AIMC Topic: Radiography, Panoramic
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Do machine learning methods solve the main pitfall of linear regression in dental age estimation?
Forensic science international
Dec 20, 2024
INTRODUCTION: Age estimation is crucial in forensic and anthropological fields. Teeth, are valued for their resilience to environmental factors and their preservation over time, making them essential for age estimation when other skeletal remains det...
Automated segmentation of dental restorations using deep learning: exploring data augmentation techniques.
Oral radiology
Dec 9, 2024
OBJECTIVES: Deep learning has revolutionized image analysis for dentistry. Automated segmentation of dental radiographs is of great importance towards digital dentistry. The performance of deep learning models heavily relies on the quality and divers...
Deep Learning Architecture to Infer Kennedy Classification of Partially Edentulous Arches Using Object Detection Techniques and Piecewise Annotations.
International dental journal
Dec 6, 2024
OBJECTIVES: Dental health is integral to overall well-being, with early detection of issues critical for prevention. This research work focuses on utilizing artificial intelligence and deep learning-based object detection techniques for automated det...
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...
Development and validation of a deep learning algorithm for the classification of the level of surgical difficulty in impacted mandibular third molar surgery.
International journal of oral and maxillofacial surgery
Dec 4, 2024
The aim of this study was to develop and validate a convolutional neural network (CNN) algorithm for the detection of impacted mandibular third molars in panoramic radiographs and the classification of the surgical extraction difficulty level. A data...
DEEP LEARNING-DRIVEN SEGMENTATION OF DENTAL IMPLANTS AND PERI-IMPLANTITIS DETECTION IN ORTHOPANTOMOGRAPHS: A NOVEL DIAGNOSTIC TOOL.
The journal of evidence-based dental practice
Nov 26, 2024
INTRODUCTION AND OBJECTIVE: Dental implants are well-established for restoring partial or complete tooth loss, with osseointegration being essential for their long-term success. Peri-implantitis, marked by inflammation and bone loss, compromises impl...
An AI-assisted explainable mTMCNN architecture for detection of mandibular third molar presence from panoramic radiography.
International journal of medical informatics
Nov 23, 2024
OBJECTIVE: This study aimed to design and systematically evaluate an architecture, proposed as the Explainable Mandibular Third Molar Convolutional Neural Network (E-mTMCNN), for detecting the presence of mandibular third molars (m-M3) in panoramic r...
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...
ResNet-Transformer deep learning model-aided detection of dens evaginatus.
International journal of paediatric dentistry
Nov 15, 2024
BACKGROUND: Dens evaginatus is a dental morphological developmental anomaly. Failing to detect it may lead to tubercles fracture and pulpal/periapical disease. Consequently, early detection and intervention of dens evaginatus are significant to prese...
A two-stage deep-learning model for determination of the contact of mandibular third molars with the mandibular canal on panoramic radiographs.
BMC oral health
Nov 13, 2024
OBJECTIVES: This study aimed to assess the accuracy of a two-stage deep learning (DL) model for (1)Â detecting mandibular third molars (MTMs) and the mandibular canal (MC), and (2)Â classifying the anatomical relationship between these structures (cont...
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