Synergy between Laminin-Derived Elastin-like Polypeptides (LELPs) Optimizes Cell Spreading.

A major component of the extracellular matrix (ECM), laminins, modulates cells via diverse receptors. Their fragments have emerging utility as components of "ECM-mimetics" optimized to promote cell-based therapies. Recently, we reported that a bioactive laminin peptide known as A99 enhanced cell binding and spreading via fusion to an elastin-like polypeptide (ELP). The ELP "handle" serves as a rapid, noncovalent strategy to concentrate bioactive peptide mixtures onto a surface. We now report that this strategy can be further generalized across an expanded panel of additional laminin-derived elastin-like polypeptides (LELPs). A99 (AGTFALRGDNPQG), A2G80 (VQLRNGFPYFSY), AG73 (RKRLQVQLSIRT), and EF1m (LQLQEGRLHFMFD) all promote cell spreading while showing morphologically distinct F-actin formation. Equimolar mixtures of A99:A2G80-LELPs have synergistic effects on adhesion and spreading. Finally, three of these ECM-mimetics promote the neurite outgrowth of PC-12 cells. The evidence presented here demonstrates the potential of ELPs to deposit ECM-mimetics with applications in regenerative medicine, cell therapy, and tissue engineering.

Factors influencing the development of artificial intelligence in orthodontics.

OBJECTIVES: Since developing AI procedures demands significant computing resources and time, the implementation of a careful experimental design is essential. The purpose of this study was to investigate factors influencing the development of AI in orthodontics. MATERIALS AND METHODS: A total of 162 AI models were developed, with various combinations of sample sizes (170, 340, 679), input variables (40, 80, 160), output variables (38, 76, 154), training sessions (100, 500, 1000), and computer specifications (new vs. old). The TabNet deep-learning algorithm was used to develop these AI models, and leave-one-out cross-validation was applied in training. The goodness-of-fit of the regression models was compared using the adjusted coefficient of determination values, and the best-fit model was selected accordingly. Multiple linear regression analyses were employed to investigate the relationship between the influencing factors. RESULTS: Increasing the number of training sessions enhanced the effectiveness of the AI models. The best-fit regression model for predicting the computational time of AI, which included logarithmic transformation of time, sample size, and training session variables, demonstrated an adjusted coefficient of determination of 0.99. CONCLUSION: The study results show that estimating the time required for AI development may be possible using logarithmic transformations of time, sample size, and training session variables, followed by applying coefficients estimated through several pilot studies with reduced sample sizes and reduced training sessions.

East Asian and Southern European craniofacial class III phenotype: two sides of the same coin?

OBJECTIVES: The skeletal class III phenotype is a heterogeneous condition in populations of different ethnicities. This study aimed to analyse the joint and ethnicity-specific clustering of morphological features in skeletal class III patients of Asian and European origins. MATERIALS AND METHODS: This cross-sectional study involved South Korean and Spanish participants who fulfilled the cephalometric, clinical, and ethnic-related selection criteria. Radiographic records were standardised, calibrated, and measured. A total of 54 skeletal variables were selected for varimax factorial analysis (VFA). Subsequently, a cluster analysis (CA) was performed (mixed method: k-means and hierarchical clustering). Method error and precision were assessed using ICC, Student's t-test, and the Dahlberg formula. RESULTS: A total of 285 Korean and Spanish participants with skeletal class III malocclusions were analysed. After performing VFA and CA, the joint sample revealed three global clusters, and ethnicity-specific analysis revealed four Korean and five Spanish clusters. Cluster_1_global was predominantly Spanish (79.2%) and male (83.01%) and was characterised by a predominantly mesobrachycephalic pattern and a larger cranial base, maxilla, and mandible. Cluster_2_global and Cluster_3_global were mainly South Korean (73.9% and 75.6%, respectively) and depicted opposite phenotypes of mandibular projection and craniofacial pattern. CONCLUSIONS: A distinct distribution of Spanish and South Korean participants was observed in the global analysis. Interethnic and interethnic differences were observed, primarily in the cranial base and maxilla size, mandible projection, and craniofacial pattern. CLINICAL RELEVANCE: Accurate phenotyping, reflecting the complexity of skeletal class III phenotype across diverse populations, is critical for improving diagnostic predictability and future personalised treatment protocols.

