Abstract

The MarketScan Dental Database is a standalone product that corresponds with and is linkable to a given year and version of the IBM MarketScan Commercial Claims and Encounters Database and the MarketScan Medicare Supplemental and Coordination of Benefits Database. Currently, data is available for the years: 2005 - 2023. In order to view the MarketScan Dental user guide or data dictionary, you must have data access to this dataset.

Usage

In addition to what's on this page, we also have:

• The MarketScan Commercial Database
• The MarketScan Medicare Supplemental Database
• The MarketScan Benefit Plan Design data

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• MarketScan Redbook (The MarketScan Code Reference Book)

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**Starting in 2026, there will be a data access fee for using the full dataset **(though the 1% sample will remain free to use). The pricing structure and other **relevant information can be found in this **FAQ Sheet.

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Data Documentation

Data access is required to view this section.

Section 3

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Section 4

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Section 5

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Section 6

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ODSI-DB is an image database of oral and dental reflectance spectral images of human test subjects. Image sets of the test subjects contain the front-view and the occlusal surfaces of lower and upper teeth, oral mucosa, and face surrounding the mouth. Other features-of-interest have been imaged on case-by-case basis. The spectral images in the database have been annotated by dental experts.

With healthcare database selections covering NHS Management, Doctors, General Practice, Dentists, Care Homes and Private Hospitals, contact lists can be purchased by job role, seniority level, size and region.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F17784270%2Fbd748df17d668d8707473f63f33e4ee9%2FKaggle%20Dataset.png?generation=1731144181999827&alt=media" alt=""> The Dental Cavity Detection Dataset consists of 418 annotated dental images designed to support research and development in automated dental diagnostics. Each image in the dataset is labeled for cavities using the YOLOv5 Oriented Object Detection format, ensuring compatibility with modern object detection frameworks.

This dataset aims to facilitate the training and evaluation of machine learning models for precise detection and localization of cavities within dental X-rays. It is ideal for researchers, data scientists, and developers focusing on dental health, computer vision, and medical imaging applications.

The dataset can contribute significantly to improving the accuracy of AI-driven dental diagnostic tools, potentially assisting dentists in faster and more reliable cavity detection.

World’s first dataset of children’s dental panoramic radiographs for caries segmentation and dental disease detection by segmenting and detecting annotations is published in this dataset.

There are three folders: i) adult tooth segmentation (semantic segmentation), ii) children's dental caries segmentation (instance segmentation), and iii) pediatric dental disease detection (object detection).

A pipeline with Mask-RCNN for the second dataset children's dental caries is created in the notebooks section for being a guide (EDA, Training, Inference).

Dental data is pretty important, however, the importance of this field is not sufficiently appreciated.

Paper: https://www.nature.com/articles/s41597-023-02237-5

Fossil relative abundance data in combination with specimen-level measurements can reveal community-level changes underpinning long-term ecological and evolutionary dynamics. However relative abundance data is not consistently reported in the paleontological literature, and measurement data is presented in individual studies in varying formats. Here, we compiled dental measurements (tooth crown length, width, and height) from the paleontological literature (or otherwise available sources) in a single database, simultaneously permitting analyses of size and relative abundance for the fossil record. This first version of the database focuses on large mammals and on the African and Arabian Neogene fossil record. Measurements of teeth of extant species were also collected. Each row gives the available measurements for a single tooth. Specimens with multiple teeth reported are divided among several rows. Taxonomic information was entered largely as given in the original source, without stand...

In the rapidly evolving field of dental intelligent healthcare, where Artificial Intelligence (AI) plays a pivotal role, the demand for multimodal datasets is critical. Existing public datasets are primarily composed of single-modal data, predominantly dental radiographs or scans, which limits the development of AI-driven applications for intelligent dental treatment. In this paper, we collect a MultiModal Dental (MMDental) dataset to address this gap. MMDental comprises data from 660 patients, including 3D Cone-beam Computed Tomography (CBCT) images and corresponding detailed expert medical records with initial diagnoses and follow-up documentation. All CBCT scans are conducted under the guidance of professional physicians, and all patient records are reviewed by senior doctors. To the best of our knowledge, this is the first and largest dataset containing 3D CBCT images of teeth with corresponding medical records. Furthermore, we provide a comprehensive analysis of the dataset by exploring patient demographics, prevalence of various dental conditions, and the disease distribution across age groups. We believe this work will be beneficial for further advancements in dental intelligent treatment.

Dentists’ data is compiled by the Department of Health as part of the Non-Monetary Health Care Statistics, administered jointly by Eurostat, OECD and WHO in fulfilment of the European regulation (EU) 2022/2294. These statistics are compiled and published on an annual basis and refer to the number of practicing dentists and dentists licensed to practice in the Republic of Ireland, as at end of the referenced ending calendar year. .hidden { display: none }

This database contains 598 panoramic radiographs, whose dimensions are 2041 x 1024 and are in a JPEG format.
These images were acquired with Owandy I-max Touch panoramic radiography equipment belonging to the Departamento de Radiología de la Facultad de Odontología, Universidad Nacional de Asunción, located in Asuncion, Paraguay.

