☆ 4.6 Review

Advancements in oral and maxillofacial surgery medical images segmentation techniques: An overview

JOURNAL OF DENTISTRY (2023)

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WITS: Weakly-supervised individual tooth segmentation model trained on box-level labels

Ruicheng Xie et al.

Summary: This paper presents an automatic tooth segmentation model that combines deep learning methods and level-set approaches. The proposed model accurately detects the location and size of each tooth, generates prior ellipses, and employs level-set functions to separate and segment teeth. The performance of this model is more accurate and stable compared to classical level-set models and deep-learning models.

PATTERN RECOGNITION (2023)

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Article Dentistry, Oral Surgery & Medicine

Convolutional neural network-based automated maxillary alveolar bone segmentation on cone-beam computed tomography images

Rocharles Cavalcante Fontenele et al.

Summary: This study developed an AI-based CNN tool that can automatically segment the maxillary alveolar bone on CBCT images. The tool showed high accuracy and was 116 times faster than manual segmentation.

CLINICAL ORAL IMPLANTS RESEARCH (2023)

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Article Neurosciences

Ischemic stroke prediction of patients with carotid atherosclerotic stenosis via multi-modality fused network

Peng Lv et al.

Summary: The purpose of this study was to predict the symptoms of unknown patients by studying the symptoms of patients with carotid atherosclerotic stenosis. A deep neural network prediction model based on brain MRI imaging data was constructed and trained using multi-modality features as inputs and diagnosis incidence as output. A machine learning-based classification algorithm was developed to compare and evaluate clinical features. The experimental results showed that the deep learning model using imaging data could better predict the clinical symptom classification of patients. This study could assist in stroke prevention for patients with carotid atherosclerosis narrowing.

FRONTIERS IN NEUROSCIENCE (2023)

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Article Dentistry, Oral Surgery & Medicine

Automatic recognition of teeth and periodontal bone loss measurement in digital radiographs using deep-learning artificial intelligence

Chin-Chang Chen et al.

Summary: This study proposes a new deep learning ensemble model based on deep Convolutional Neural Network algorithms to predict tooth position, detect shape, detect remaining interproximal bone level, and detect radiographic bone loss using periapical and bitewing radiographs. The DL-trained ensemble model shows an accuracy of approximately 90% for periapical radiographs, and achieves high accuracy in tooth position detection, tooth shape detection, peri-odontal bone level detection, and radiographic bone loss detection. The AI model outperforms dentists in accuracy, indicating its strong potential to enhance clinical professional performance and build more efficient dental health services.

JOURNAL OF DENTAL SCIENCES (2023)

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Article Dentistry, Oral Surgery & Medicine

Development and validation of a novel artificial intelligence driven tool for accurate mandibular canal segmentation on CBCT

Pierre Lahoud et al.

Summary: This study developed and validated a novel AI-driven tool for fast and accurate mandibular canal segmentation on CBCT scans. Using a deep learning algorithm trained on 235 CBCT scans, the automated segmentation demonstrated high precision and speed, providing a potential solution to relieve practitioners from manual segmentation tasks.

JOURNAL OF DENTISTRY (2022)

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Review Dentistry, Oral Surgery & Medicine

Applications of artificial intelligence in dentistry: A comprehensive review

Francisco Carrillo-Perez et al.

Summary: This comprehensive review examines the use of artificial intelligence and machine learning in dentistry, focusing on deep learning, fuzzy logic, and other techniques applied to disease identification, image segmentation, image correction, and color analysis. The study highlights the potential for high-performance decision support systems and personalized treatments in digital dentistry, with an emphasis on improving the accuracy of dental restorations in esthetic dentistry through advanced modeling techniques.

JOURNAL OF ESTHETIC AND RESTORATIVE DENTISTRY (2022)

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Review Dentistry, Oral Surgery & Medicine

The need for communication between clinicians and pathologists in the context of oral and maxillofacial diseases

Maria Eduarda Perez-De-Oliveira et al.

Summary: Good communication between clinicians and pathologists is essential for the diagnostic process, but there is a lack of research on communication in oral and maxillofacial pathology. This narrative review highlights collaboration, clear reporting, communication skills training, and patient-centered care as strategies to improve communication quality. Further studies are needed to evaluate current practices in oral and maxillofacial pathology and identify barriers to optimal communication and patient safety.

