically acceptable threshold of 2mm (1.39mm; 95% confidence interval: 0.85-1.92mm). For 3D images, meta-analysis could not be conducted due to significant heterogeneity amongst the study designs. However, qualitative synthesis indicated that the mean error of landmark detection on 3D images ranged from 1.0 to 5.8mm. Both automated 2D and 3D landmarking proved to be time-efficient, taking less than 1min. Most studies exhibited a high risk of bias in data selection (n = 27) and reference standard (n = 29). The performance of AI-driven cephalometric landmark detection on both 2D cephalograms and 3D-CBCT images showed potential in terms of accuracy and time efficiency. However, the generalizability and robustness of these AI systems could benefit from further improvement. PROSPERO: CRD42022328800.

Deep learning for automatic detection of cephalometric landmarks on lateral cephalometric radiographs using the Mask Region-based Convolutional Neural Network: a pilot study

ObjectiveWe examined the effectiveness and feasibility of the Mask Region-based Convolutional Neural Network (Mask R-CNN) for automatic detection of cephalometric landmarks on lateral cephalometric radiographs (LCRs). Study DesignIn total, 400 LCRs, each with 19 manually identified landmarks, were collected. Of this total, 320 images were randomly selected as the training dataset for Mask R-CNN, and the remaining 80 images were used for testing the automatic detection of the 19 cephalometric landmarks, for a total of 1520 landmarks. Detection rate, average error, and detection accuracy rate were calculated to assess Mask R-CNN performance. ResultsOf the 1520 landmarks, 1494 were detected, for a detection rate of 98.29%. The average error, or linear deviation distance between the detected points and the originally marked points of each detected landmark, ranged from 0.56 to 9.51 mm, with an average of 2.19 mm. For detection accuracy rate, 649 landmarks (43.44%) had a linear deviation distance less than 1mm, 1020 (68.27%) less than 2mm, and 1281 (85.74%) less than 4mm in deviation from the manually marked point. The average detection time was 1.48 seconds per image. ConclusionsDeep learning Mask R-CNN shows promise in enhancing cephalometric analysis by automating landmark detection on LCRs, addressing the limitations of manual analysis and demonstrating effectiveness and feasibility.

Automated Cephalometric Landmark Detection: A Novel Software Model Compared with Manual Annotation Method

Introduction AI-based automated cephalometric landmark detection streamlines orthodontic diagnosis and treatment planning, providing accurate, efficient, and reliable results. Benefits include saving time, minimizing subjectivity, improving precision, and facilitating continuous improvement. However, they should complement clinician expertise, ensuring qualified orthodontists make the final diagnosis and treatment plan. Aim To propose a method that automatically detects cephalometric landmarks on the X-ray images and compare these values with the manual annotation method. Methodology A dataset of 600 X-ray images, each containing 19 landmarks, was collected. Two orthodontists manually marked the 19 landmarks in 300 cephalograms and their coordinates were automatically extracted. The dataset was cleaned for errors, and a pre-trained CNN model with an EfficientNetB7 backbone was used for landmark detection. The model was trained on 80% of the dataset and tested on the remaining 20%. The two-step method included ROI extraction and landmark detection. The RMSE score was used to evaluate inter-examiner reliability and the R2 score was used to compare manual and automated models. Result Model landmark locations were compared to the manual method. The mean deviation of the predicted landmarks from the actual landmarks was calculated using RMSE, and the model showed acceptable accuracy compared to manual annotation. EfficientNetB7 was found to have detection accuracies similar to the manual annotation method. For landmarks like Porion, articulare, and soft tissue pogonion, the model outperformed the human annotation method and provides a consistent better result, and for points like Point A, pogonion, gnathion, and menton, the manual methods show more accurate results. Conclusion The study introduced an automated approach using deep learning to predict landmark locations, and the results demonstrate its accuracy in comparison with the manual annotation method. This approach effectively detects cephalometric landmarks, suggesting its potential for clinical use with orthodontist’s supervision.

LOW CERTAINTY OF EVIDENCE SUPPORTS THE APPLICATION OF (AI) FOR THE AUTOMATIC DETECTION OF CEPHALOMETRIC LANDMARKS WITH PROSPECTS FOR IMPROVEMENTS

Artificial Intelligence for Detecting Cephalometric Landmarks: A Systematic Review and Meta-analysis. J Digit Imaging. 2023 Jun;36(3):1158-1179. doi:10.1007/s10278-022-00766-w. The study was financed in part by the Coordenacao de Aperfeicoamentode Pessoal de Nivel Superior-Brazil (CAPES)-Finance Code 001. Systematic review and meta-analysis.

