Artificial Intelligence in Maxillofacial Radiology by Leaps and Bounds

Artificial intelligence (AI) is a branch of computer science concerned with building smart software or machines capable of performing tasks that typically require human intelligence. AI is capable of mimicking human brain. Recent advances in machine learning have produced algorithms that allow automated and accurate detection, imaging, diagnosis, as well as other specialties of dentistry, which reduces stressful work and manpower. The AI plays a major role in Dental imaging by diagnosing the conditions based on the Radiographic or optical images. AI technology in dentistry could reduce cost, time, human expertise and medical error.AI in everyday life are growing by leaps and bounds. By no means there exists a doubt in the ascendancy of integrating AI into practice

Metrology and Digital Image Processing in Dentistry

Metrological techniques using digital image processing techniques have been extended into different fields of science such as optics, meteorology, mineralogy, agriculture, and medicine, among others. In the field of medicine, particularly in dentistry, it is important to perform different dental measurements to support the biometric work of specialists using panoramic radiographic images. Due to the poor capturing of these radiographic images, several problems, such as poor contrast and quality, are generally present. As the detection of the dental area must be done using these images, this chapter presents an algorithm that will assist in bettering image quality and make the dental measurements needed. This is done by binarizing using the histogram statistics of the image for the determination of threshold in order to establish sections of the teeth and the detection of the intramaxillary section by fitting a nonlinear function. The proposed method is applied to panoramic digital radiographs of subjects with permanent dentition (≥12 years and <30 years). The algorithm achieved an adjustment of 96% of the processed radiographs as a result from patients of the School of Dentistry of the Universidad de la Salle Bajío

eXplainable Artificial Intelligence (XAI) in aging clock models

eXplainable Artificial Intelligence (XAI) is a rapidly progressing field of machine learning, aiming to unravel the predictions of complex models. XAI is especially required in sensitive applications, e.g. in health care, when diagnosis, recommendations and treatment choices might rely on the decisions made by artificial intelligence systems. AI approaches have become widely used in aging research as well, in particular, in developing biological clock models and identifying biomarkers of aging and age-related diseases. However, the potential of XAI here awaits to be fully appreciated. We discuss the application of XAI for developing the "aging clocks" and present a comprehensive analysis of the literature categorized by the focus on particular physiological systems

Automated Bone Age Assessment: Motivation, Taxonomies, and Challenges

Bone age assessment (BAA) of unknown people is one of the most important topics in clinical procedure for evaluation of biological maturity of children. BAA is performed usually by comparing an X-ray of left hand wrist with an atlas of known sample bones. Recently, BAA has gained remarkable ground from academia and medicine. Manual methods of BAA are time-consuming and prone to observer variability. This is a motivation for developing automated methods of BAA. However, there is considerable research on the automated assessment, much of which are still in the experimental stage. This survey provides taxonomy of automated BAA approaches and discusses the challenges. Finally, we present suggestions for future research

Age estimation using pulp/tooth ratio single rooted premolars with digital intraoral periapical radiograph and longitudinal hemisection of tooth: A Comparative study in dravidian population

BACKGROUND: Age determination of a subject is one of the most important aspects of medico-legal cases and anthropological research. Age verification is required to obtain identification papers in order to be entitled to civil rights and /or social benefits in modern society. From the forensic point of view, the tooth may be the only undamaged human tissue remaining in mass deaths like those killed in a fire or explosion or even in individual homicides. Teeth consist of enamel as the outermost covering of tooth crown and dentin underneath, both of which are hard tissues resistant to decomposition, followed by pulp as the innermost soft tissue core. Likewise cementum is the outermost covering for the surface of root which is also resistant to decomposition. AIM OF THE STUDY To estimate and compare the age using digital intraoral periapical radiographs and longitudinal hemi section of tooth. MATERIALS AND METHODS: Specimens for the study were collected from extracted teeth in Department of Oral and maxillofacial surgery, Vivekananda Dental College for Women, Tiruchengode. The total sample of 120 mandibular premolars was collected and were divided into 5 groups. Two methods namely radiographic and hemisectioning, were used to estimate age using two parameter namely Pulp/Tooth area ratio and Pulp/Tooth width ratio at Cementoenamel Junction. With the age range of 20-70 years are included in the study. RESULTS: The reliability of the radiological and hemisectioning in estimating the age was performed using cohen’s kappa statistics. The value obtained by radiological method was 0.928 and by hemisectioning method was 0.928 and thus the reliability of both the methods are almost similar from the obtained values. CONCLUSION: The forensic judiciary has strict requirements for exact age estimation. Since there are only limited methods available for adult age estimation using extracted teeth, a combination of various methods are required for accurate age estimation. Increasing the number of parameters which would involve clinical parameter like attrition, radiological parameter like secondary dentin deposition and histological parameters like cementum annulations, dentin transluceny along with contribution of additional number of teeth can be a beneficiary aid in standardizing the precision of age estimation procedure in near future

