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.

Dentistry is a technically oriented profession, and the health care sector is significantly influenced by the ubiquitous trend of digitalization. Some of these digital developments have the potential to result in disruptive changes for dental practice, while others may turn out to be just a pipedream. This Discovery! essay focuses on innovations built on artificial intelligence (AI) as the center-technology influencing 1) dental eHealth data management, 2) clinical and technical health care applications, and 3) services and operations. AI systems enable personalized dental medicine workflows by analyzing all eHealth data gathered from an individual patient. Besides dental-specific data, this also includes genomic, proteomic, and metabolomic information and therefore facilitates optimized and personalized treatment strategies and risk management. Based on the power of AI, the triangular frame of “data”/“health care”/“service” is supplemented by technological advancements in the field of social media, Internet of things, augmented and virtual reality, rapid prototyping, and intraoral optical scanning as well as teledentistry. Innovation continues to be critical to tackle dental problems until its routine implementation based on sound scientific evidence. Novel technologies must be viewed critically in relation to the cost-benefit ratio and the ethical implications of a misleading diagnosis or treatment produced by AI algorithms. Highly sensitive eHealth data must be handled responsibly to enable the immense benefits of these technologies to be realized for society. The focus on patient-centered research and the development of personalized dental medicine have the potential to improve individual and public health, as well as clarify the interconnectivity of disease in a more cost-effective way.

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A real-time surgical navigation system used for the placement of quad zygomatic implants demonstrated a high level of accuracy with only minimal planned-placed deviations, irrespective of the lengths or locations of the implants.

The increasing use of three-dimensional (3D) imaging techniques in dental medicine has boosted the development and use of artificial intelligence (AI) systems for various clinical problems. Cone beam computed tomography (CBCT) and intraoral/facial scans are potential sources of image data to develop 3D image-based AI systems for automated diagnosis, treatment planning, and prediction of treatment outcome. This review focuses on current developments and performance of AI for 3D imaging in dentomaxillofacial radiology (DMFR) as well as intraoral and facial scanning. In DMFR, machine learning-based algorithms proposed in the literature focus on three main applications, including automated diagnosis of dental and maxillofacial diseases, localization of anatomical landmarks for orthodontic and orthognathic treatment planning, and general improvement of image quality. Automatic recognition of teeth and diagnosis of facial deformations using AI systems based on intraoral and facial scanning will very likely be a field of increased interest in the future. The review is aimed at providing dental practitioners and interested colleagues in healthcare with a comprehensive understanding of the current trend of AI developments in the field of 3D imaging in dental medicine.

Background Abnormalities of some facial bones derived from the ectomesenchyme have been found in ectodermal dysplasia (ED) patients, but the characteristics of the zygoma are unknown. Purpose Comparison between ED patients and normal individuals to understand the anatomical features of the zygoma in ED patients. Materials and Methods Thirty patients diagnosed with ED based on clinical features and/or gene sequence tests and 80 normal individuals were recruited from 2016 to 2018. The thickness of the zygomatic body at 12 points on the superior, middle, and inferior areas and the length of four lines were measured on a three‐dimensional cone beam computed tomography image. Differences between ED patients and normal individuals were then compared. Results The zygomatic thicknesses and lengths were smaller in ED patients than in normal individuals. For ED patients, the largest thicknesses on the superior, middle, and inferior areas of the zygoma were 8.47 ± 1.49, 7.03 ± 1.56, and 5.99 ± 1.22 mm. Conclusion The development of zygomatic thickness on the inferior area and the zygomatic length were insufficient in ED patients with oligodontia. Consequently, zygomatic hypoplasia presented difficulties for the “quad approach” to zygomatic implants in this group of patients.

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