If you use the dataset, please cite the paper:

Román, J.C.M.; Fretes, V.R.; Adorno, C.G.; Silva, R.G.; Noguera, J.L.V.; Legal-Ayala, H.; Mello-Román, J.D.; Torres, R.D.E.; Facon, J. Panoramic Dental Radiography Image Enhancement Using Multiscale Mathematical Morphology. Sensors 2021, 21, 3110. https://doi.org/10.3390/s21093110

This dataset provides beneficiary and service counts for annual dental visits, dental preventive services, dental treatment, and dental exams by rendering providers (by NPI) for calendar year (CY) 2020. It includes Fee-For-Service (FFS), Geographic Managed Care (GMC), and Pre-Paid Health (PHP) Plans delivery systems. Rendering providers are categorized as either rendering or rendering at a safety net clinic. Beneficiaries are grouped by Age 0-20 and Age 21+.

Salary and employment data for associate dentist dentists

Dental Encounter System (DES) is an automated health care application designed to capture critical data about the operations of VA Dental Services. Information on completed procedures is gathered for each patient encounter through the Dental Record Manager software, which is a VA Computerized Patient Record System Class I adjunct application. This DES information is linked and includes encounter date, patient, provider, procedures, diagnosis codes, and additional associated qualifiers. DES productivity is measured using weighted work units known as relative value units (RVUs). RVU values are determined cooperatively by the Dental Coding Committee and Decision Support System (DSS) staff. One RVU is a measure of work effort and represents one minute of the average provider's time (presuming the provider is supported with one dental assistant and one treatment room) and is reported as a value with each procedure reported to DES. DES transactions are sent to the Austin Information Technology Center (AITC) database using standard Health Level Seven (HL7) messaging and the VA Vitria Interface Engine. These batched HL7 messages are parsed at the AITC and placed in a flat database file.

This dataset shows the use of Medi-Cal dental benefits by Medi-Cal members displayed by county, age groups, and calendar year (for 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, and 2023). This information consists of members receiving dental services in the following categories: Annual Dental Visit, Exams/Oral Health Evaluations, Use of Diagnostic Services, Use of Dental Treatment Services, Use of Preventive Services, Use of Restorative Services, Treatment/Prevention for Caries, and Use of Sealants. The Medi-Cal members were continuously enrolled in either Dental Managed Care or the Dental Fee-for-Service delivery system for at least 3 months during each measure year.

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

Dentist email list is a list of dentist email addresses. It helps you reach dentists fast. You can use it to share news, tools, or offers. A dentist, known as a doctor of dental medicine or doctor of dental surgery, is a medical professional who specializes in the diagnosis, prevention, and treatment of diseases and conditions of the oral cavity. This datas may have names, emails, clinic names, and job titles. It can also show where they work and what they do. Some are general dentists. Others fix braces or do surgery. Many businesses use this list. Dental tool companies use it to show new products. Clinics use it to find new staff. Schools use it to send training info.

Dentist email list saves time. It helps grow your business. It builds trust and connections. You can buy the dataset from List to Data. Our dataset is accurate and active. They are checked and updated often. This helps avoid wrong emails. Good package follows privacy rules. Dentists must agree to get emails. This is called “opt-in.” If not, it can break the law. Making a position takes time and uses the leds. Some people use software to help. Some companies sell big lists. Others offer small, local ones. Always write clear and short emails, and add useful info. Do not spam respect the dentist’s time. In short, dentist email address is a smart tool. It helps reach the right people. Dentist email database will make your business more profitable through digital campaigns. If any business person wants to get more profit, they have to buy contact number. Besides, we assure you that our service cost is more reasonable. This directory will help to do online marketing in B2B and B2C platforms. We guarantee 95% accuracy of the lead because we update it regularly. Lastly, people get an Excel format or CSV that can operate in CRM software easily.

Comprehensive salary and practice data for private practice dentists from 43 verified submissions

The 2009 Adult Dental Health Survey (ADHS) is the fifth in a series of national dental surveys that have been carried out every decade since 1968. The main purpose of these surveys has been to get a picture of the dental health of the adult population and how this has changed over time. The tables in the report are available to download in Excel format in the ADHS tables in Excel format.zip file

Comprehensive salary and practice data for corporate dental (dso) dentists from 16 verified submissions

This data set shows the use of Medi-Cal dental benefits by Medi-Cal members displayed by county, age group, and calendar year (for 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, and 2022). This information is displayed in the following categories: Continuity Care oral evaluation or a prophylaxis, Usual Source of Care, Overall Utilization of Dental Services 1 year, 2 years and 3 years. The Medi-Cal members were continuously enrolled in either Dental Managed Care or the dental Fee-for-Service delivery system for the entire measurement period.

This annual report brings together information on NHS dental activity in England for the 12-month period to 31 March 2023 (including clinical treatments, orthodontic activity, patient eligibility and charges, and dental workforce) and information on the number of patients seen by an NHS dentist up to 30 June 2023. Data is also provided for the previous 4 financial years for comparison. This publication includes information about patients seen and NHS dental activity broken down to dental contract level. For further information about the data in this publication please see the supporting information in the resources linked below. To limit COVID-19 transmissions, dental practices were instructed to close and cease all routine dental care from the 25th March 2020, and began to reopen from 8th June 2020. The data reported in the publication including activity, patient numbers, finances and treatments, will be lower than expected during the time period of restrictions. Further information can be found in our 2019/20 Annual publication supplementary report and the data quality statement - see links below. NOTE: .csv files may not open in applications such as Microsoft Excel due to the number of rows included in the dataset. Users may wish to import the file directly into a database, or alternatively use a text editor to split the file for import/use in applications such as Microsoft Excel.