Accurate diagnosis of periodontal and peri‐implant diseases relies significantly on radiographic examination, especially for assessing alveolar bone levels, bone defect morphology, and bone quality. This narrative review aimed to comprehensively outline the current state‐of‐the‐art in radiographic diagnosis of alveolar bone diseases, covering both two‐dimensional (2D) and three‐dimensional (3D) modalities. Additionally, this review explores recent technological advances in periodontal imaging diagnosis, focusing on their potential integration into clinical practice. Clinical probing and intraoral radiography, while crucial, encounter limitations in effectively assessing complex periodontal bone defects. Recognizing these challenges, 3D imaging modalities, such as cone beam computed tomography (CBCT), have been explored for a more comprehensive understanding of periodontal structures. The significance of the radiographic assessment approach is evidenced by its ability to offer an objective and standardized means of evaluating hard tissues, reducing variability associated with manual clinical measurements and contributing to a more precise diagnosis of periodontal health. However, clinicians should be aware of challenges related to CBCT imaging assessment, including beam‐hardening artifacts generated by the high‐density materials present in the field of view, which might affect image quality. Integration of digital technologies, such as artificial intelligence‐based tools in intraoral radiography software, the enhances the diagnostic process. The overarching recommendation is a judicious combination of CBCT and digital intraoral radiography for enhanced periodontal bone assessment. Therefore, it is crucial for clinicians to weigh the benefits against the risks associated with higher radiation exposure on a case‐by‐case basis, prioritizing patient safety and treatment outcomes.

The digital workflow process follows different steps for all dental specialties. However, the main ingredient for the diagnosis, treatment planning and follow-up workflow recipes is the imaging chain. The steps in the imaging chain usually include all or at least some of the following modalities: cone-beam computed tomographic data acquisition, segmentation of the cone-beam computed tomography image, intraoral scanning, facial three-dimensional soft tissue capture and superimposition of all the images for the creation of a virtual augmented model. As a relevant clinical problem, the accumulation of error at each step of the chain might negatively influence the final outcome. For an efficient digital workflow, it is important to be aware of the existing challenges within the imaging chain. Furthermore, artificial intelligence-based strategies need to be integrated in the future to make the workflow more simplified, accurate and efficient.

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 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). 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.

The purpose of the study was to propose and validate a three-dimensional (3D) tool for the assessment of orthognathic surgery planning accuracy and postoperative follow-up. A total of 15 patients (four male, 11 female; mean age 29.6 years) with skeletal class II and III, who underwent bimaxillary surgery were recruited for the study. All patients had preoperative computed tomography (CT), and cone-beam computerized tomography (CBCT) scans 1-6 weeks and 6 months postoperatively. The data was exported to a customized stepwise module developed in Amira software resulting in the accuracy being presented as translational and rotational differences between the planning and the actual outcome. To evaluate the reliability of the proposed method, intra-class correlation coefficient (ICC) was applied at a 95% confidence interval on the translational and rotational output of two observers. The inter- and intra-observer reliability were found to be high (ICC range: 0.94-0.98) with mean variability of less than 0.4mm and 0.7° for translational and rotational movements for both planning accuracy and follow-up protocols. The study provides a reliable, quantitative and time-efficient method for evaluating the accuracy of virtual surgical planning and postoperative follow-up.

The purpose of this study was to propose a machine learning model and assess its ability to classify TMJ pathologies on magnetic resonance (MR) images. This retrospective cohort study included 214 TMJs from 107 patients with TMJ signs and symptoms. A radiomics platform was used to extract (Huiying Medical Technology Co., Ltd., China) imaging features of TMJ pathologies, condylar bone changes, and disc displacements. Thereafter, different machine learning (ML) algorithms and logistic regression were implemented on radiomic features for feature selection, classification, and prediction. The following radiomic features included first‐order statistics, shape, texture, gray‐level cooccurrence matrix (GLCM), gray‐level run length matrix (GLRLM), and gray‐level size zone matrix (GLSZM). Six classifiers, including logistic regression (LR), random forest (RF), decision tree (DT), k‐nearest neighbors (KNN), XGBoost, and support vector machine (SVM) were used for model building which could predict the TMJ pathologies. The performance of models was evaluated by sensitivity, specificity, and ROC curve. KNN and RF classifiers were found to be the most optimal machine learning model for the prediction of TMJ pathologies. The AUC, sensitivity, and specificity for the training set were 0.89 and 1, while those for the testing set were 0.77 and 0.74, respectively, for condylar changes and disc displacement, respectively. For TMJ condylar bone changes Large‐Area High‐Gray‐Level Emphasis, Gray‐Level Nonuniformity, Long‐Run Emphasis Long‐Run High‐Gray‐Level Emphasis, Flatness, and Volume features, while for TMJ disc displacements Average Intensity, Sum Average, Spherical Disproportion, and Entropy features, were selected. This study has proposed a machine learning model by KNN and RF analysis on TMJ MR images, which can be used to classify condylar changes and TMJ disc displacements.