AIMC Topic: Molar
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Automatic detection of developmental stages of molar teeth with deep learning.
BMC oral health
Apr 1, 2025
BACKGROUND: The aim was to fully automate molar teeth developmental staging and to comprehensively analyze a wide range of deep learning models' performances for molar tooth germ detection on panoramic radiographs.
Enhancing furcation involvement classification on panoramic radiographs with vision transformers.
BMC oral health
Jan 29, 2025
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...
Detection of three-rooted mandibular first molars on panoramic radiographs using deep learning.
Scientific reports
Dec 5, 2024
This study aimed to develop a deep learning system for the detection of three-rooted mandibular first molars (MFMs) on panoramic radiographs and to assess its diagnostic performance. Panoramic radiographs, together with cone beam computed tomographic...
AI-driven segmentation of the pulp cavity system in mandibular molars on CBCT images using convolutional neural networks.
Clinical oral investigations
Nov 21, 2024
OBJECTIVE: To develop and validate an artificial intelligence (AI)-driven tool for automated segmentation of the pulp cavity system of mandibular molars on cone-beam computed tomography (CBCT) images.
Detection of C-shaped mandibular second molars on panoramic radiographs using deep convolutional neural networks.
Clinical oral investigations
Nov 18, 2024
OBJECTIVES: The C-shaped mandibular second molars (MSMs) may pose an endodontic challenge. The aim of this study was to develop a convolutional neural network (CNN)-based deep learning system for the diagnosis of C-shaped MSMs on panoramic radiograph...
Application of 3D neural networks and explainable AI to classify ICDAS detection system on mandibular molars.
The Journal of prosthetic dentistry
Oct 22, 2024
STATEMENT OF PROBLEM: Considerable variations exist in cavity preparation methods and approaches. Whether the extent and depth of cavity preparation because of the extent of caries affects the overall accuracy of training deep learning models remains...
External validation of an artificial intelligence-based method for the detection and classification of molar incisor hypomineralisation in dental photographs.
Journal of dentistry
Jul 5, 2024
OBJECTIVES: This ex vivo diagnostic study aimed to externally validate an open-access artificial intelligence (AI)-based model for the detection, classification, localisation and segmentation of enamel/molar incisor hypomineralisation (EH/MIH).
Novel AI-based automated virtual implant placement: Artificial versus human intelligence.
Journal of dentistry
Jun 22, 2024
OBJECTIVES: To assess quality, clinical acceptance, time-efficiency, and consistency of a novel artificial intelligence (AI)-driven tool for automated presurgical implant planning for single tooth replacement, compared to a human intelligence (HI)-ba...
Identification of Root Canal Morphology in Fused-rooted Mandibular Second Molars From X-ray Images Based on Deep Learning.
Journal of endodontics
May 29, 2024
INTRODUCTION: Understanding the intricate anatomical morphology of fused-rooted mandibular second molars (MSMs) is essential for root canal treatment. The present study utilized a deep learning approach to identify the three-dimensional root canal mo...
Panoramic Radiography in the Evaluation of the Relationship of Maxillary Molar Teeth and Maxillary Sinuses on the Deep Learning Models Improved with the Findings Obtained by Cone Beam Computed Tomography.
Nigerian journal of clinical practice
May 29, 2024
BACKGROUND: Panoramic radiography (PR) is available to determine the contact relationship between maxillary molar teeth (MMT) and the maxillary sinus floor (MSF). However, as PRs do not provide clear and detailed anatomical information, advanced imag...
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