AI Medical Compendium Journal:
BMC oral health

Showing 41 to 50 of 130 articles

Three-dimensional analysis of mandibular and condylar growth using artificial intelligence tools: a comparison of twin-block and Frankel II Appliances.

BMC oral health
BACKGROUND: Analyzing the morphological growth changes upon mandibular advancement between Twin Block (TB) and Functional Regulator II (FR2) in Class II patients involves measuring the condylar and mandibular changes in terms of linear and volumetric...

The influence of different factors on the bond strength of lithium disilicate-reinforced glass-ceramics to Resin: a machine learning analysis.

BMC oral health
BACKGROUND: To assess the influence of various factors on the bond strength of glass-based ceramics and develop a model that can predict the bond strength values using machine learning (ML).

Development of a diagnostic classification model for lateral cephalograms based on multitask learning.

BMC oral health
OBJECTIVES: This study aimed to develop a cephalometric classification method based on multitask learning for eight diagnostic classifications.

Machine learning models for prognosis prediction in regenerative endodontic procedures.

BMC oral health
BACKGROUND: This study aimed to establish and validate machine learning (ML) models to predict the prognosis of regenerative endodontic procedures (REPs) clinically, assisting clinicians in decision-making and avoiding treatment failure.

Detection of dental caries under fixed dental prostheses by analyzing digital panoramic radiographs with artificial intelligence algorithms based on deep learning methods.

BMC oral health
BACKGROUND: The aim of this study was to evaluate the efficacy of detecting dental caries under fixed dental prostheses (FDPs) through the analysis of panoramic radiographs utilizing convolutional neural network (CNN) based You Only Look Once (YOLO) ...

Performance of artificial intelligence on cervical vertebral maturation assessment: a systematic review and meta-analysis.

BMC oral health
BACKGROUND: Artificial intelligence (AI) methods, including machine learning and deep learning, are increasingly applied in orthodontics for tasks like assessing skeletal maturity. Accurate timing of treatment is crucial, but traditional methods such...

ChatGPT-4 Omni's superiority in answering multiple-choice oral radiology questions.

BMC oral health
OBJECTIVES: This study evaluates and compares the performance of ChatGPT-3.5, ChatGPT-4 Omni (4o), Google Bard, and Microsoft Copilot in responding to text-based multiple-choice questions related to oral radiology, as featured in the Dental Specialty...

Postoperative fever following surgery for oral cancer: Incidence, risk factors, and the formulation of a machine learning-based predictive model.

BMC oral health
BACKGROUND: Postoperative fever (POF) is a common occurrence in patients undergoing major surgery, presenting challenges and burdens for both patients and surgeons yet. This study endeavors to examine the incidence, identify risk factors, and establi...

Enhancing furcation involvement classification on panoramic radiographs with vision transformers.

BMC oral health
BACKGROUND: The severity of furcation involvement (FI) directly affected tooth prognosis and influenced treatment approaches. However, assessing, diagnosing, and treating molars with FI was complicated by anatomical and morphological variations. Cone...

Impacted lower third molar classification and difficulty index assessment: comparisons among dental students, general practitioners and deep learning model assistance.

BMC oral health
BACKGROUND: Assessing the difficulty of impacted lower third molar (ILTM) surgical extraction is crucial for predicting postoperative complications and estimating procedure duration. The aim of this study was to evaluate the effectiveness of a convol...