Dental age is one of the most reliable methods for determining a patient’s age. The timing of teething, the period of tooth replacement, or the degree of tooth attrition is an important diagnostic factor in the assessment of an individual’s developmental age. It is used in orthodontics, pediatric dentistry, endocrinology, forensic medicine, and pathomorphology, but also in scenarios regarding international adoptions and illegal immigrants. The methods used to date are time-consuming and not very precise. For this reason, artificial intelligence methods are increasingly used to estimate the age of a patient. The present work is a continuation of the work of Zaborowicz et al. In the presented research, a set of 21 original indicators was used to create deep neural network models. The aim of this study was to verify the ability to generate a more accurate deep neural network model compared to models produced previously. The quality parameters of the produced models were as follows. The MAE error of the produced models, depending on the learning set used, was between 2.34 and 4.61 months, while the RMSE error was between 5.58 and 7.49 months. The correlation coefficient R2 ranged from 0.92 to 0.96.
The analog methods used in the clinical assessment of the patient’s chronological age are subjective and characterized by low accuracy. When using those methods, there is a noticeable discrepancy between the chronological age and the age estimated based on relevant scientific studies. Innovations in the field of information technology are increasingly used in medicine, with particular emphasis on artificial intelligence methods. The paper presents research aimed at developing a new, effective methodology for the assessment of the chronological age using modern IT methods. In this paper, a study was conducted to determine the features of pantomographic images that support the determination of metric age, and neural models were produced to support the process of identifying the age of children and adolescents. The whole conducted work was a new methodology of metric age assessment. The result of the conducted study is a set of 21 original indicators necessary for the assessment of the chronological age with the use of computer image analysis and neural modelling, as well as three non-linear models of radial basis function networks (RBF), whose accuracy ranges from 96 to 99%. The result of the research are three neural models that determine the chronological age.
Innovative computer techniques including artificial intelligence technique have been widely used in many areas, including dentistry. Increasing requirements in the field of diagnostics and dental treatment are conducive to the digitization of many areas of dentistry. The mentioned diagnosis and treatment are based on the knowledge of a specialist. The introduction of artificial neural networks (ANN) makes it possible to support the doctor's decision regarding the diagnosis and treatment plan. The article presents the possibilities offered by artificial intelligence in the field of paediatric dentistry, both in terms of diagnostics and in the treatment process. There are many publications and ongoing research on the use of machine learning methods for dental treatment. Various types of analysis are based on digital images such as intraoral photographs, panoramic images, computed tomography images, and cephalometric radiography. StreszczenieInnowacyjne 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.
Determining the chronological age of children or adolescents is becoming an extremely necessary and important issue. Correct age-assessment methods are especially important in the process of international adoption and in the case of immigrants without valid documents confirming their identity. It is well known that traditional, analog methods widely used in clinical evaluation are burdened with a high error rate and are characterized by low accuracy. On the other hand, new digital approaches appear in medicine more and more often, which allow the increase of the accuracy of these estimates, and thus equip doctors with a tool for reliable estimation of the chronological age of children and adolescents. In this study, the work on a fast and effective metamodel is continued. Metamodels have one great advantage over all other analog and quasidigital methods—if they are well trained, a priori, on a representative set of samples, then in the age-assessment phase, results are obtained in a fraction of a second and with little error (reduced to ±7.5 months). In the here-proposed method, the standard deviation for each estimate is additionally obtained, which allows the assessment of the certainty of each result. In this study, 619 pantomographic photos of 619 patients (296 girls and 323 boys) of different ages were used. In the numerical procedure, on the other hand, a metamodel based on the Proper Orthogonal Decomposition (POD) and Gaussian Processes (GP) were utilized. The accuracy of the trained model was up to 95%.
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.
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