Comparison of individualized facial growth prediction models based on the partial least squares and artificial intelligence.

OBJECTIVES: To compare facial growth prediction models based on the partial least squares and artificial intelligence (AI). MATERIALS AND METHODS: Serial longitudinal lateral cephalograms from 410 patients who had not undergone orthodontic treatment but had taken serial cephalograms were collected from January 2002 to December 2022. On every image, 46 skeletal and 32 soft-tissue landmarks were identified manually. Growth prediction models were constructed using multivariate partial least squares regression (PLS) and a deep learning method based on the TabNet deep neural network incorporating 161 predictor, and 156 response, variables. The prediction accuracy between the two methods was compared. RESULTS: On average, AI showed less prediction error by 2.11 mm than PLS. Among the 78 landmarks, AI was more accurate in 63 landmarks, whereas PLS was more accurate in nine landmarks, including cranial base landmarks. The remaining six landmarks showed no statistical difference between the two methods. Overall, soft-tissue landmarks, landmarks in the mandible, and growth in the vertical direction showed greater prediction errors than hard-tissue landmarks, landmarks in the maxilla, and growth changes in the horizontal direction, respectively. CONCLUSIONS: PLS and AI methods seemed to be valuable tools for predicting growth. PLS accurately predicted landmarks with low variability in the cranial base. In general, however, AI outperformed, particularly for those landmarks in the maxilla and mandible. Applying AI for growth prediction might be more advantageous when uncertainty is considerable.

αB-Crystallin Peptide Fused with Elastin-like Polypeptide: Intracellular Activity in Retinal Pigment Epithelial Cells Challenged with Oxidative Stress.

BACKGROUND: Oxidative stress-induced retinal degeneration is among the main contributing factors of serious ocular pathologies that can lead to irreversible blindness. αB-crystallin (cry) is an abundant component of the visual pathway in the vitreous humor, which modulates protein and cellular homeostasis. Within this protein exists a 20 amino acid fragment (mini-cry) with both chaperone and antiapoptotic activity. This study fuses this mini-cry peptide to two temperature-sensitive elastin-like polypeptides (ELP) with the goal of prolonging its activity in the retina. METHODS: The biophysical properties and chaperone activity of cry-ELPs were confirmed by mass spectrometry, cloud-point determination, and dynamic light scattering 'DLS'. For the first time, this work compares a simpler ELP architecture, cry-V96, with a previously reported ELP diblock copolymer, cry-SI. Their relative mechanisms of cellular uptake and antiapoptotic potential were tested using retinal pigment epithelial cells (ARPE-19). Oxidative stress was induced with H2O2 and comparative internalization of both cry-ELPs was made using 2D and 3D culture models. We also explored the role of lysosomal membrane permeabilization by confocal microscopy. RESULTS: The results indicated successful ELP fusion, cellular association with both 2D and 3D cultures, which were enhanced by oxidative stress. Both constructs suppressed apoptotic signaling (cleaved caspase-3); however, cry-V96 exhibited greater lysosomal escape. CONCLUSIONS: ELP architecture is a critical factor to optimize delivery of therapeutic peptides, such as the anti-apoptotic mini-cry peptide; furthermore, the protection of mini-cry via ELPs is enhanced by lysosomal membrane permeabilization.

Gelatin nanofiber-reinforced decellularized amniotic membrane promotes axon regeneration and functional recovery in the surgical treatment of peripheral nerve injury.

Effective recovery of peripheral nerve injury (PNI) after surgical treatment relies on promoting axon regeneration and minimizing the fibrotic response. Decellularized amniotic membrane (dAM) has unique features as a natural matrix for promoting PNI repair due to its pro-regenerative extracellular matrix (ECM) structure and anti-inflammatory properties. However, the fragile nature and rapid degradation rate of dAM limit its widespread use in PNI surgery. Here we report an engineered composite membrane for PNI repair by combining dAM with gelatin (Gel) nanofiber membrane to construct a Gel nanofiber-dAM composite membrane (Gel-dAM) through interfacial bonding. The Gel-dAM showed enhanced mechanical properties and reduced degradation rate, while retaining maximal bioactivity and biocompatibility of dAM. These factors led to improved axon regeneration, reduced fibrotic response, and better functional recovery in PNI repair. As a fully natural materials-derived off-the-shelf matrix, Gel-dAM exhibits superior clinical translational potential for the surgical treatment of PNI.

