This dataset includes dental OPG X-rays collected from three different dental clinics. This dataset can be used for tasks like object detection, image analysis, disease classification, and segmentation. It has two folders: the object detection dataset folder and the classification dataset folder. The object detection folder contains 232 original and 604 augmented images and labels. The classification folder contains six distinct files for each class. The images are in JPG format, and the labels are in JSON format. The augmented data is split into training, validation, and testing sets in an 80:10:10 ratio.

Dataset collection: • Source: Prescription Point Ltd, Lab Aid Specialized Hospital, Ibn Sina Diagnostic and Imaging Center. • Capture Method: Using android phone camera. • Anonymization: All data were rigorously anonymized to maintain confidentiality and privacy. • Informed Consent: All patients provided their consent in accordance with the dental ethical principles.

Dataset composition: • Total Participants: 232 Male and female patients aged 10 years or older.

Variables: • Healthy Teeth: 223 • Caries: 119 • Impacted Teeth: 87 • Broken Down Crown/ Root: 52 • Infection: 23 • Fractured Teeth: 13

DENTEX CHALLENGE

We present the Dental Enumeration and Diagnosis on Panoramic X-rays Challenge (DENTEX), organized in conjunction with the International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI) in 2023. The primary objective of this challenge is to develop algorithms that can accurately detect abnormal teeth with dental enumeration and associated diagnosis. This not only aids in accurate treatment planning but also helps practitioners carry out procedures with a low margin of error.

The challenge provides three types of hierarchically annotated data and additional unlabeled X-rays for optional pre-training. The annotation of the data is structured using the Fédération Dentaire Internationale (FDI) system. The first set of data is partially labeled because it only includes quadrant information. The second set of data is also partially labeled but contains additional enumeration information along with the quadrant. The third data is fully labeled because it includes all quadrant-enumeration-diagnosis information for each abnormal tooth, and all participant algorithms will be benchmarked on the third data.

DENTEX aims to provide insights into the effectiveness of AI in dental radiology analysis and its potential to improve dental practice by comparing frameworks that simultaneously point out abnormal teeth with dental enumeration and associated diagnosis on panoramic dental X-rays.

Please visit our website to join DENTEX (Dental Enumeration and Diagnosis on Panoramic X- rays Challenge) which is held at MICCAI2023.

DATA

The DENTEX dataset comprises panoramic dental X-rays obtained from three different institutions using standard clinical conditions but varying equipment and imaging protocols, resulting in diverse image quality reflecting heterogeneous clinical practice. The dataset includes X-rays from patients aged 12 and above, randomly selected from the hospital's database to ensure patient privacy and confidentiality.

To enable effective use of the FDI system, the dataset is hierarchically organized into three types of data;

• (a) 693 X-rays labeled for quadrant detection and quadrant classes only,

• (b) 634 X-rays labeled for tooth detection with quadrant and tooth enumeration classes,

• (c) 1005 X-rays fully labeled for abnormal tooth detection with quadrant, tooth enumeration, and diagnosis classes.

The diagnosis class includes four specific categories: caries, deep caries, periapical lesions, and impacted teeth. An additional 1571 unlabeled X-rays are provided for pre-training.

• The dataset is used for DENTEX (Dental Enumeration and Diagnosis on Panoramic X-rays Challenge).

Data Split for Evaluation and Training

The DENTEX 2023 dataset comprises three types of data: (a) partially annotated quadrant data, (b) partially annotated quadrant-enumeration data, and (c) fully annotated quadrant-enumeration-diagnosis data. The first two types of data are intended for training and development purposes, while the third type is used for training and evaluations.

To comply with standard machine learning practices, the fully annotated third dataset, consisting of 1005 panoramic X-rays, is partitioned into training, validation, and testing subsets, comprising 705, 50, and 250 images, respectively. Ground truth labels are provided only for the training data, while the validation data is provided without associated ground truth, and the testing data is kept hidden from participants.

Annotation Protocol

The DENTEX provides three hierarchically annotated datasets that facilitate various dental detection tasks: (1) quadrant-only for quadrant detection, (2) quadrant-enumeration for tooth detection, and (3) quadrant-enumeration-diagnosis for abnormal tooth detection. Although it may seem redundant to provide a quadrant detection dataset, it is crucial for utilizing the FDI Numbering System. The FDI system is a globally-used system that assigns each quadrant of the mouth a number from 1 through 4. The top right is 1, the top left is 2, the bottom left is 3, and the bottom right is 4. Then each of the eight teeth and each molar are numbered 1 through 8. The 1 starts at the front middle tooth, and the numbers rise the farther back we go. So for example, the back tooth on the lower left side would be 48 according to FDI notation, which means quadrant 4, number 8. Therefore, the quadrant segmentation dataset can significantly simplify the dental enumeration task, even though evaluations will be made only on the fully annotated third data.