Cephalometric landmark detection without X-rays combining coordinate regression and heatmap regression

Fully automated techniques using convolutional neural networks for cephalometric landmark detection have recently advanced. However, all existing studies have adopted X-rays. The problem of direct exposure of patients to X-ray radiation remains unsolved. We propose a model for detecting cephalometric landmarks using only facial profile images without X-rays. First, the model estimates the landmark coordinates using the features of facial profile images through high-resolution representation learning. Second, considering the spatial relationship of the landmarks, the model refines the estimated coordinates. The estimated coordinates are input into fully connected networks to improve the accuracy. During the experiment, a total of 2000 facial profile images collected from 2000 female patients were used. Experiments results suggested that the proposed method may perform at a level equal to or potentially better than existing methods using cephalograms. We obtained an MRE of 0.61 mm for the test data and a mean detection rate of 98.20% within 2 mm. Our proposed two-stage learning method enables a highly accurate estimation of the landmark positions using only facial profile images. The results indicate that X-rays may not be required when detecting cephalometric landmarks.

Ceph-Net: automatic detection of cephalometric landmarks on scanned lateral cephalograms from children and adolescents using an attention-based stacked regression network

BackgroundThe success of cephalometric analysis depends on the accurate detection of cephalometric landmarks on scanned lateral cephalograms. However, manual cephalometric analysis is time-consuming and can cause inter- and intra-observer variability. The purpose of this study was to automatically detect cephalometric landmarks on scanned lateral cephalograms with low contrast and resolution using an attention-based stacked regression network (Ceph-Net).MethodsThe main body of Ceph-Net compromised stacked fully convolutional networks (FCN) which progressively refined the detection of cephalometric landmarks on each FCN. By embedding dual attention and multi-path convolution modules in Ceph-Net, the network learned local and global context and semantic relationships between cephalometric landmarks. Additionally, the intermediate deep supervision in each FCN further boosted the training stability and the detection performance of cephalometric landmarks.ResultsCeph-Net showed a superior detection performance in mean radial error and successful detection rate, including accuracy improvements in cephalometric landmark detection located in low-contrast soft tissues compared with other detection networks. Moreover, Ceph-Net presented superior detection performance on the test dataset split by age from 8 to 16 years old.ConclusionsCeph-Net demonstrated an automatic and superior detection of cephalometric landmarks by successfully learning local and global context and semantic relationships between cephalometric landmarks in scanned lateral cephalograms with low contrast and resolutions.

Automated Cephalometric Landmark Detection Using Deep Reinforcement Learning.

Accurate cephalometric landmark detection leads to accurate analysis, diagnosis, and surgical planning. Many studies on automated landmark detection have been conducted, however reinforcement learning-based networks have not yet been applied. This is the first study to apply deep Q-network (DQN) and double deep Q-network (DDQN) to automated cephalometric landmark detection to the best of our knowledge. The performance of the DQN-based network for cephalometric landmark detection was evaluated using the IEEE International Symposium of Biomedical Imaging (ISBI) 2015 Challenge data set and compared with the previously proposed methods. Furthermore, the clinical applicability of DQN-based automated cephalometric landmark detection was confirmed by testing the DQN-based and DDQN-based network using 500-patient data collected in a clinic. The DQN-based network demonstrated that the average mean radius error of 19 landmarks was smaller than 2mm, that is, the clinically accepted level, without data augmentation and additional preprocessing. Our DQN-based and DDQN-based approaches tested with the 500-patient data set showed the average success detection rate of 67.33% and 66.04% accuracy within 2mm, respectively, indicating the feasibility and potential of clinical application.

An automatic cephalometric landmark detection method based on heatmap regression and Monte Carlo dropout.

Cephalometric analysis plays an important role in orthodontic diagnosis and treatment planning. It depends on the detection of multiple landmarks, while the process is time-consuming and tedious. Although some deep learning-based automatic landmark detection algorithms have achieved excellent performance, most of them adopt multi-stage models increasing the complexity and detection time. Meanwhile, few studies focused on the uncertainty of detection results, thereby ignoring its significant clinical value. In this paper, we propose a novel approach based on heatmap regression for landmark detection, which can achieve competitive accuracy and good robustness with only one step. Furthermore, by applying Monte Carlo dropout to a U-shaped convolutional neural network, we can obtain not only the coordinate of each landmark but also the corresponding simple uncertainty, so that doctors can pay more attention to those landmarks with higher uncertainty. The evaluation results showed the mean radial error is 1.39±1.06mm and the average successful detection rate is 79.65%, 97.22% within 2mm, 4mm for the IEEE ISBI2015 Test Dataset 1, the indicators for the IEEE ISBI2015 Test Dataset 2 are 1.33±0.93mm, 80.05% and 97.53%, respectively. Our method has the potential to become an assistant tool in clinical practice. Automatic and accurate detection with uncertainty analysis is expected to help guide the doctor's judgment.

