AIMC Topic: Tooth Extraction
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Evaluation of multi-task learning in deep learning-based positioning classification of mandibular third molars.
Scientific reports
Jan 13, 2022
Pell and Gregory, and Winter's classifications are frequently implemented to classify the mandibular third molars and are crucial for safe tooth extraction. This study aimed to evaluate the classification accuracy of convolutional neural network (CNN...
Machine learning from clinical data sets of a contemporary decision for orthodontic tooth extraction.
Orthodontics & craniofacial research
Jun 7, 2021
OBJECTIVE: To examine the robustness of the published machine learning models in the prediction of extraction vs non-extraction for a diverse US sample population seen by multiple providers.
Deep learning based prediction of extraction difficulty for mandibular third molars.
Scientific reports
Jan 21, 2021
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...
Automated detection of third molars and mandibular nerve by deep learning.
Scientific reports
Jun 21, 2019
The approximity of the inferior alveolar nerve (IAN) to the roots of lower third molars (M3) is a risk factor for the occurrence of nerve damage and subsequent sensory disturbances of the lower lip and chin following the removal of third molars. To a...
Predicting postoperative facial swelling following impacted mandibular third molars extraction by using artificial neural networks evaluation.
Scientific reports
Aug 16, 2018
Patients' postoperative facial swelling following third molars extraction may have both biological impacts and social impacts. The purpose of this study was to evaluate the accuracy of artificial neural networks in the prediction of the postoperative...
Machine learning to predict the occurrence of bisphosphonate-related osteonecrosis of the jaw associated with dental extraction: A preliminary report.
Bone
Apr 24, 2018
INTRODUCTION: The aim of this study was to build and validate five types of machine learning models that can predict the occurrence of BRONJ associated with dental extraction in patients taking bisphosphonates for the management of osteoporosis.
Multivariate decoding of cerebral blood flow measures in a clinical model of on-going postsurgical pain.
Human brain mapping
Oct 12, 2014
Recent reports of multivariate machine learning (ML) techniques have highlighted their potential use to detect prognostic and diagnostic markers of pain. However, applications to date have focussed on acute experimental nociceptive stimuli rather tha...
Automated diagnosis for extraction difficulty of maxillary and mandibular third molars and post-extraction complications using deep learning.
Scientific reports
May 30, 2025
Optimal surgical methods require accurate prediction of extraction difficulty and complications. Although various automated methods related to third molar (M3) extraction have been proposed, none fully predict both extraction difficulty and post-extr...
Robot Technology in Analyzing Tooth Removal - a Proof of Concept.
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Jul 1, 2020
A measurement setup is proposed that, for the first time, is capable of capturing the combination of high forces and subtle movements exerted during tooth removal procedures in high detail and in a reproducible manner by using robot technology. The o...
New approach for the diagnosis of extractions with neural network machine learning.
American journal of orthodontics and dentofacial orthopedics : official publication of the American Association of Orthodontists, its constituent societies, and the American Board of Orthodontics
Jan 1, 2016
INTRODUCTION: The decision to extract teeth for orthodontic treatment is important and difficult because it tends to be based on the practitioner's experiences. The purposes of this study were to construct an artificial intelligence expert system for...
Decision Making, Computer-Assisted
Nose
Lip
Nasal Bone
Orthodontics, Corrective
Incisor
Humans
Patient Care Planning
Neural Networks, Computer
Maxilla
Artificial Intelligence
Cephalometry
Machine Learning
Adult
Mandible
Algorithms
Tooth Extraction
Male
Expert Systems
Anatomic Landmarks
Female
Young Adult
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