Note: The datasets are fully identical to the data used for our baseline method named as HierarchicalDet. Therefore, please visit HierarchicalDet (Diffusion-Based Hierarchical Multi-Label Object Detection to Analyze Panoramic Dental X-rays) repo for more info.

CITING US

If you use DENTEX, we would appreciate references to the following papers.

1. Ibrahim Ethem Hamamci, Sezgin Er, Enis Simsar, Anjany Sekuboyina, Mustafa Gundogar, Bernd Stadlinger, Albert Mehl, Bjoern Menze., Diffusion-Based Hierarchical Multi-Label Object Detection to Analyze Panoramic Dental X-rays, 2023.
   Pre-print: https://arxiv.org/abs/2303.06500

2. Hamamci, I., Er, S., Simsar, E., Yuksel, A., Gultekin, S., Ozdemir, S., Yang, K., Li, H., Pati, S., Stadlinger, B., & others (2023). DENTEX: An Abnormal Tooth Detection with Dental Enumeration and Diagnosis Benchmark for Panoramic X-rays.
   Pre-print: https://arxiv.org/abs/2305.19112

Intraoral 3D scans analysis is a fundamental aspect of Computer-Aided Dentistry (CAD) systems, playing a crucial role in various dental applications, including teeth segmentation, detection, labeling, and dental landmark identification. Accurate analysis of 3D dental scans is essential for orthodontic and prosthetic treatment planning, as it enables automated processing and reduces the need for manual adjustments by dental professionals. However, developing robust automated tools for these tasks remains a significant challenge due to the limited availability of high-quality public datasets and benchmarks. This article introduces Teeth3DS+, the first comprehensive public benchmark designed to advance the field of intraoral 3D scan analysis. Developed as part of the 3DTeethSeg 2022 and 3DTeethLand 2024 MICCAI challenges, Teeth3DS+ aims to drive research in teeth identification, segmentation, labeling, 3D modeling, and dental landmarks identification. The dataset includes at least 1,800 intraoral scans (containing 23,999 annotated teeth) collected from 900 patients, covering both upper and lower jaws separately. All data have been acquired and validated by experienced orthodontists and dental surgeons with over five years of expertise. Detailed instructions for accessing the dataset are available at https://crns-smartvision.github.io/teeth3ds

Computer-aided design (CAD) tools have become increasingly popular in modern dentistry for highly accurate treatment planning. In particular, in orthodontic CAD systems, advanced intraoral scanners (IOSs) are now widely used as they provide precise digital surface models of the dentition. Such models can dramatically help dentists simulate teeth extraction, move, deletion, and rearrangement and ease therefore the prediction of treatment outcomes. Hence, digital teeth models have the potential to release dentists from otherwise tedious and time consuming tasks. Although IOSs are becoming widespread in clinical dental practice, there are only few contributions on teeth segmentation/labelling available in the literature [1,2,3] and no publicly available database. A fundamental issue that appears with IOS data is the ability to reliably segment and identify teeth in scanned observations. Teeth segmentation and labelling is difficult as a result of the inherent similarities between teeth shapes as well as their ambiguous positions on jaws. In addition, it faces several challenges: 1- The teeth position and shape variation across subjects. 2- The presence of abnormalities in dentition. For example, teeth crowding which results in teeth misalignment and thus non-explicit boundaries between neighboring teeth. Moreover, lacking teeth and holes are commonly seen among people. 3- Damaged teeth. 4- The presence of braces, and other dental equipment. The challenge we propose will particularly focus on point 1, i.e. the teeth position and shape variation across subjects. With the extension of available data in the mid and long term, the other points will also be addressed in further editions of the challenge. [1] Lian, Chunfeng, et al. "MeshSNet: Deep multi-scale mesh feature learning for end-to-end tooth labeling on 3D dental surfaces." International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, Cham, 2019. [2] Xu, Xiaojie, Chang Liu, and Youyi Zheng. "3D tooth segmentation and labeling using deep convolutional neural networks." IEEE transactions on visualization and computer graphics 25.7 (2018): 2336-2348. [3] Sun, Diya, et al. "Automatic Tooth Segmentation and Dense Correspondence of 3D Dental Model." International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, Cham, 2020.