BRAZILIAN ORAL RESEARCH (2022)

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Article Computer Science, Information Systems

Deep Segmentation of the Mandibular Canal: A New 3D Annotated Dataset of CBCT Volumes

Marco Cipriano et al.

Summary: This study describes a novel and publicly available CBCT dataset of the mandible, and improves the level of 3D mandibular canal segmentation using deep learning techniques. Researchers addressed the limitation of small maxillofacial datasets by providing 2D and 3D manual annotations.

IEEE ACCESS (2022)

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Article Multidisciplinary Sciences

A fully automatic AI system for tooth and alveolar bone segmentation from cone-beam CT images

Zhiming Cui et al.

Summary: This paper presents a deep learning system for efficient, precise, and fully automatic segmentation of dental cone-beam CT images. The system achieves comparable segmentation accuracy to experienced radiologists, while being 500 times faster. It consistently obtains accurate results on challenging cases with variable dental abnormalities, demonstrating its potential to boost clinical workflows of digital dentistry.

NATURE COMMUNICATIONS (2022)

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Article Computer Science, Information Systems

Tufts Dental Database: A Multimodal Panoramic X-Ray Dataset for Benchmarking Diagnostic Systems

Karen Panetta et al.

Summary: The application of Artificial Intelligence in dental healthcare has a very promising role, with expert analysis of X-ray images providing crucial clinical information. The Tufts Dental Database, containing 1000 panoramic dental radiography images, aims to help researchers incorporate human expertise into AI for improved abnormality detection accuracy. This dataset also includes eye-tracking and think-aloud protocol capturing the radiologist's expertise for further development in AI-powered dental care.

IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS (2022)

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Review Dentistry, Oral Surgery & Medicine

Global prevalence of cleft palate, cleft lip and cleft palate and lip: A comprehensive systematic review and meta-analysis

Nader Salari et al.

Summary: This study conducted a systematic review and meta-analysis to determine the overall prevalence of cleft palate, lip, and cleft lip and palate. The findings revealed a high prevalence of these oral clefts among infants. Health system policymakers need to implement measures to reduce the number of patients and mitigate the impact on children through preventive and therapeutic measures.

JOURNAL OF STOMATOLOGY ORAL AND MAXILLOFACIAL SURGERY (2022)

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Editorial Material Medicine, General & Internal

WHO calls to end the global crisis of oral health

Habib Benzian et al.

LANCET (2022)

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Dual-Stage Deeply Supervised Attention-Based Convolutional Neural Networks for Mandibular Canal Segmentation in CBCT Scans

Muhammad Usman et al.

Summary: This paper proposes a novel dual-stage deep learning-based scheme for the automatic segmentation of mandibular canals in lower jaws. By enhancing the visibility of CBCT scans and designing specific architectures, accurate segmentation of the mandibular canals has been achieved with good results.

SENSORS (2022)

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Roles of osteoclasts in alveolar bone remodeling

Maiko Omi et al.

Summary: Understanding the mechanisms of osteoclastic bone resorption is critical for the effective treatment of bone diseases in the jaw.

GENESIS (2022)

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Article Multidisciplinary Sciences

Improving performance of deep learning models using 3.5D U-Net via majority voting for tooth segmentation on cone beam computed tomography

Kang Hsu et al.

Summary: This study proposes a 3.5D U-Net model to improve the segmentation performance of teeth on CBCT. Majority voting and mathematical morphology operations can enhance the performance of U-Net. The 3.5Dv5 U-Net achieves the best segmentation performance among all models.

SCIENTIFIC REPORTS (2022)

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Article Multidisciplinary Sciences

Canal-Net for automatic and robust 3D segmentation of mandibular canals in CBCT images using a continuity-aware contextual network

Bo-Soung Jeoun et al.

Summary: This study proposes a continuity-aware contextual network (Canal-Net) for the automatic and robust 3D segmentation of the mandibular canal (MC) in cone-beam CT (CBCT) images. The Canal-Net incorporates spatio-temporal features and a multi-task learning framework to learn the anatomical context information and structural continuity of the MC. It demonstrates higher segmentation accuracies compared to other deep learning networks, achieving high consistent accuracy in 3D segmentations of the entire MC volume.

SCIENTIFIC REPORTS (2022)

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Article Computer Science, Information Systems

Mandibular Canal Segmentation From CBCT Image Using 3D Convolutional Neural Network With scSE Attention

Gang Du et al.

