AI Medical Compendium Journal:
BMC oral health

Showing 121 to 130 of 130 articles

A two-stage deep learning architecture for radiographic staging of periodontal bone loss.

BMC oral health
BACKGROUND: Radiographic periodontal bone loss is one of the most important basis for periodontitis staging, with problems such as limited accuracy, inconsistency, and low efficiency in imaging diagnosis. Deep learning network may be a solution to im...

A pilot study of a deep learning approach to detect marginal bone loss around implants.

BMC oral health
BACKGROUND: Recently, there has been considerable innovation in artificial intelligence (AI) for healthcare. Convolutional neural networks (CNNs) show excellent object detection and classification performance. This study assessed the accuracy of an a...

Deep semi-supervised learning for automatic segmentation of inferior alveolar nerve using a convolutional neural network.

BMC oral health
BACKGROUND: The inferior alveolar nerve (IAN) innervates and regulates the sensation of the mandibular teeth and lower lip. The position of the IAN should be monitored prior to surgery. Therefore, a study using artificial intelligence (AI) was planne...

Deep learning based prediction of necessity for orthognathic surgery of skeletal malocclusion using cephalogram in Korean individuals.

BMC oral health
BACKGROUND: Posteroanterior and lateral cephalogram have been widely used for evaluating the necessity of orthognathic surgery. The purpose of this study was to develop a deep learning network to automatically predict the need for orthodontic surgery...

Individual mandibular movement registration and reproduction using an optoeletronic jaw movement analyzer and a dedicated robot: a dental technique.

BMC oral health
BACKGROUND: Fully adjustable articulators and pantographs record and reproduce individual mandibular movements. Although these instruments are accurate, they are operator-dependant and time-consuming. Pantographic recording is affected by inter and i...

Automated cephalometric landmark detection with confidence regions using Bayesian convolutional neural networks.

BMC oral health
BACKGROUND: Despite the integral role of cephalometric analysis in orthodontics, there have been limitations regarding the reliability, accuracy, etc. of cephalometric landmarks tracing. Attempts on developing automatic plotting systems have continuo...

Deep learning-based dental plaque detection on primary teeth: a comparison with clinical assessments.

BMC oral health
BACKGROUND: Dental plaque causes many common oral diseases (e.g., caries, gingivitis, and periodontitis). Therefore, plaque detection and control are extremely important for children's oral health. The objectives of this study were to design a deep l...

Artificial intelligence in fixed implant prosthodontics: a retrospective study of 106 implant-supported monolithic zirconia crowns inserted in the posterior jaws of 90 patients.

BMC oral health
BACKGROUND: Artificial intelligence (AI) is a branch of computer science concerned with building smart software or machines capable of performing tasks that typically require human intelligence. We present a protocol for the use of AI to fabricate im...

Predicting oral malodour based on the microbiota in saliva samples using a deep learning approach.

BMC oral health
BACKGROUND: Oral malodour is mainly caused by volatile sulphur compounds produced by bacteria and bacterial interactions. It is difficult to predict the presence or absence of oral malodour based on the abundances of specific species and their combin...

Evaluating the impact of AI-generated educational content on patient understanding and anxiety in endodontics and restorative dentistry: a comparative study.

BMC oral health
BACKGROUND: Effective patient education is critical in enhancing treatment outcomes and reducing anxiety in dental procedures. This study compares the effectiveness of AI-generated educational materials with traditional methods in improving patient c...