Validation of artificial intelligence application for dental caries diagnosis on intraoral bitewing and periapical radiographs.

Objectives: This study aimed to assess the reliability of AI-based system that assists the healthcare processes in the diagnosis of caries on intraoral radiographs.

Methods: The proximal surfaces of the 323 selected teeth on the intraoral radiographs were evaluated by two independent observers using an AI-based (Diagnocat) system. The presence or absence of carious lesions was recorded during Phase 1.

Determining the reliability of diagnosis and treatment using artificial intelligence software with panoramic radiographs.

Purpose: The objective of this study was to evaluate the accuracy and effectiveness of an artificial intelligence (AI) program in identifying dental conditions using panoramic radiographs (PRs), as well as to assess the appropriateness of its treatment recommendations.

Material And Methods: PRs from 100 patients (representing 4497 teeth) with known clinical examination findings were randomly selected from a university database. Three dentomaxillofacial radiologists and the Diagnocat AI software evaluated these PRs.

Assessing the reliability of CBCT-based AI-generated STL files in diagnosing osseous changes of the mandibular condyle: a comparative study with ground truth diagnosis.

Objectives: This study aims to evaluate the reliability of AI-generated STL files in diagnosing osseous changes of the mandibular condyle and compare them to a ground truth (GT) diagnosis made by six radiologists.

Methods: A total of 432 retrospective CBCT images from four universities were evaluated by six dentomaxillofacial radiologists who identified osseous changes such as flattening, erosion, osteophyte formation, bifid condyle formation, and osteosclerosis. All images were evaluated by each radiologist blindly and recorded on a spreadsheet.