Effect of maxillary impaction on mandibular surgical accuracy in virtually-planned orthognathic surgery: A retrospective study.

Although surgical accuracy has been evaluated in bi-maxillary procedures, few studies have investigated the relationship between maxillary and mandibular accuracy. The present study evaluated the effect of maxillary impaction accuracy on mandibular surgical outcome. This cohort study analyzed skeletal class III patients who underwent planned maxillary impaction in bi-maxillary surgery. The primary predictor was the difference between the virtual plan and surgical outcome in the maxilla, as determined by three-dimensional (3D) and vertical differences. The secondary predictors were the planned 3D distances in the maxilla and mandible. The primary outcome was mandibular surgical accuracy, defined as the difference between the planned and actual outcomes, calculated as 3D Euclidean distance. The study included 73 patients. Increased differences between the planned and actual outcomes in the maxilla were associated with increased differences in the mandible. The post-operative position of the mandible was closer to the planned position when the position of the impacted maxilla was superior than when it was inferior to the planned position. Moving the maxilla closer to the planned position resulted in a more accurate mandibular position. These findings suggest that careful surgical procedures are needed to avoid inferior positioning of the maxilla during maxillary impaction surgery.

Public interest in Invisalign in developed and developing countries: A Google Trends analysis.

OBJECTIVE: To investigate long-term changes and possible seasonal variations in Google search volumes related to Invisalign in developed and developing countries. DESIGN: Cross-sectional, Google search-based study. METHODS: Google Trends (GT) was accessed to retrieve the Relative Search Volume (RSV) of Google queries related to the search term 'Invisalign' in 10 countries selected on the basis of population size, Internet usage and socioeconomic criteria between 1 January 2004 and 30 June 2021. The countries examined were the following: Australia, Brazil, Italy, Mexico, Philippines, Saudi Arabia, Spain, Thailand, UK and USA. By applying the time series decomposition method, the trend component and the seasonal variation were identified. RESULTS: Overall, RSVs regarding Invisalign have increased significantly in all countries with the developed countries outperforming developing countries throughout most of the observation period. There was no meaningful pattern when the trends were compared either on a monthly or quarterly basis. Similar peaks and valleys were found in Australia - Brazil, UK - USA, Italy - Spain and Saudi Arabia - Philippines - Thailand. CONCLUSIONS: Public interest in online information for Invisalign has grown significantly over the years across countries of diverse socioeconomic and cultural backgrounds while seasonal patterns were observed in the related Google searches. Seasonal fluctuations seemed to follow the academic calendar. The study results may have direct implications on practice management and professional development.

Evaluation of an individualized facial growth prediction model based on the multivariate partial least squares method.

OBJECTIVES: To develop a facial growth prediction model incorporating individual skeletal and soft tissue characteristics. MATERIALS AND METHODS: Serial longitudinal lateral cephalograms were collected from 303 children (166 girls and 137 boys), who had never undergone orthodontic treatment. A growth prediction model was devised by applying the multivariate partial least squares (PLS) algorithm, with 161 predictor variables. Response variables comprised 78 lateral cephalogram landmarks. Multiple linear regression analysis was performed to investigate factors influencing growth prediction errors. RESULTS: Using the leave-one-out cross-validation method, a PLS model with 30 components was developed. Younger age at prediction resulted in greater prediction error (0.03 mm/y). Further, prediction error increased in proportion to the growth prediction interval (0.24 mm/y). Girls, subjects with Class II malocclusion, growth in the vertical direction, skeletal landmarks, and landmarks on the maxilla were associated with more accurate prediction results than boys, subjects with Class I or III malocclusion, growth in the anteroposterior direction, soft tissue landmarks, and landmarks on the mandible, respectively. CONCLUSIONS: The prediction error of the prediction model was proportional to the remaining growth potential. PLS growth prediction seems to be a versatile approach that can incorporate large numbers of predictor variables to predict numerous landmarks for an individual subject.

Hydra-Elastin-like Polypeptides Increase Rapamycin Potency When Targeting Cell Surface GRP78.

