Background: Artificial intelligence (AI) is playing an increasingly important role in everyday dental practice and diagnosis, especially in the area of analysing digital pantomographic images. Through the use of innovative and modern algorithms, clinicians can more quickly and accurately identify pathological changes contained in digital pantomographic images, such as caries, periapical lesions, cysts, and tumours. It should be noted that pantomographic images are one of the most commonly used imaging modalities in dentistry, and their digital analysis enables the construction of AI models to support diagnosis. Objectives: This paper presents a systematic narrative review of studies included in scientific articles from 2020 to 2025, focusing on three main diagnostic areas: detection of caries, periapical lesions, and cysts and tumours. The results show that neural network models, such as U-Net, Swin Transformer, and CNN, are most commonly used in caries diagnosis and have achieved high performance in lesion identification. In the case of periapical lesions, AI models such as U-Net and Decision Tree also showed high performance, surpassing the performance of young dentists in assessing radiographs in some cases. Results: The studies cited in this review show that the diagnosis of cysts and tumours, on the other hand, relies on more advanced models such as YOLO v8, DCNN, and EfficientDet, which in many cases achieved more than 95% accuracy in the detection of this pathology. The cited studies were conducted at various universities and institutions around the world, and the databases (case databases) analysed in this work ranged from tens to thousands of images. Conclusions: The main conclusion of the literature analysis is that, thanks to its accessibility, speed, and accuracy, AI can significantly assist the work of physicians by reducing the time needed to analyse images. However, despite the promising results, AI should only be considered as an enabling tool and not as a replacement for the knowledge of doctors and their long experience. There is still a need for further improvement of algorithms and further training of the network, especially in identifying more complex clinical cases.

Eruption is a complex and dynamic process determined by both genetic and epigenetic factors. This process involves a number of changes in the tissues surrounding the tooth and in tooth morphology. The aim of this study was to analyze the eruption sequence of permanent canines and premolars on the basis of pantomographic images. The study material consisted of 300 digital pantomographic images of children in the developmental period. The study group consisted of 165 boys and 135 girls. Images of patients of Polish nationality, aged 6–10 years, without diagnosed systemic diseases and local disorders were used in the study. Results: The study has shown that the most common pattern of tooth eruption in the lateral zones is type A positioning of the lateral teeth, which is 4-5-3. This pattern is characteristic of both girls and boys. This pattern also occurs most frequently in the maxilla in both boys and girls. In the mandible, on the contrary, two patterns of lateral tooth eruption were predominant. In girls, types A and E/4-5-3 and 3-4-5/occurred in the mandible, while in boys, types A and C/4-5-3 and 5-4-3/were observed in the mandible. The process of tooth eruption is a recognized measure of a child’s physical development, and pantomographic images are an effective and common diagnostic tool.

Background: The dynamic development of information technologies, particularly in the fields of computer image analysis and artificial intelligence (AI) algorithms, plays an increasingly important role in orthodontic diagnostics. Cephalometric images constitute a fundamental element in orthodontic treatment planning. They contain encoded information related to the assessment of craniofacial growth and development, which is the focus of algorithms employing machine learning and process automation. Objectives: The aim of this paper is to present the current state of knowledge regarding the application of artificial intelligence methods in cephalometric image analysis, with particular emphasis on studies published between 2020 and 2025 in the Scopus and Web of Science databases. Results: Twenty key studies were included. The most commonly used models were convolutional neural networks (CNN), You Only Look Once (YOLO), Bayesian convolutional neural networks (BCNN), artificial neural networks (ANN), stacked hourglass networks, and Deep Neural Patchworks (DNP). In landmark detection tasks, the average location errors ranged from 1 to 2 mm compared to expert annotations, remaining within clinically acceptable limits. YOLO- and CNN-based systems achieved accuracy comparable to that of experienced orthodontists, while BCNN models additionally provided uncertainty estimates that improved clinical interpretability. In classification tasks, artificial neural network (ANN) models assessing cervical vertebral maturity (CVM) achieved an accuracy of up to 95%. In screening studies prior to orthognathic surgery, a multilayer perceptron combined with a regional convolutional neural network achieved 96.3% agreement with expert decisions. Conclusions: AI-based tools provide clinically acceptable accuracy in cephalometric analysis, with landmark detection errors typically ranging from 1 to 2 mm compared to expert assessment. These systems improve repeatability and significantly reduce analysis time, especially when used in semi-automated workflows. AI-based assessment of cervical vertebral maturity and surgical eligibility shows high agreement with expert decisions, confirming their role as reliable tools to support clinical decision-making. Nevertheless, broader validation in different patient populations is necessary before routine clinical implementation.

Panoramic radiograph is a universally used diagnostic method in dentistry for identifying various dental anomalies and assessing developmental stages of the dentition. The second premolar is the tooth with the highest number of developmental abnormalities. The purpose of this study was to generate neural models for assessing the position of the bud of the second premolar tooth based on analysis of tooth–bone indicators of other teeth. The study material consisted of 300 digital pantomographic radiographs of children in their developmental period. The study group consisted of 165 boys and 135 girls. The study included radiographs of patients of Polish nationality, aged 6–10 years, without diagnosed systemic diseases and local disorders. The study resulted in a set of original indicators to accurately assess the development of the second premolar tooth using computer image analysis and neural modelling. Five neural networks were generated, whose test quality was between 68–91%. The network dedicated to all quadrants of the dentition showed the highest test quality at 91%. The training, validation and test subsets were divided in a standard 2:1;1 ratio into 150 training cases, 75 test cases and 75 validation cases.

Innowacyjne techniki komputerowe, w tym możliwości, jakie daje sztuczna inteligencja, znajdują coraz szersze zastosowanie w wielu dziedzinach stomatologii. Coraz większe wymagania w zakresie diagnostyki i leczenia sprzyjają cyfryzacji wielu dziedzin stomatologii. Diagnoza i leczenie są oparte na wiedzy specjalisty. Wprowadzenie sztucznych sieci neuronowych pozwala na wspomaganie decyzji lekarza dotyczącej diagnozy i planu leczenia. W artykule przedstawiono możliwości, jakie daje sztuczna inteligencja w dziedzinie stomatologii dziecięcej zarówno w zakresie diagnostyki, jak i w procesie leczenia. Trwają aktywne badania nad zastosowaniem uczenia maszynowego opartego na sztucznych sieciach neuronowych do leczenia stomatologicznego poprzez analizę różnego rodzaju obrazów, takich jak fotografie wewnątrzustne, zdjęcia panoramiczne, obrazy tomografii komputerowej i radiografii cefalometrycznej.

Acceptable speed of tooth movement in orthodontics is main limiting factor in treatment time. Although there are many devices and accessories which helps to move teeth – big forces in small period can cause complications like tooth resorption. The paper aimed to verify low-intensity pulsed ultrasound as method to accelerate orthodontic movement. A literature review from 2002-2020 using data bases like PubMed and Medline was performed. LIPUS orthodontics, LIPUS in dentistry, low-intensity pulsed ultrasound. 35 articles associated with the aim of this review were chosen and analyzed. Low-intensity pulsed ultrasounds start biological sequence on molecular level. Reactions on cellular structures accelerate bone healing and reduce negative effects and complications during treatment. Low-intensity pulsed ultrasounds have positive impact on bone wounds healing, treating developmental bone defects, cartilage and soft tissues reactions, tooth resorption, teeth tissue and periodontal regeneration and on orthodontic treatment acceleration.