A5063197806- Dental Radiography and Imaging
- Advanced X-ray and CT Imaging
- Medical Imaging Techniques and Applications
- Medical Imaging and Analysis
- Radiomics and Machine Learning in Medical Imaging
- Dental Research and COVID-19
- Geophysical Methods and Applications
- Seismic Imaging and Inversion Techniques
- Dental Implant Techniques and Outcomes
- Tracheal and airway disorders
- Temporomandibular Joint Disorders
- Lung Cancer Diagnosis and Treatment
- Periodontal Regeneration and Treatments
- Obstructive Sleep Apnea Research
- COVID-19 diagnosis using AI
- Oral microbiology and periodontitis research
- AI in cancer detection
- Orthodontics and Dentofacial Orthopedics
- Advanced Neural Network Applications
- Nasal Surgery and Airway Studies
- Endodontics and Root Canal Treatments
Diagnologix (United States)
2022-2023
University of Manchester
2021
Near East University
2021
Ankara University
2021
Innopolis University
2017
The aim of this study was to evaluate the success artificial intelligence (AI) system in implant planning using three-dimensional cone-beam computed tomography (CBCT) images.Seventy-five CBCT images were included study. In these images, bone height and thickness 508 regions where implants required measured by a human observer with manual assessment method InvivoDental 6.0 (Anatomage Inc. San Jose, CA, USA). Also, canals/sinuses/fossae associated alveolar bones missing tooth detected....
Abstract In this study, a novel AI system based on deep learning methods was evaluated to determine its real-time performance of CBCT imaging diagnosis anatomical landmarks, pathologies, clinical effectiveness, and safety when used by dentists in setting. The consists 5 modules: ROI-localization-module (segmentation teeth jaws), tooth-localization numeration-module, periodontitis-module, caries-localization-module, periapical-lesion-localization-module. These modules use CNN state-of-the-art...
This study aims to generate and also validate an automatic detection algorithm for pharyngeal airway on CBCT data using AI software (Diagnocat) which will procure a measurement method. The second aim is the newly developed artificial intelligence system in comparison commercially available 3D evaluation. A Convolutional Neural Network-based machine learning was used segmentation of airways OSA non-OSA patients. Radiologists semi-automatic manually determine their measurements were compared...
The objective of this study was to evaluate the accuracy and effectiveness an artificial intelligence (AI) program in identifying dental conditions using panoramic radiographs (PRs), as well assess appropriateness its treatment recommendations.PRs from 100 patients (representing 4497 teeth) with known clinical examination findings were randomly selected a university database. Three dentomaxillofacial radiologists Diagnocat AI software evaluated these PRs. evaluations focused on various...
Abstract This retrospective study is aimed at developing a web‐based artificial intelligence (AI) software (DiagnoCat) for periodontal bone loss detection on panoramic radiographs and evaluating the model's performance by comparing it with clinicians' results. Separate models are trained tooth detection. The first objective was to detect teeth, segmenting their masks, define numbering developed Mask R‐CNN using pretrained ResNet‐101 as backbone. second model based Cascade architecture used...
Bone suppression in lung radiographs is an important task, as it improves the results on other related tasks, such nodule detection or pathologies classification. In this paper, we propose two architectures that suppress bones by treating them noise. proposed methods, create end-to-end learning frameworks minimize noise images while maintaining sharpness and detail them. Our show our noise-cancellation scheme robust does not introduce artifacts into images.
This study aims to investigate the effect of using an artificial intelligence (AI) system (Diagnocat, Inc., San Francisco, CA, USA) for caries detection by comparing cone-beam computed tomography (CBCT) evaluation results with and without software. 500 CBCT volumes are scored three dentomaxillofacial radiologists presence separately on a five-point confidence scale aid AI system. After visual evaluation, deep convolutional neural network (CNN) model generated radiological report observers...
We consider the problem of localizing and segmenting individual teeth inside 3D Cone-Beam Computed Tomography (CBCT) images. To handle large image sizes we approach this task with a coarse-to-fine framework, where whole volume is first analyzed as 33-class semantic segmentation (adults have up to 32 teeth) in coarse resolution, followed by binary cropped region interest original resolution. improve performance challenging segmentation, train Coarse step model on weakly labeled dataset, then...
This study aims to evaluate the reliability of AI-generated STL files in diagnosing osseous changes mandibular condyle and compare them a ground truth (GT) diagnosis made by six radiologists.
Abstract Background: The aim of this study was to evaluate the success artificial intelligence (AI) system in implant planning using three-dimensional cone-beam computed tomography (CBCT) images. Methods: Seventy-five CBCT images were included study. In these images, bone height and thickness 508 regions where implants required measured by a human observer with manual segmentation method InvivoDental 6.0 (Anatomage Inc. San Jose, CA, USA). Also, canals/sinuses/fossae associated alveolar...
Abstract This study aims to investigate the effect of using an artificial intelligence (AI) system (Diagnocat, Inc., San Francisco, USA) for caries detection, by comparing cone-beam computed tomography (CBCT) evaluation results with and without software. 500 CBCT volumes are scored three dentomaxillofacial radiologists presence separately on a five-point confidence scale aid AI system. After visual evaluation, deep convolutional neural network model generated radiological report observers...
Cone-beam computed tomography (CBCT) is a valuable imaging method in dental diagnostics that provides information not available traditional 2D imaging. However, interpretation of CBCT images time-consuming process requires physician to work with complicated software. In this we propose an automated pipeline composed several deep convolutional neural networks and algorithmic heuristics. Our task two-fold: a) find locations each present tooth inside 3D image volume, b) detect common conditions...
Abstract Cone-beam computed tomography (CBCT) in dental practice is becoming increasingly popular. However, the correct teeth identification, positioning and diagnosis based on CBCT can be tedious challenging for untrained eye. This due to additional training, specific knowledge time required analysis diagnosis. When compared conventional imaging methods. In this study, we introduce a novel artificial intelligence (AI) system that facilitates deep learning approaches localize define...
We consider the problem of localizing and segmenting individual teeth inside 3D Cone-Beam Computed Tomography (CBCT) images. To handle large image sizes we approach this task with a coarse-to-fine framework, where whole volume is first analyzed as 33-class semantic segmentation (adults have up to 32 teeth) in coarse resolution, followed by binary cropped region interest original resolution. improve performance challenging segmentation, train Coarse step model on weakly labeled dataset, then...