Artificial Intelligence and Machine Learning for Automated Cephalometric Landmark Identification: A Meta-Analysis Previewed by a Systematic Review.

Digital dentistry has become an integral part of our practice today, with artificial intelligence (AI) playing the predominant role. The present systematic review was intended to detect the accuracy of landmarks identified cephalometrically using machine learning and artificial intelligence and compare the same with the manual tracing (MT) group. According to the PRISMA-DTA guidelines, a scoping evaluation of the articles was performed. Electronic databases like Doaj, PubMed, Scopus, Google Scholar, and Embase from January 2001 to November 2022 were searched. Inclusion and exclusion criteria were applied, and 13 articles were studied in detail. Six full-text articles were further excluded (three articles did not provide a comparison between manual tracing and AI for cephalometric landmark detection, and three full-text articles were systematic reviews and meta-analyses). Finally, seven articles were found appropriate to be included in this review. The outcome of this systematic review has led to the conclusion that AI, when employed for cephalometric landmark detection, has shown extremely positive and promising results as compared to manual tracing.

Automatic recognition of cephalometric landmarks via multi-scale sampling strategy

The identification of head landmarks in cephalometric analysis significantly contributes in the anatomical localization of maxillofacial tissues for orthodontic and orthognathic surgery. However, the existing methods face the limitations of low accuracy and cumbersome identification process. In this pursuit, the present study proposed an automatic target recognition algorithm called Multi-Scale YOLOV3 (MS-YOLOV3) for the detection of cephalometric landmarks. It was characterized by multi-scale sampling strategies for shallow and deep features at varied resolutions, and especially contained the module of spatial pyramid pooling (SPP) for highest resolution. The proposed method was quantitatively and qualitatively compared with the classical YOLOV3 algorithm on the two data sets of public lateral cephalograms, undisclosed anterior-posterior (AP) cephalograms, respectively, for evaluating the performance. The proposed MS-YOLOV3 algorithm showed better robustness with successful detection rates (SDR) of 80.84% within 2 mm, 93.75% within 3 mm, and 98.14% within 4 mm for lateral cephalograms, and 85.75% within 2 mm, 92.87% within 3 mm, and 96.66% within 4 mm for AP cephalograms, respectively. It was concluded that the proposed model could be robustly used to label the cephalometric landmarks on both lateral and AP cephalograms for the clinical application in orthodontic and orthognathic surgery.

Cascade-refine model for cephalometric landmark detection in high-resolution orthodontic images

Landmark detection has been well developed by deep learning methods, and the cascade-connected neural network (CCNN) stands out as a widely used deep learning landmark detection method. CCNNs consist of several stacked network backbones, where the predictions of the previous network backbone are used as the input of the following one. Due to GPU memory bottlenecks, CCNNs have two limitations. First, the network backbones of CCNNs have limited numbers and depths; thus, the learning ability of CCNNs is limited. Second, CCNNs are usually trained in low-resolution images. However, the neighboring pixels in high-resolution images are usually vital for landmark detection, especially for cephalometric landmark detection. This paper interprets CCNNs as the discrete approximation of ordinary differential equations. Relying on this explanation, we further propose a novel model, called the cascade-refine model, which takes advantage of CCNNs and makes it possible to overcome the limitations of number and depth by sharing parameters among stacked network backbones. Moreover, the proposed model obeys the rule of coarse-to-fine architectures, where a global module is used to generate the coarse landmark locations, and a local module is adopted to tune the pixel error of latent landmark locations in the region of interest. The proposed model is trained in an end-to-end manner. Thus, the neighboring detailed high-resolution pixels are directly exploited for cephalometric landmark detection. The proposed cascade-refine model outperforms state-of-the-art methods on the public Automatic Cephalometric X-ray Landmark Detection Challenge 2015 dataset. We also built two private cephalometric X-ray datasets, and experimental results on both datasets demonstrate the good performance of the proposed model.

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 3mm, 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.