Summary: This paper proposes a mandibular canal segmentation method based on 3D convolutional neural network to improve the accuracy and efficiency of mandibular canal segmentation in dental implant planning. The proposed method combines different segmentation techniques and attention module to achieve good segmentation results, with potential practical applications in dental implants.

IEEE ACCESS (2022)

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Article Computer Science, Information Systems

Dental-YOLO: Alveolar Bone and Mandibular Canal Detection on Cone Beam Computed Tomography Images for Dental Implant Planning

Monica Widiasri et al.

Summary: This study proposes an automatic and simultaneous detection system based on 2D grayscale CBCT images for accurate measurement and planning of the position and size of the alveolar bone and mandibular canal. The detection using Dental-YOLO shows high accuracy and performance, which could be helpful for dental implant surgery and presurgical treatment planning.

IEEE ACCESS (2022)

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A Fully Automated Method for 3D Individual Tooth Identification and Segmentation in Dental CBCT

Tae Jun Jang et al.

Summary: This paper proposes a fully automated method for identifying and segmenting 3D individual teeth from CBCT images. The method utilizes deep learning-based hierarchical multi-step model to overcome the difficulty of separating an individual tooth from adjacent teeth and its surrounding alveolar bone. Experimental results show that the proposed method achieves high accuracy in tooth identification and segmentation.

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2022)

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Review Materials Science, Biomaterials

Hydrogels for the treatment of oral and maxillofacial diseases: current research, challenges, and future directions

Mingshu Huang et al.

Summary: Hydrogels, with their unique structure and biocompatibility, are widely used in the treatment of oral diseases and defect repair. They serve as an excellent drug-delivery system for oral mucosal diseases, wounds, periodontitis, and cancer therapy. Hydrogels also function as ideal scaffolds in regenerative engineering of dentin-pulp complex, periodontal tissue, bone, and cartilage.

BIOMATERIALS SCIENCE (2022)

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Review Dentistry, Oral Surgery & Medicine

Scope and performance of artificial intelligence technology in orthodontic diagnosis, treatment planning, and clinical decision-making - A systematic review

Sanjeev B. Khanagar et al.

Summary: Artificial intelligence technology has been widely applied in dentistry, particularly in orthodontics, and has shown promising results. These automated systems can simplify tasks, improve efficiency, and provide valuable assistance to dentists in orthodontic diagnosis and treatment planning.

JOURNAL OF DENTAL SCIENCES (2021)

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Review Dentistry, Oral Surgery & Medicine

Developments, application, and performance of artificial intelligence in dentistry - A systematic review

Sanjeev B. Khanagar et al.

Summary: Artificial intelligence (AI) has made significant advancements in dentistry, with various applications widely employed for diagnosis and prediction tasks, showing excellent performance and accuracy.

JOURNAL OF DENTAL SCIENCES (2021)

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Article Dentistry, Oral Surgery & Medicine

Evaluation of impacted canines' localization and adjacent lateral incisors' root resorption with orthopantomography and cone-beam computed tomography

Sibel Akkuc et al.

Summary: This retrospective study investigated the frequency and severity of ectopic eruption in children's permanent canines, looking at changes between impacted canine parameters on OPG and CBCT. It found a significant difference between the two imaging methods, with a recommendation for pediatric dentists to prefer OPG for early diagnosis of impacted canines and consider CBCT for detailed evaluation.

ORAL 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

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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TSegNet: An efficient and accurate tooth segmentation network on 3D dental model

Zhiming Cui et al.

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MEDICAL IMAGE ANALYSIS (2021)

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Article Dentistry, Oral Surgery & Medicine

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

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JOURNAL OF DENTISTRY (2021)

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Yue Zhao et al.

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PATTERN RECOGNITION LETTERS (2021)

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Review Health Care Sciences & Services

Automatic Segmentation of Mandible from Conventional Methods to Deep Learning-A Review

Bingjiang Qiu et al.

Summary: Medical imaging techniques, such as computed tomography and magnetic resonance imaging, are crucial in oral and maxillofacial surgery, but segmentation of the mandible remains challenging due to complex structures and high attenuation materials that cause noise and artifacts. With advancements in computer vision, automatic segmentation algorithms have been developed to tackle this issue.

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SCIENTIFIC REPORTS (2020)

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