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

OBJECTIVES: 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. SEARCH METHODS: 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. SELECTION CRITERIA: Studies that employed AI for 2D or 3D cephalometric landmark detection were included. DATA COLLECTION AND ANALYSIS: 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. RESULTS: 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 2 mm (1.39 mm; 95% confidence interval: 0.85-1.92 mm). 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 5.8 mm. Both automated 2D and 3D landmarking proved to be time-efficient, taking less than 1 min. Most studies exhibited a high risk of bias in data selection (n = 27) and reference standard (n = 29). CONCLUSION: 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. REGISTRATION: PROSPERO: CRD42022328800.

Orthodontists' criteria for prescribing cone-beam computed tomography-a multi-country survey.

OBJECTIVES: This study aimed to describe and compare CBCT imaging prescription in clinical practice among orthodontists from five countries in Europe and America. Additionally, it investigated factors associated with the prescribing and the use of guidelines for CBCT imaging. MATERIALS AND METHODS: A cross-sectional survey was carried out using an online questionnaire sent to all registered orthodontists in Belgium, Brazil, Canada, Romania, and the United States of America (USA). The data were analyzed by descriptive statistics, bivariate tests, and Poisson regression. RESULTS: The final sample consisted of 1284 participants. CBCT was prescribed by 84.4% of the participants for selected cases (84.9%), mainly for impacted teeth (92.4%), presurgical planning (54.1%), and root resorption (51.9%). High cost was most frequently the limiting factor for CBCT prescription (55.4%). Only 45.2% of those who were using CBCT imaging reported adhering to guidelines. CBCT imaging prescription was associated with the orthodontists' countries (p < .009, except for Belgium, p = .068), while the use of guidelines was associated with the respondents' country and additional training on CBCT imaging (p < .001). CONCLUSIONS: Orthodontists refer patients for CBCT for selected indications (impacted teeth, root resorption, presurgical planning, dentofacial deformities, as suggested by the international guidelines, and also for upper airway and temporomandibular joint evaluation). Many do not adhere to specific guidelines. There are substantial variations between the countries about the orthodontists' referral for CBCT and guideline usage, irrespective of gender. CBCT prescription may be limited by financial barriers, adhering to specific guidelines and prior CBCT training. CLINICAL RELEVANCE: CBCT prescription among orthodontists must be based on prescription criteria and current guidelines. It is advised to improve CBCT education and training to enhance CBCT selection, referral, analysis, and interpretation in orthodontic practice.

Interventions for impacted maxillary canines: A systematic review of the relationship between initial canine position and treatment outcome.

BACKGROUND: The objective of this review was to critically assess the existing literature on the relationship between the initial position of impacted canines and treatment outcomes. METHODS: We performed a systematic review of the available literature until February 2020 using the MEDLINE, Cochrane Central, Web of Science and PubMed databases. Prospective and retrospective studies (randomized controlled trials [RCTs], cohort studies, longitudinal follow-up studies) considering impacted maxillary canines that were orthodontically and/or surgically treated, and clearly reporting the initial position using 2D and/or 3D classifications, were included if they assessed at least one of the following: treatment success, treatment duration, number of treatment visits, radiographic outcome, periodontal health, esthetics and/or treatment complications. The included studies were assessed for risk of bias according to the Cochrane guidelines. RESULTS: Seventeen studies were reviewed (2 RCTs and 15 non-RCTs). The included studies enrolled a total of 1247 patients with an average age of 14.1 years and a total of 1597 impacted canines. Various causal relationships were detected between the success of treatment modalities and the initial state of the impacted canine (bucco-palatal position, vertical position, canine angulation, root development). DISCUSSION: Evidence, though limited, suggests that a higher alpha angle, higher vertical position and more mesial sector of the impacted canine are related to less successful interceptive and active treatment solutions, prolonged treatment time and inferior outcomes.

Deep convolutional neural network-based automated segmentation of the maxillofacial complex from cone-beam computed tomography:A validation study.

OBJECTIVES: The present study investigated the accuracy, consistency, and time-efficiency of a novel deep convolutional neural network (CNN) based model for the automated maxillofacial bone segmentation from cone beam computed tomography (CBCT) images. METHOD: A dataset of 144 scans was acquired from two CBCT devices and randomly divided into three subsets: training set (n = 110), validation set (n = 10) and testing set (n = 24). A three-dimensional (3D) U-Net (CNN) model was developed, and the achieved automated segmentation was compared with a manual approach. RESULTS: The average time required for automated segmentation was 39.1 s with a 204-fold decrease in time consumption compared to manual segmentation (132.7 min). The model was highly accurate for identification of the bony structures of the anatomical region of interest with a dice similarity coefficient (DSC) of 92.6%. Additionally, the fully deterministic nature of the CNN model was able to provide 100% consistency without any variability. The inter-observer consistency for expert-based minor correction of the automated segmentation observed an excellent DSC of 99.7%. CONCLUSION: The proposed CNN model provided a time-efficient, accurate, and consistent CBCT-based automated segmentation of the maxillofacial complex. CLINICAL SIGNIFICANCE: Automated segmentation of the maxillofacial complex could act as an alternative to the conventional segmentation techniques for improving the efficiency of the digital workflows. This approach could deliver accurate and ready-to-print3D models, essential to patient-specific digital treatment planning for orthodontics, maxillofacial surgery, and implant dentistry.