Rapalogues are powerful therapeutic modalities for breast cancer; however, they suffer from low solubility and dose-limiting side effects. To overcome these challenges, we developed a long-circulating multiheaded drug carrier called 5FA, which contains rapamycin-binding domains linked with elastin-like polypeptides (ELPs). To target these "Hydra-ELPs" toward breast cancer, we here linked 5FA with four distinct peptides which are reported to engage the cell surface form of the 78 kDa glucose-regulated protein (csGRP78). To determine if these peptides affected the carrier solubility, this library was characterized by light scattering and mass spectrometry. To guide in vitro selection of the most potent functional carrier for rapamycin, its uptake and inhibition of mTORC1 were monitored in a ductal breast cancer model (BT474). Using flow cytometry to track cellular association, it was found that only the targeted carriers enhanced cellular uptake and were susceptible to proteolysis by SubA, which specifically targets csGRP78. The functional inhibition of mTOR was monitored by Western blot for pS6K, whereby the best carrier L-5FA reduced mTOR activity by 3-fold compared to 5FA or free rapamycin. L-5FA was further visualized using super-resolution confocal laser scanning microscopy, which revealed that targeting increased exposure to the carrier by ∼8-fold. This study demonstrates how peptide ligands for GRP78, such as the L peptide (RLLDTNRPLLPY), may be incorporated into protein-based drug carriers to enhance targeting.

Time series analysis.

INTRODUCTION: This article describes a simple method of applying a time series analysis to sample data sets using a free and open statistical software program, Language R. METHODS: Records of new patients who visited 2 different university-affiliated orthodontic departments in 2 different countries were collected. Time series analysis was performed by applying Language R software. The data sets and codes were provided for tutorial and illustrative purposes. RESULTS: Using time series decomposition, the trend component and the seasonal variation were separated and visualized graphically. CONCLUSIONS: Time series analysis may be helpful to clinicians by providing a simple tool to evaluate patient characteristics and manage the practice.

Assessment of reliability in orthodontic literature.

OBJECTIVES: To map the statistical methods applied to assess reliability in orthodontic publications and to identify possible trends over time. MATERIALS AND METHODS: Original research articles published in 2009 and 2019 in a subset of orthodontic journals were downloaded. Publication characteristics, including publication year, number of authors, single vs multicenter study, geographic origin of the study, statistician involvement, study category, subject category, types of reliability assessment, and statistical methods applied to assess reliability, were recorded. Descriptive statistics, Chi-square tests, and logistic regression analyses were performed to investigate associations between reliability analysis and study characteristics. RESULTS: A total of 768 original research articles were analyzed. The most prevalent study category was observational (69%) with a statistician involved in 16% of studies. Overall, reliability was assessed in 47% of studies, and the most frequent methods applied to assess reliability were intraclass correlation coefficients or kappa statistics (60.4%). The odds of applying appropriate methods were greater in 2019 than in 2009 (odds ratio [OR]: 2.43; 95% confidence interval [CI]: 1.75, 3.37; P < .001). Involvement of a statistician resulted in greater odds of applying appropriate methods compared to no statistician involvement (OR: 1.88; 95% CI: 1.23, 2.87; P < .01). CONCLUSIONS: Over the past decade (2009 vs 2019), reliability assessment became more common in the orthodontic literature, and studies applying correct statistical methods to assess reliability significantly increased. This trend was more apparent in studies that involved a statistician, which may highlight the role of the statistician.

Evaluation of an automated superimposition method based on multiple landmarks for growing patients.

OBJECTIVES: To determine if an automated superimposition method using six landmarks (Sella, Nasion, Porion, Orbitale, Basion, and Pterygoid) would be more suitable than the traditional Sella-Nasion (SN) method to evaluate growth changes. MATERIALS AND METHODS: Serial lateral cephalograms at an average interval of 2.7 years were taken on 268 growing children who had not undergone orthodontic treatment. The T1 and T2 lateral images were manually traced. Three different superimposition methods: Björk's structural method, conventional SN, and the multiple landmark (ML) superimposition methods were applied. Bjork's structural method was used as the gold standard. Comparisons among the superimposition methods were carried out by measuring the linear distances between Anterior Nasal Spine, point A, point B, and Pogonion using each superimposition method. Multiple linear regression analysis was performed to identify factors that could affect the accuracy of the superimpositions. RESULTS: The ML superimposition method demonstrated smaller differences from Björk's method than the conventional SN method did. Greater differences among the cephalometric landmarks tested resulted when: the designated point was farther from the cranial base, the T1 age was older, and the more time elapsed between T1 and T2. CONCLUSIONS: From the results of this study in growing patients, the ML superimposition method seems to be more similar to Björk's structural method than the SN superimposition method. A major advantage of the ML method is likely to be that it can be applied automatically and may be just as reliable as manual superimposition methods.

