AI Medical Compendium Journal:
BMC oral health

Showing 1 to 10 of 130 articles

Mandibular condyle detection using deep learning and double attractor-based energy valley optimizer algorithm.

BMC oral health
The temporomandibular joint (TMJ) constitutes a bilateral ginglymoarthrodial joint, wherein each condyle interacts with its corresponding glenoid fossa of the temporal bone. There is a critical need to understand better and accurately characterize th...

Application of Mask R-CNN for automatic recognition of teeth and caries in cone-beam computerized tomography.

BMC oral health
OBJECTIVES: Deep convolutional neural networks (CNNs) are advancing rapidly in medical research, demonstrating promising results in diagnosis and prediction within radiology and pathology. This study evaluates the efficacy of deep learning algorithms...

Artificial intelligence-based detection of dens invaginatus in panoramic radiographs.

BMC oral health
OBJECTIVE: The aim of this study was to automatically detect teeth with dens invaginatus (DI) in panoramic radiographs using deep learning algorithms and to compare the success of the algorithms.

Comparison of responses from different artificial intelligence-powered chatbots regarding the All-on-four dental implant concept.

BMC oral health
BACKGROUND: Recent advancements in Artificial Intelligence (AI) have transformed the healthcare field, particularly through chatbots like ChatGPT, OpenEvidence, and MediSearch. These tools analyze complex data to aid clinical decision-making, enhanci...

AI-powered segmentation of bifid mandibular canals using CBCT.

BMC oral health
OBJECTIVE: Accurate segmentation of the mandibular and bifid canals is crucial in dental implant planning to ensure safe implant placement, third molar extractions and other surgical interventions. The objective of this study is to develop and valida...

Machine learning model for preoperative classification of stromal subtypes in salivary gland pleomorphic adenoma based on ultrasound histogram analysis.

BMC oral health
OBJECTIVES: Accurate preoperative discrimination of salivary gland pleomorphic adenoma (SPA) stromal subtypes is essential for therapeutic plannings. We aimed to establish and test machine learning (ML) models for classification of stromal subtypes i...

The potentials and challenges of integrating generative artificial intelligence (AI) in dental and orthodontic education: a systematic review.

BMC oral health
BACKGROUND: Generative AI technologies offer significant opportunities to enhance orthodontic education by improving knowledge retention, clinical decision-making, and skills training. This systematic review aimed to evaluate the impact of generative...

Comparative analysis of AI chatbot (ChatGPT-4.0 and Microsoft Copilot) and expert responses to common orthodontic questions: patient and orthodontist evaluations.

BMC oral health
OBJECTIVE: The aim of this study was to evaluate the adequacy of responses provided by experts and artificial intelligence-based chatbots (ChatGPT-4.0 and Microsoft Copilot) to frequently asked orthodontic questions, utilizing scores assigned by pati...

Segmentation of airways and soft tissues on panoramic radiographs using artificial intelligence technology.

BMC oral health
BACKGROUND: Segmentation of airways and soft tissues on panoramic radiographs is a challenging yet crucial task in dental diagnostics, as these regions can often be confused with fractures or other lesions due to superimposition. This study aimed to ...