The anterior tooth length data from Eocene fossil sharks are labeled as the following columns: "Red_Hot" is the Red Hot Truck Stop locality of the Bashi/Tuscahoma Formations (Fm); Whiskey_Bridge is the Whiskey Bridge locality in the Stone City Member of the Crockett Fm.; Banks is Banks Island in the Cyclic Member of the Eureka Sound Fm. in the Arctic; Seymour is Seymour Island in the La Meseta Fm. of Antartica. The modern data from Delaware Bay takes total length data from the 2012 Delaware State and University of Delaware shark tagging program and transforms it to labial measurements of anterior tooth crown height (shown as column "modern_DE").

Introduction

The dataset consists of 1488 orthopantomogram (OPG) images collected from a dental clinic in India. The images were taken using a digital OPG machine, which captures a panoramic view of the patient's teeth and jaws. The dataset does not contain any personal information about the patients, as it has been deleted for privacy reasons. Additionally, the data does not have any labels or annotations.

Data Collection

The OPG images were collected over a period of six months from patients who visited the dental clinic for routine check-ups or specific dental problems. The images were captured using a digital OPG machine that uses X-rays to create a panoramic view of the patient's teeth and jaws. The machine is designed to capture high-quality images with minimal radiation exposure to the patient.

Data Preprocessing

Before being added to the dataset, each OPG image was checked for quality and clarity. Images that were blurry or had artifacts were removed from the dataset. Additionally, all personal information about the patients was deleted from the images to ensure privacy.

Data Description

The dataset contains 1488 OPG images in PNG format. Each image has a resolution of 2400 x 1200 pixels and is grayscale. The size of each image ranges from 900 KB to 1.1 MB, depending on its complexity.

The OPG images in this dataset show different types of dental problems such as cavities, gum disease, impacted teeth, and fractures. Some images also show normal healthy teeth and jaws.

Snapshot img

Dental Cleansing Tablet Market Size 2025-2029

The dental cleansing tablet market size is forecast to increase by USD 652.4 million at a CAGR of 7.4% between 2024 and 2029.

The Dental Cleansing Tablets market is witnessing significant growth, driven by the increasing trend towards product innovation and portfolio extension. Companies are investing heavily in research and development to launch new and improved dental cleansing tablet offerings, catering to diverse consumer preferences and oral health needs. Another key trend influencing the market is the rising popularity of private-label brands. Consumers are increasingly opting for cost-effective and high-quality private-label dental cleansing tablets, posing a challenge for established brands. Additionally, the use of oral care substitutes, such as natural and organic alternatives, is on the rise. This shift towards more natural and sustainable oral care solutions presents both opportunities and challenges for market participants.
Companies can capitalize on this trend by offering eco-friendly and effective dental cleansing tablets, while also addressing the need for consistent quality and affordability. However, navigating the competitive landscape and ensuring regulatory compliance will be crucial for market success. Overall, the Dental Cleansing Tablets market is poised for growth, with companies that prioritize innovation, sustainability, and consumer-centricity well-positioned to capture market share. Ethical sourcing and saliva stimulation are also essential for maintaining oral hygiene.

What will be the Size of the Dental Cleansing Tablet Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
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The market is experiencing significant activity and trends, driven by growing concerns over tooth decay and periodontal disease. These conditions are linked to biofilm formation, enamel erosion, and gum recession. Hydrogen peroxide and baking soda are popular ingredients for cleansing tablets due to their cavity prevention properties. However, environmental impact is a rising concern, leading to the adoption of sustainable dentistry practices. Sustainable options include dental sealants made from biocompatible materials and green packaging.
Dental floss picks and dental calculus removal are other areas of focus, with anti-microbial agents and essential oils providing added benefits. Dry mouth is a common issue, leading to the use of anti-microbial agents and saliva stimulation agents in dental products. Overall, the market is dynamic, with a focus on oral health education, cavity prevention, and sustainable practices. Anti-inflammatory agents, essential oils, and tongue cleaners are additional ingredients gaining popularity. Antibiotic resistance poses a significant threat, necessitating the development of alternative infection control strategies.

How is this Dental Cleansing Tablet Industry segmented?

The dental cleansing tablet industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Distribution Channel

  Offline
  Online


Product

  Denture cleansing tablet
  Toothpaste tablet


Product Type

  Whitening tablet
  Effervescent tablets
  Antibacterial tablets
  Fluoride tablets
  Others


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    Italy
    UK


  APAC

    China
    India
    Japan


  South America

    Brazil


  Rest of World (ROW)

By Distribution Channel Insights

The offline segment is estimated to witness significant growth during the forecast period. The global dental cleansing tablets market is driven by the growing emphasis on preventative oral care and consumer preferences for convenient, portable solutions. Quality control measures ensure effective fluoride delivery and address the needs of sensitive teeth and gum health. Price sensitivity remains a significant factor, leading to increased online shopping and retail sales. Social media engagement and digital marketing strategies are essential for brand loyalty and reaching consumers. Emerging technologies, such as artificial intelligence and oral cancer screening, are transforming oral health products. Oral care professionals and dental clinics continue to recommend single-dose packets for their ease of use and effectiveness in plaque removal and tartar control.