A systematic overview of dental methods for age assessment in living individuals: from traditional to artificial intelligence-based approaches

Dental radiographies have been used for many decades for estimating the chronological age, with a view to forensic identification, migration flow control, or assessment of dental development, among others. This study aims to analyse the current application of chronological age estimation methods from dental X-ray images in the last 6 years, involving a search for works in the Scopus and PubMed databases. Exclusion criteria were applied to discard off-topic studies and experiments which are not compliant with a minimum quality standard. The studies were grouped according to the applied methodology, the estimation target, and the age cohort used to evaluate the estimation performance. A set of performance metrics was used to ensure good comparability between the different proposed methodologies. A total of 613 unique studies were retrieved, of which 286 were selected according to the inclusion criteria. Notable tendencies to overestimation and underestimation were observed in some manual approaches for numeric age estimation, being especially notable in the case of Demirjian (overestimation) and Cameriere (underestimation). On the other hand, the automatic approaches based on deep learning techniques are scarcer, with only 17 studies published in this regard, but they showed a more balanced behaviour, with no tendency to overestimation or underestimation. From the analysis of the results, it can be concluded that traditional methods have been evaluated in a wide variety of population samples, ensuring good applicability in different ethnicities. On the other hand, fully automated methods were a turning point in terms of performance, cost, and adaptability to new populationsOpen Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. This work has received financial support from Consellería de Cultura, Educación e Ordenación Universitaria (accreditation 2019–2022 ED431G-2019/04 and Group with Growth Potential ED431B 2020–2022 GPC2020/27) and the European Regional Development Fund (ERDF), which acknowledges the CiTIUS-Research Center in Intelligent Technologies of the University of Santiago de Compostela as a Research Center of the Galician University SystemS

Soft tissue coverage on the segmentation accuracy of the 3D surface-rendered model from cone-beam CT

OBJECTIVES: The aim of this study is to investigate the effect of soft tissue presence on the segmentation accuracy of the 3D hard tissue models from cone-beam computed tomography (CBCT). MATERIALS AND METHODS: Seven pairs of CBCT Digital Imaging and Communication in Medicine (DICOM) datasets, containing data of human cadaver heads and their respective dry skulls, were used. The effect of the soft tissue presence on the accuracy of the segmented models was evaluated by performing linear and angular measurements and by superimposition and color mapping of the surface discrepancies after splitting the mandible and maxillo-facial complex in the midsagittal plane. RESULTS: The linear and angular measurements showed significant differences for the more posterior transversal measurements on the mandible (p < 0.01). By splitting and superimposing the maxillo-facial complex, the mean root-mean-square error (RMSE) as a measurement of inaccuracy decreased insignificantly from 0.936 to 0.922 mm (p > 0.05). The RMSE value for the mandible, however, significantly decreased from 1.240 to 0.981 mm after splitting (p < 0.01). CONCLUSIONS: The soft tissue presence seems to affect the accuracy of the 3D hard tissue model obtained from a cone-beam CT, below a generally accepted level of clinical significance of 1 mm. However, this level of accuracy may not meet the requirement for applications where high precision is paramount. CLINICAL RELEVANCE: Accuracy of CBCT-based 3D surface-rendered models, especially of the hard tissues, are crucial in several dental and medical applications, such as implant planning and virtual surgical planning on patients undergoing orthognathic and navigational surgeries. When used in applications where high precision is paramount, the effect of soft tissue presence should be taken into consideration during the segmentation process