Automatic analysis of lateral cephalograms based on high-resolution net

Cephalometric analysis is essential in orthodontic treatment, and it is progressing toward automatic cephalometric analysis. This study aimed to establish a cephalometric landmark detection model on the basis of a high-resolution net and improve the accuracy with high resolution. A total of 2000 lateral cephalograms were collected to construct a dataset, and the number of target landmarks was 51. A high-resolution network model was applied to the landmark detection task. Four models were trained by adjusting different input resolutions to choose the most suitable resolution. A test set consisting of 300 lateral cephalograms was used for evaluation. The model was evaluated from the error size and distribution of each landmark. After 200 epochs of training, a landmark detection model was established. Under different resolutions of the input image, the mean model radial error decreased initially and then increased. At 680 × 920 pixels resolution, the minimum error and the highest detection success rate were obtained. The mean radial error was 1.08 ± 0.87 mm. The detection success rates of 2.0 mm, 2.5 mm, 3.0 mm, and 4.0 mm were 89.00%, 94.00%, 96.33%, and 98.67%, respectively. The mean radial errors of 22 landmarks were<1 mm, and the errors of other landmarks were<2 mm except for the pterion. The error distribution of landmarks followed a certain pattern. An automatic landmark detection model based on a high-resolution net was established to recognize 51 landmarks. The model showed high detection accuracy, which provides a basis for further measurement application.

DEEP LEARNING ALGORITHMS HAVE HIGH ACCURACY FOR AUTOMATED LANDMARK DETECTION ON 2D LATERAL CEPHALOGRAMS.

Deep learning for cephalometric landmark detection: systematic review and meta-analysis. Schwendicke F, Chaurasia A, Arsiwala L, Lee JH, Elhennawy K, Jost-Brinkmann PG, Demarco F, Krois J. Clin Oral Investig. 2021 Jul;25(7):4299-4309. This study was self-funded by the authors and their institutions. Systematic review and meta-analysis.

Performance of a Convolutional Neural Network- Based Artificial Intelligence Algorithm for Automatic Cephalometric Landmark Detection.

The aim of this study is to develop an artificial intelligence model to detect cephalometric landmark automatically en- abling the automatic analysis of cephalometric radiographs which have a very important place in dental practice and is used routinely in the diagnosis and treatment of dental and skeletal disorders. In this study, 1620 lateral cephalograms were obtained and 21 landmarks were included. The coordinates of all landmarks in the 1620 films were obtained to establish a labeled data set: 1360 were used as a training set, 140 as a validation set, and 180 as a testing set. A convolutional neural network-based artificial intelligence algorithm for automatic cephalometric landmark detection was developed. Mean radial error and success detection rate within the range of 2 mm, 2.5 mm, 3 mm, and 4 mm were used to eval- uate the performance of the model. Presented artificial intelligence system (CranioCatch, Eskişehir, Turkey) could detect 21 anatomic landmarks in a lateral ceph- alometric radiograph. The highest success detection rate scores of 2 mm, 2.5 mm, 3 mm, and 4 mm were obtained from the sella point as 98.3, 99.4, 99.4, and 99.4, respectively. The mean radial error ± standard deviation value of the sella point was found as 0.616 ± 0.43. The lowest success detection rate scores of 2 mm, 2.5 mm, 3 mm, and 4 mm were obtained from the Gonion point as 48.3, 62.8, 73.9, and 87.2, respectively. The mean radial error ± standard deviation value of Gonion point was found as 8.304 ± 2.98. Although the success of the automatic landmark detection using the developed artificial intelligence model was not in- sufficient for clinical use, artificial intelligence-based cephalometric analysis systems seem promising to cephalometric analysis which provides a basis for diagnosis, treatment planning, and following-up in clinical orthodontics practice.

Cephalometric Landmark Detection in Lateral Skull X-ray Images by Using Improved SpatialConfiguration-Net

Accurate automated localization of cephalometric landmarks in skull X-ray images is the basis for planning orthodontic treatments, predicting skull growth, or diagnosing face discrepancies. Such diagnoses require as many landmarks as possible to be detected on cephalograms. Today’s best methods are adapted to detect just 19 landmarks accurately in images varying not too much. This paper describes the development of the SCN-EXT convolutional neural network (CNN), which is designed to localize 72 landmarks in strongly varying images. The proposed method is based on the SpatialConfiguration-Net network, which is upgraded by adding replications of the simpler local appearance and spatial configuration components. The CNN capacity can be increased without increasing the number of free parameters simultaneously by such modification of an architecture. The successfulness of our approach was confirmed experimentally on two datasets. The SCN-EXT method was, with respect to its effectiveness, around 4% behind the state-of-the-art on the small ISBI database with 250 testing images and 19 cephalometric landmarks. On the other hand, our method surpassed the state-of-the-art on the demanding AUDAX database with 4695 highly variable testing images and 72 landmarks statistically significantly by around 3%. Increasing the CNN capacity as proposed is especially important for a small learning set and limited computer resources. Our algorithm is already utilized in orthodontic clinical practice.