Virtually-Planned Orthognathic Surgery Achieves an Accurate Condylar Position.

PURPOSE: Accuracy in orthognathic surgery with virtual planning has been reported, but detailed analysis of accuracy according to anatomic location, including the mandibular condyle, is insufficient. The purpose of this study was to compare the virtual plan and surgical outcomes and analyze the degree and distribution of errors according to each anatomic location. PATIENTS AND METHODS: This retrospective cohort study evaluated skeletal class III patients, treated with bimaxillary surgery. The primary predictor was anatomic locations that consisted of right and left condyles, maxilla, and the distal segment of the mandible. Other variables were age and gender. The primary outcome was surgical accuracy, defined as mean 3-dimensional distance error, mean absolute error, and mean error along the horizontal, vertical, and anteroposterior axes between the virtual plan and surgical outcomes. Landmarks were compared using a computational method based on affine transformation with a 1-time landmark setting. The mean errors were visualized with multidimensional scattergrams. Bivariate and regression statistics were computed. RESULTS: This study included 52 patients, 26 men and 26 women, with a mean age of 21 years and 3 months. The mean 3D distance errors for condylar landmarks, maxillary landmarks, and landmarks on the distal segment of the mandible were 1.03, 1.25, and 2.24 mm, respectively. Condylar landmarks, maxillary landmarks, and the landmarks on the distal segment of the mandible were positioned at 0.49 mm inferior, 0.28 mm anterior, and 1.25 mm inferior, respectively. The landmark errors for the distal segment of the mandible exhibited a wider distribution than those for condylar and maxillary landmarks. CONCLUSIONS: Agreement between the planned and actual outcome aided by virtual surgical planning was highest for the condyles, followed by the maxilla, and the distal segment of the mandible. It is important to consider the tendency for surgical errors in each anatomic location during operations.

Central and peripheral GLP-1 systems independently suppress eating.

The anorexigenic peptide glucagon-like peptide-1 (GLP-1) is secreted from gut enteroendocrine cells and brain preproglucagon (PPG) neurons, which, respectively, define the peripheral and central GLP-1 systems. PPG neurons in the nucleus tractus solitarii (NTS) are widely assumed to link the peripheral and central GLP-1 systems in a unified gut-brain satiation circuit. However, direct evidence for this hypothesis is lacking, and the necessary circuitry remains to be demonstrated. Here we show that PPGNTS neurons encode satiation in mice, consistent with vagal signalling of gastrointestinal distension. However, PPGNTS neurons predominantly receive vagal input from oxytocin-receptor-expressing vagal neurons, rather than those expressing GLP-1 receptors. PPGNTS neurons are not necessary for eating suppression by GLP-1 receptor agonists, and concurrent PPGNTS neuron activation suppresses eating more potently than semaglutide alone. We conclude that central and peripheral GLP-1 systems suppress eating via independent gut-brain circuits, providing a rationale for pharmacological activation of PPGNTS neurons in combination with GLP-1 receptor agonists as an obesity treatment strategy.

Evaluation of automated cephalometric analysis based on the latest deep learning method.

