Influence of dental fillings and tooth type on the performance of a novel artificial intelligence-driven tool for automatic tooth segmentation on CBCT images-A validation study

Article Dentistry, Oral Surgery & Medicine

Mapping the expression of beam hardening artefacts produced by metal posts positioned in different regions of the dental arch

Rocharles C. Fontenele et al.

Summary: The study results indicate that the expression and direction of artefacts produced in the vicinity of metal posts vary according to the mandibular region, composition of the post, and CBCT unit.

CLINICAL ORAL INVESTIGATIONS (2021)

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Article Dentistry, Oral Surgery & Medicine

Artificial intelligence-driven novel tool for tooth detection and segmentation on panoramic radiographs

Andre Ferreira Leite et al.

Summary: The study evaluated the performance of a new AI-driven tool for tooth detection and segmentation on panoramic radiographs, demonstrating highly accurate and fast results compared to manual segmentation. The AI tool showed a sensitivity of 98.9% and a precision of 99.6% for tooth detection, with lower canines presenting the best segmentation results. The method also significantly reduced the time consumed for manual segmentation by 67%.

CLINICAL ORAL INVESTIGATIONS (2021)

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Article Dentistry, Oral Surgery & Medicine

Refined tooth and pulp segmentation using U-Net in CBCT image

Wei Duan et al.

Summary: This study proposed a two-phase deep learning solution for accurately extracting and segmenting single teeth and pulp cavities, achieving high accuracy in experimental results.

DENTOMAXILLOFACIAL RADIOLOGY (2021)

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Review Dentistry, Oral Surgery & Medicine

Integration of imaging modalities in digital dental workflows - possibilities, limitations, and potential future developments

Sohaib Shujaat et al.

Summary: The digital workflow in dental specialties involves various steps, with imaging chain playing a crucial role. Errors accumulating at each step of the chain can have a negative impact on the final outcome. Integration of artificial intelligence strategies into the workflow is essential for simplifying, improving accuracy, and increasing efficiency.

DENTOMAXILLOFACIAL RADIOLOGY (2021)

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Article Dentistry, Oral Surgery & Medicine

Artificial Intelligence for Fast and Accurate 3-Dimensional Tooth Segmentation on Cone-beam Computed Tomography

Pierre Lahoud et al.

Summary: This study developed an AI-driven tool for automated tooth segmentation on CBCT imaging, demonstrating a unique fast and accurate approach that may open doors for AI-driven applications in surgical and treatment planning in oral health care.

JOURNAL OF ENDODONTICS (2021)

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Article Dentistry, Oral Surgery & Medicine

A novel deep learning system for multi-class tooth segmentation and classification on cone beam computed tomography. A validation study

Eman Shaheen et al.

Summary: This study successfully developed a deep learning AI framework for efficient and accurate automatic tooth segmentation and classification; validation results showed excellent performance of the AI framework with significantly faster segmentation speed compared to experts; the system has the potential for application in diagnostics and treatment planning in digital dentistry, reducing clinical workload.

JOURNAL OF DENTISTRY (2021)

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Article Dentistry, Oral Surgery & Medicine

Multiclass CBCT Image Segmentation for Orthodontics with Deep Learning

H. Wang et al.

Summary: This study trained and validated a mixed-scale dense (MS-D) convolutional neural network for multiclass segmentation of the jaw, teeth, and background in cone beam computed tomography (CBCT) scans. The results showed that the MS-D network demonstrated excellent segmentation performance, with large overlap with the gold standard segmentations and minor surface deviations compared to the gold standard 3D models.

JOURNAL OF DENTAL RESEARCH (2021)

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Article Dentistry, Oral Surgery & Medicine

Layered deep learning for automatic mandibular segmentation in cone-beam computed tomography

Pieter-Jan Verhelst et al.

Summary: The research developed and validated a layered deep learning algorithm that automatically creates 3D surface models of the human mandible from CBCT imaging, showing efficient and accurate results. The AI and RAI segmentation methods demonstrated better consistency than SA, with similar accuracy when benchmarked against the clinical standard.

JOURNAL OF DENTISTRY (2021)

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Article Dentistry, Oral Surgery & Medicine

Automatic segmentation of the pharyngeal airway space with convolutional neural network

Sohaib Shujaat et al.

Summary: The study proposed and investigated a 3D convolutional neural network model for automatic segmentation of the pharyngeal airway space. The CNN model showed high precision and recall in identifying the segmented region, with Newtom VGi evo CBCT device outperforming others. This method provides an accurate and time-efficient way to diagnose and treat upper airway problems.

JOURNAL OF DENTISTRY (2021)

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Article Computer Science, Information Systems