Real-time Detection of Patient Head Position and Cephalometric Landmarks from Neuro-Interventional Procedure Images Using Machine Learning for Patient Eye-Lens Dose Prediction.

A deep learning (DL) model has been developed to estimate patient-lens dose in real-time for given exposure and geometric conditions during fluoroscopically-guided neuro-interventional procedures. Parameters input into the DL model for dose prediction include the patient head shift from isocenter and cephalometric landmark locations as a surrogate for head size. Machine learning (ML) models were investigated to automatically detect these parameters from the in-procedure fluoroscopic image. Fluoroscopic images of a Kyoto Kagaku anthropomorphic head phantom were taken at various known X (transverse) and Y (longitudinal) shifts, as well as different magnification modes, to create an image database. For each image, anatomical landmark coordinate locations were obtained manually using ImageJ and are used as ground-truth labels for training. This database was then used to train the two separate ML models. One ML model predicts the patient head shift in both the X and Y directions and the other model predicts the coordinates of the anatomical landmarks. From the coordinates, the distance between these anatomical landmarks is calculated, and input into the DL dose-prediction model. Model performance was evaluated using mean absolute error (MAE) and mean absolute percentage error (MAPE) for the head-shift and landmark-coordinate models, respectively. The goal is to implement these two separate models into the Dose Tracking System (DTS) developed by our group. This would allow the DTS to automatically detect the patient head size and position for eye-lens dose prediction and eliminate the need for manual input by the clinical staff.

Effectiveness of Human-Artificial Intelligence Collaboration in Cephalometric Landmark Detection.

Detection of cephalometric landmarks has contributed to the analysis of malocclusion during orthodontic diagnosis. Many recent studies involving deep learning have focused on head-to-head comparisons of accuracy in landmark identification between artificial intelligence (AI) and humans. However, a human–AI collaboration for the identification of cephalometric landmarks has not been evaluated. We selected 1193 cephalograms and used them to train the deep anatomical context feature learning (DACFL) model. The number of target landmarks was 41. To evaluate the effect of human–AI collaboration on landmark detection, 10 images were extracted randomly from 100 test images. The experiment included 20 dental students as beginners in landmark localization. The outcomes were determined by measuring the mean radial error (MRE), successful detection rate (SDR), and successful classification rate (SCR). On the dataset, the DACFL model exhibited an average MRE of 1.87 ± 2.04 mm and an average SDR of 73.17% within a 2 mm threshold. Compared with the beginner group, beginner–AI collaboration improved the SDR by 5.33% within a 2 mm threshold and also improved the SCR by 8.38%. Thus, the beginner–AI collaboration was effective in the detection of cephalometric landmarks. Further studies should be performed to demonstrate the benefits of an orthodontist–AI collaboration.

Comparative study of two software for the detection of cephalometric landmarks by artificial intelligence

Manual, tedious cephalometric analyzes of a lack of productivity (errors in plotting and measurement) making the prospect of a fully automated algorithm turning out attractive. The objectives of the study were to evaluate the positioning (accuracy and precision) of common landmarks by two software programs offering detection by artificial intelligence (WebCeph™ and DentaliQ®) compared to a manual reference and then to the comparators. 68 lateral cephalograms were selected, 22 landmarks were distributed manually and then the differences between this gold standard and the points detected by each software were measured, as well as the success detection rate (SDR). Statistical analysis was carried out by “confidence ellipses” and two-tailed t-test (p-value of 5%). In terms of accuracy, WebCeph™ and DentaliQ® show a 2 mm SDR of 57.2% and 66.5% respectively. In terms of trueness, the best results are obtained for S, Na and the incisal edges. Large random errors are found for the points Po, So, ENA, ENP, Ba and Go. Other points like Pog and B show a large vertical dispersion. Overall, a slight advantage goes to DentaliQ® even if the difference is not significant. The detection precision still seems insufficient for an utilization without human supervision. The results are promising on the detection of certain points. The artificial intelligence saves time for the clinician but the change of positioning of points should still be possible. Advances are rapid and will probably be soon for an effective clinical use.