Publicly Available Dental Image Datasets for Artificial Intelligence

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dc.contributor.authorUribe, Sergio E.
dc.contributor.authorIssa, Julien
dc.contributor.authorSohrabniya, F.
dc.contributor.authorDenny, A.
dc.contributor.authorKim, N.N.
dc.contributor.authorDayo, A. F.
dc.contributor.authorChaurasia, Akhilanand
dc.contributor.authorSofi-Mahmudi, Ahmad
dc.contributor.authorBüttner, Martha
dc.contributor.authorSchwendicke, Falk
dc.contributor.institutionDepartment of Conservative Dentistry and Oral Health
dc.date.accessioned2024-12-18T19:35:02Z
dc.date.available2024-12-18T19:35:02Z
dc.date.issued2024
dc.descriptionPublisher Copyright: © The Author(s) 2024.
dc.description.abstractThe development of artificial intelligence (AI) in dentistry requires large and well-annotated datasets. However, the availability of public dental imaging datasets remains unclear. This study aimed to provide a comprehensive overview of all publicly available dental imaging datasets to address this gap and support AI development. This observational study searched all publicly available dataset resources (academic databases, preprints, and AI challenges), focusing on datasets/articles from 2020 to 2023, with PubMed searches extending back to 2011. We comprehensively searched for dental AI datasets containing images (intraoral photos, scans, radiographs, etc.) using relevant keywords. We included datasets of >50 images obtained from publicly available sources. We extracted dataset characteristics, patient demographics, country of origin, dataset size, ethical clearance, image details, FAIRness metrics, and metadata completeness. We screened 131,028 records and extracted 16 unique dental imaging datasets. The datasets were obtained from Kaggle (18.8%), GitHub, Google, Mendeley, PubMed, Zenodo (each 12.5%), Grand-Challenge, OSF, and arXiv (each 6.25%). The primary focus was tooth segmentation (62.5%) and labeling (56.2%). Panoramic radiography was the most common imaging modality (58.8%). Of the 13 countries, China contributed the most images (2,413). Of the datasets, 75% contained annotations, whereas the methods used to establish labels were often unclear and inconsistent. Only 31.2% of the datasets reported ethical approval, and 56.25% did not specify a license. Most data were obtained from dental clinics (50%). Intraoral radiographs had the highest findability score in the FAIR assessment, whereas cone-beam computed tomography datasets scored the lowest in all categories. These findings revealed a scarcity of publicly available imaging dental data and inconsistent metadata reporting. To promote the development of robust, equitable, and generalizable AI tools for dental diagnostics, treatment, and research, efforts are needed to address data scarcity, increase diversity, mandate metadata completeness, and ensure FAIRness in AI dental imaging research.en
dc.description.statusPeer reviewed
dc.format.extent10
dc.format.extent2203197
dc.identifier.citationUribe, S E, Issa, J, Sohrabniya, F, Denny, A, Kim, N N, Dayo, A F, Chaurasia, A, Sofi-Mahmudi, A, Büttner, M & Schwendicke, F 2024, 'Publicly Available Dental Image Datasets for Artificial Intelligence', Journal of Dental Research, vol. 103, no. 13, pp. 1365-1374. https://doi.org/10.1177/00220345241272052
dc.identifier.doi10.1177/00220345241272052
dc.identifier.issn0022-0345
dc.identifier.otherMendeley: 623923f6-a68a-366a-9359-81c349c5d3c0
dc.identifier.urihttps://dspace.rsu.lv/jspui/handle/123456789/16981
dc.identifier.urlhttps://www-webofscience-com.db.rsu.lv/wos/alldb/full-record/MEDLINE:39422586
dc.identifier.urlhttp://www.scopus.com/inward/record.url?scp=85207306740&partnerID=8YFLogxK
dc.language.isoeng
dc.relation.ispartofJournal of Dental Research
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectArtificial intelligence
dc.subjectdentistry
dc.subjectdataset
dc.subjectpanoramic radiography
dc.subjectmetadata
dc.subjectdiagnostic imaging
dc.subjectbig data
dc.subjectdeep learning/machine learning
dc.subjectdigital imaging/radiology
dc.subject3.2 Clinical medicine
dc.subject1.2 Computer and information sciences
dc.subject1.1. Scientific article indexed in Web of Science and/or Scopus database
dc.subjectGeneral Dentistry
dc.subjectArtificial Intelligence
dc.titlePublicly Available Dental Image Datasets for Artificial Intelligenceen
dc.type/dk/atira/pure/researchoutput/researchoutputtypes/contributiontojournal/article

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