Clinical trials and dental hygienists utilize X-ray imaging and diagnostic tools to assess dental care routines and identify potential issues. Pharmaceutical development and ingredient sourcing prioritize natural ingredients and sugar reduction, while antibacterial properties rem

Data from: Impact of explainable artificial intelligence assistance on clinical decision making of novice dental clinicians

https://doi.org/10.5061/dryad.j0zpc86h6

Description of the data and file structure

File: DryadData.csv

Description: Data category description presented in row 2. All time in seconds.

This labelling standard describes the requirements necessary to receive market authorization (a Drug Identification Number (DIN)) for oral stool softener laxative non-prescription products containing docusate sodium or docusate calcium as a single ingredient for use in adults and children 6 years of age and older to relieve occasional constipation.

Performance of the model on validation datasets for classifying the primary molars based on the pulp involvement.

This labelling standard describes the requirements necessary to receive marketing authorization (Drug identification number (DIN)) for oral expectorant nonprescription product containing guaifenesin as a single ingredient for use in adults and children 12 years of age and older to relieve symptoms of common cold.

Vivli is an independent, non-profit organization that has developed a global data-sharing and analytics platform to serve all elements of the international research community. Our mission is to promote, coordinate, and facilitate scientific sharing and reuse of clinical research data through the creation and implementation of a sustainable global data-sharing enterprise. The Vivli platform includes an independent data repository, in-depth search engine and a cloud-based, secure analytics platform.

The purpose of this study is to evaluate the bioequivalence of a single oral administration of a vortioxetine (Lu AA21004) 20 mg tablet in comparison with two of vortioxetine 10 mg tablets in Japanese healthy adult participants.

This Labelling Standard describes the requirements necessary to receive marketing authorization (a Drug Identification Number (DIN)) for single and multiple-ingredient orally administered nonprescription paediatric products for use in children 6 to under 12 years of age to provide relief of symptoms associated with the common cold.

Performance of the model on a new set of images for classifying the primary molars based on the pulp involvement.

The purpose of this study is to evaluate the bio-equivalence of a single oral administration of TAK-536 pediatric formulation (granules) in comparison with a TAK-536 commercial formulation (tablet) in Japanese healthy adult male participants in an open label, 2-period, 2-treatment, cross-over design.

Oral squamous cell carcinoma (OSCC) is a prevalent type of head and neck cancer, comprising over 90% of all oral malignancies worldwide. The identification of diagnostic and prognostic markers for OSCC is crucial for improving patient outcomes, as early detection and treatment are critical for the successful management of this disease. Previously, we demonstrated that N-myc downstream-regulated gene 1 (NDRG1) and phosphoglycerate kinase 1 (PGK1) are prognostic markers for OSCC; however, their role in OSCC development remains unclear. To investigate this, we used TurboID-based proximity labeling to identify the interactomes of NDRG1 and PGK1 in HEK293 cells. Herein, protein abundance patterns from three time points were used for clustering 364 proteins with a “fast” or “slow” response to biotin. Of these, 65 proteins were also identified in neoplastic islands of OSCC patients from our previous study, and 28 of these proteins have their gene expression associated with prognostic features, including death, metastasis, and relapse. PRM-MS enabled the quantification of 17 of these proteins, providing further evidence of their presence in the OSCC prognostic interactome. Finally, we characterized a prognostic-associated interactome composed of 28 proteins, which enabled the prioritization of candidates that can be further explored in OSCC progression. The mass spectrometry data generated in this study have been deposited in ProteomeXchange with the data set identifier PXD048046.

This labelling standard describes the requirements necessary to receive marketing authorization (a Drug Identification Number (DIN)) for oral decongestant non-prescription products containing phenylephrine hydrochloride as a single ingredient for use in adults and children 12 years of age and older.

This Labelling Standard describes the requirements necessary to receive marketing authorization (a Drug Identification Number (DIN)) for single and multiple-ingredient orally administered nonprescription paediatric products for use in children 6 to under 12 years of age to provide relief of symptoms associated with the common cold.

This labelling standard describes the requirements necessary to receive market authorization (a Drug Identification Number (DIN)) for non-prescription oral antitussive products containing dextromethorphan or dextromethorphan hydrobromide as a single ingredient for use in adults and children 12 years of age and older to relieve symptoms of the common cold.