OBJECTIVES: To compare an automated cephalometric analysis based on the latest deep learning method of automatically identifying cephalometric landmarks (AI) with previously published AI according to the test style of the worldwide AI challenges at the International Symposium on Biomedical Imaging conferences held by the Institute of Electrical and Electronics Engineers (IEEE ISBI). MATERIALS AND METHODS: This latest AI was developed by using a total of 1983 cephalograms as training data. In the training procedures, a modification of a contemporary deep learning method, YOLO version 3 algorithm, was applied. Test data consisted of 200 cephalograms. To follow the same test style of the AI challenges at IEEE ISBI, a human examiner manually identified the IEEE ISBI-designated 19 cephalometric landmarks, both in training and test data sets, which were used as references for comparison. Then, the latest AI and another human examiner independently detected the same landmarks in the test data set. The test results were compared by the measures that appeared at IEEE ISBI: the success detection rate (SDR) and the success classification rates (SCR). RESULTS: SDR of the latest AI in the 2-mm range was 75.5% and SCR was 81.5%. These were greater than any other previous AIs. Compared to the human examiners, AI showed a superior success classification rate in some cephalometric analysis measures. CONCLUSIONS: This latest AI seems to have superior performance compared to previous AI methods. It also seems to demonstrate cephalometric analysis comparable to human examiners.

Evaluation of an automated superimposition method for computer-aided cephalometrics.

OBJECTIVES: To evaluate a new superimposition method compatible with computer-aided cephalometrics and to compare superimposition error to that of the conventional Sella-Nasion (SN) superimposition method. MATERIALS AND METHODS: A total of 283 lateral cephalometric radiographs were collected and cephalometric landmark identification was performed twice by the same examiner at a 3-month interval. The second tracing was superimposed on the first tracing by both the SN superimposition method and the new, proposed method. The proposed method not only relied on SN landmarks but also minimized the differences between four additional landmarks: Porion, Orbitale, Basion, and Pterygoid. The errors between the landmarks of the duplicate tracings oriented by the two superimposition methods were calculated at Anterior Nasal Spine, Point A, Point B, Pogonion, and Gonion. The paired t-test was used to find any statistical difference in the superimposition errors by the two superimposition methods and to investigate whether there existed clinically significant differences between the two methods. RESULTS: The proposed method demonstrated smaller superimposition errors than did the conventional SN superimposition method. When comparisons between the two superimposition methods were made with a 1-mm error range, there were clinically significant differences between them. CONCLUSIONS: The proposed method that was compatible with computer-aided cephalometrics might be a reliable superimposition method for superimposing serial cephalometric images.

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.

Embolization of pancreatic arteriovenous malformation: A case report.

BACKGROUND: Pancreatic arteriovenous malformation (AVM) is a rare disease with a number of different reported treatment methods, but there are as yet no established or definite treatments for the disease. CASE SUMMARY: A 43-year-old man visited the hospital due to periumbilical pain. The patient underwent imaging study and laboratory testing for evaluation of cause. Pancreatic AVM associated with pancreatitis was suspected on computed tomography and magnetic resonance imaging. The patient was diagnosed with pancreatic AVM with pancreatitis on imaging study and angiography. Transcatheter arterial embolization with various embolic materials was performed. Follow-up computed tomography scan revealed progressive regression of AVM and improvement of pancreatitis. At two-year follow-up, the patient showed no recurrence of symptom or pancreatitis. CONCLUSION: Transcatheter arterial embolization can be considered an effective treatment modality for selective cases of pancreatic AVM.

Automated identification of cephalometric landmarks: Part 2-Might it be better than human?

OBJECTIVES: To compare detection patterns of 80 cephalometric landmarks identified by an automated identification system (AI) based on a recently proposed deep-learning method, the You-Only-Look-Once version 3 (YOLOv3), with those identified by human examiners. MATERIALS AND METHODS: The YOLOv3 algorithm was implemented with custom modifications and trained on 1028 cephalograms. A total of 80 landmarks comprising two vertical reference points and 46 hard tissue and 32 soft tissue landmarks were identified. On the 283 test images, the same 80 landmarks were identified by AI and human examiners twice. Statistical analyses were conducted to detect whether any significant differences between AI and human examiners existed. Influence of image factors on those differences was also investigated. RESULTS: Upon repeated trials, AI always detected identical positions on each landmark, while the human intraexaminer variability of repeated manual detections demonstrated a detection error of 0.97 ± 1.03 mm. The mean detection error between AI and human was 1.46 ± 2.97 mm. The mean difference between human examiners was 1.50 ± 1.48 mm. In general, comparisons in the detection errors between AI and human examiners were less than 0.9 mm, which did not seem to be clinically significant. CONCLUSIONS: AI showed as accurate an identification of cephalometric landmarks as did human examiners. AI might be a viable option for repeatedly identifying multiple cephalometric landmarks.