YOLO-V5 based deep learning approach for tooth detection and segmentation on pediatric panoramic radiographs in mixed dentition.

Objectives: In the interpretation of panoramic radiographs (PRs), the identification and numbering of teeth is an important part of the correct diagnosis. This study evaluates the effectiveness of YOLO-v5 in the automatic detection, segmentation, and numbering of deciduous and permanent teeth in mixed dentition pediatric patients based on PRs.

Methods: A total of 3854 mixed pediatric patients PRs were labelled for deciduous and permanent teeth using the CranioCatch labeling program.

Detecting white spot lesions on post-orthodontic oral photographs using deep learning based on the YOLOv5x algorithm: a pilot study.

Background: Deep learning model trained on a large image dataset, can be used to detect and discriminate targets with similar but not identical appearances. The aim of this study is to evaluate the post-training performance of the CNN-based YOLOv5x algorithm in the detection of white spot lesions in post-orthodontic oral photographs using the limited data available and to make a preliminary study for fully automated models that can be clinically integrated in the future.

Methods: A total of 435 images in JPG format were uploaded into the CranioCatch labeling software and labeled white spot lesions.

The caries impacts and experiences questionnaire for Turkish children by age groups'.

Background: The Caries Impacts and Experiences Questionnaire for Children (CARIES-QC) assess children's effects of dental caries on their quality of life. This study aimed to determine the scale's Turkish version (CARIES-QC/T) validity and reliability according to age groups and to create the scale that is specific to selected age groups.

Methods: Children were divided up into 3 age groups (5-7, 8-10 and 11-14 ages).

Accuracy of age estimation with Demirjian and Nolla methods in Eastern Turkish children aged 3-17 years old.

Purpose: Dental age assessment is one of the most reliable methods of chronological age estimation used for criminal, forensic and anthropologic purposes. This study aimed to determine how accurate it was to estimate the chronological age by looking at the dental age measured with the Nolla and Demirjian methods in a Turkish sample, based on the variables of gender and age-group.

Materials And Methods: A retrospective study was performed on panoramic radiographs of 1587 subjects (774 females and 813 males), aged 3-17 years.

Detecting the presence of taurodont teeth on panoramic radiographs using a deep learning-based convolutional neural network algorithm.

Objectives: Artificial intelligence (AI) techniques like convolutional neural network (CNN) are a promising breakthrough that can help clinicians analyze medical imaging, diagnose taurodontism, and make therapeutic decisions. The purpose of the study is to develop and evaluate the function of CNN-based AI model to diagnose teeth with taurodontism in panoramic radiography.

Methods: 434 anonymized, mixed-sized panoramic radiography images over the age of 13 years were used to develop automatic taurodont tooth segmentation models using a Pytorch implemented U-Net model.

Supernumerary Teeth and Dental Development.

Purpose: The aims of this study were to compare the radiographic development of permanent teeth in a group of children with and without supernumerary teeth (ST), determine whether using cone-beam computed tomography or panoramic radiography improves the accuracy of dental age (DA) estimation and investigate the effects of factors including the numbers and positions of ST.

Methods: One hundred fifty dental radiographs of children with and without ST at the ages of 6.0 to 14.

Evaluation of impacted canines' localization and adjacent lateral incisors' root resorption with orthopantomography and cone-beam computed tomography.

Objectives: In this retrospective study, the frequency and severity of ectopic eruption in the children's permanent canines, changes between the impacted canine parameters on orthopantomography (OPG) taken for routine follow-up were demonstrated. Also, it was aimed to reveal the differences between OPG and cone beam computed tomography (CBCT).

Methods: Ten thousand (5020 girls, 4980 boys) pediatric patient, were retrospectively investigated for the presence of impacted canine radiographically.

Evaluation of the relationship between malocclusions and sleep-disordered breathing in children.

Objective: To determine the relationship between dental/skeletal malocclusions and sleep-disordered breathing (SDB) in the early diagnosis and treatment of sleep disorders in children.

Methods: Patients were evaluated by pedodontists to identify dental, skeletal, and functional malocclusion (n = 240; <15 years). In order to determine the sleep and daytime behavior of the patients, pediatric sleep questionnaires (PSQ) were applied.

Evaluation of radix entomolaris in mandibular first and second molars using cone-beam computed tomography and review of the literature.

Objectives: The aim of the present study is to identify the prevalence of radix entomolaris in mandibular first and second molars and to determine morphological classifications and associations with other root and canals.

Methods: Mandibular first and second molar teeth of 850 Turkish patients were evaluated using cone-beam computed tomography. A total of 2800 mandibular first molars and second molars were screened.

Cone beam computed tomography imaging of superior semicircular canal morphology: a retrospective comparison of cleft lip/palate patients and normal controls.

Objective: This study evaluated the prevalence and morphological characteristics of the superior semicircular canal (SSCC) in cleft lip and palate (CL/P) patients using cone beam computed tomography (CBCT).

Materials And Methods: CBCT images of 53 CL/P patients (28 males and 25 females) and a control group of 76 patients (42 males and 34 females) were evaluated. Retrospectively, 258 temporal bone images from 129 patients were evaluated in terms of SSCC morphology and divided into a normal pattern (0.