AI Medical Compendium Journal:
BMC oral health

Showing 101 to 110 of 130 articles

An artificial intelligence study: automatic description of anatomic landmarks on panoramic radiographs in the pediatric population.

BMC oral health
BACKGROUND: Panoramic radiographs, in which anatomic landmarks can be observed, are used to detect cases closely related to pediatric dentistry. The purpose of the study is to investigate the success and reliability of the detection of maxillary and ...

Revolutionising dental technologies: a qualitative study on dental technicians' perceptions of Artificial intelligence integration.

BMC oral health
BACKGROUND: The integration of artificial intelligence (AI) in dentistry has the potential to revolutionise the field of dental technologies. However, dental technicians' views on the use of AI in dental technology are still sparse in the literature....

Intra-oral scan segmentation using deep learning.

BMC oral health
OBJECTIVE: Intra-oral scans and gypsum cast scans (OS) are widely used in orthodontics, prosthetics, implantology, and orthognathic surgery to plan patient-specific treatments, which require teeth segmentations with high accuracy and resolution. Manu...

The psc-CVM assessment system: A three-stage type system for CVM assessment based on deep learning.

BMC oral health
BACKGROUND: Many scholars have proven cervical vertebral maturation (CVM) method can predict the growth and development and assist in choosing the best time for treatment. However, assessing CVM is a complex process. The experience and seniority of t...

Detection of the pathological exposure of pulp using an artificial intelligence tool: a multicentric study over periapical radiographs.

BMC oral health
BACKGROUND: Introducing artificial intelligence (AI) into the medical field proved beneficial in automating tasks and streamlining the practitioners' lives. Hence, this study was conducted to design and evaluate an AI tool called Make Sure Caries Det...

Is automatic cephalometric software using artificial intelligence better than orthodontist experts in landmark identification?

BMC oral health
BACKGROUND: To evaluate the techniques used for the automatic digitization of cephalograms using artificial intelligence algorithms, highlighting the strengths and weaknesses of each one and reviewing the percentage of success in localizing each ceph...

Detecting representative characteristics of different genders using intraoral photographs: a deep learning model with interpretation of gradient-weighted class activation mapping.

BMC oral health
BACKGROUND: Sexual dimorphism is obvious not only in the overall architecture of human body, but also in intraoral details. Many studies have found a correlation between gender and morphometric features of teeth, such as mesio-distal diameter, buccal...

Deep learning-based prediction of osseointegration for dental implant using plain radiography.

BMC oral health
BACKGROUND: In this study, we investigated whether deep learning-based prediction of osseointegration of dental implants using plain radiography is possible.

Evaluating the accuracy of automated cephalometric analysis based on artificial intelligence.

BMC oral health
BACKGROUND: The purpose of this study was to evaluate the accuracy of automatic cephalometric landmark localization and measurements using cephalometric analysis via artificial intelligence (AI) compared with computer-assisted manual analysis.

Prediction of orthognathic surgery plan from 3D cephalometric analysis via deep learning.

BMC oral health
BACKGROUND: Preoperative planning of orthognathic surgery is indispensable for achieving ideal surgical outcome regarding the occlusion and jaws' position. However, orthognathic surgery planning is sophisticated and highly experience-dependent, which...