International journal of medical informatics
39626596
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...
OBJECTIVES: This study developed and evaluated a two-stage deep learning-based system for automatic segmentation of mandibular cortical bone, mandibular cancellous bone, maxillary cortical bone and maxillary cancellous bone on cone beam computed tomo...
OBJECTIVES: To investigate the reliability and time efficiency of the conventional compared to the automatic artificial intelligence (AI) segmentation of the mandibular canal and registration of the CBCT with the model scan data, in relation to clini...
OBJECTIVE: To develop and validate an artificial intelligence (AI)-driven tool for automated segmentation of the pulp cavity system of mandibular molars on cone-beam computed tomography (CBCT) images.
OBJECTIVES: During mandibular third molar (MTM) extraction surgery, preoperative analysis to quantify the proximity of the MTM to the surrounding inferior alveolar nerve (IAN) is essential to minimize the risk of IAN injury. This study aims to propos...
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...
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...
International journal of oral and maxillofacial surgery
39632213
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...
Zhonghua kou qiang yi xue za zhi = Zhonghua kouqiang yixue zazhi = Chinese journal of stomatology
39606979
To explore the clinical application pathway of the CT generative adversarial networks (CTGANs) algorithm in mandibular reconstruction surgery, aiming to provide a valuable reference for this procedure. A clinical exploratory study was conducted, 27...
OBJECTIVES: To develop and evaluate a deep learning (DL) model to reduce metal artefacts originating from the exomass in cone-beam CT (CBCT) of the jaws.