AIMC Topic: Mandible

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A deep learning approach for dental implant planning in cone-beam computed tomography images.

BMC medical imaging
BACKGROUND: The aim of this study was to evaluate the success of the artificial intelligence (AI) system in implant planning using three-dimensional cone-beam computed tomography (CBCT) images.

Three-dimensional virtual planning in mandibular advancement surgery: Soft tissue prediction based on deep learning.

Journal of cranio-maxillo-facial surgery : official publication of the European Association for Cranio-Maxillo-Facial Surgery
The study aimed at developing a deep-learning (DL)-based algorithm to predict the virtual soft tissue profile after mandibular advancement surgery, and to compare its accuracy with the mass tensor model (MTM). Subjects who underwent mandibular advanc...

Comparison of machine learning methods for prediction of osteoradionecrosis incidence in patients with head and neck cancer.

The British journal of radiology
OBJECTIVES: Mandible osteoradionecrosis (ORN) is one of the most severe toxicities in patients with head and neck cancer (HNC) undergoing radiotherapy (RT). The existing literature focuses on the correlation of mandible ORN and clinical and dosimetri...

Deep learning based prediction of extraction difficulty for mandibular third molars.

Scientific reports
This paper proposes a convolutional neural network (CNN)-based deep learning model for predicting the difficulty of extracting a mandibular third molar using a panoramic radiographic image. The applied dataset includes a total of 1053 mandibular thir...

Comparison of different machine learning approaches to predict dental age using Demirjian's staging approach.

International journal of legal medicine
CONTEXT: Dental age, one of the indicators of biological age, is inferred by radiological methods. Two of the most commonly used methods are using Demirjian's radiographic stages of permanent teeth excluding the third molar (Demirjian's and Willems' ...

Deep-learning for predicting C-shaped canals in mandibular second molars on panoramic radiographs.

Dento maxillo facial radiology
OBJECTIVE: The aim of this study was to evaluate the use of a convolutional neural network (CNN) system for predicting C-shaped canals in mandibular second molars on panoramic radiographs.

Trabeculae microstructure parameters serve as effective predictors for marginal bone loss of dental implant in the mandible.

Scientific reports
Marginal bone loss (MBL) is one of the leading causes of dental implant failure. This study aimed to investigate the feasibility of machine learning (ML) algorithms based on trabeculae microstructure parameters to predict the occurrence of severe MBL...

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...

Use of artificial intelligence to recover mandibular morphology after disease.

Scientific reports
Mandibular tumors and radical oral cancer surgery often cause bone dysmorphia and defects. Most patients present with noticeable mandibular deformations, and doctors often have difficulty determining their exact mandibular morphology. In this study, ...

Identifying Facial Features and Predicting Patients of Acromegaly Using Three-Dimensional Imaging Techniques and Machine Learning.

Frontiers in endocrinology
Facial changes are common among nearly all acromegalic patients. As they develop slowly, patients often fail to notice such changes before they become obvious. Consequently, diagnosis and treatment are often delayed. So far, convenient and accurate ...