Diagnosis of approximal caries in children with convolutional neural networks based detection algorithms on radiographs: A pilot study.

Objectives: Approximal caries diagnosis in children is difficult, and artificial intelligence-based research in pediatric dentistry is scarce. To create a convolutional neural network (CNN)-based diagnostic system for the prompt and efficient identification of approximal caries in pediatric patients aged 5-12 years.

Materials And Methods: Pediatric patients' digital periapical radiographic images were collected to create a unique dataset.

Oral health-related quality of life of preschool-aged Turkish children with congenital heart disease.

Background: Dental caries and poor oral hygiene can affect the quality of life (QoL) of patients with congenital heart disease (CHD). Information about the oral health-related quality of life (OHRQoL) of Turkish preschool children with CHD is scarce.

Objectives: The aim of the present study was to assess the OHRQoL, and the presence of caries, plaque and gingivitis in Turkish preschool children with CHD as compared to children without CHD (control group).