LMCD-OR: a large-scale, multilevel categorized diagnostic dataset for oral radiography.
AI-driven diagnosis
Baseline models
Dataset competition
Dentistry
Multilevel classification
Journal
Journal of translational medicine
ISSN: 1479-5876
Titre abrégé: J Transl Med
Pays: England
ID NLM: 101190741
Informations de publication
Date de publication:
14 Oct 2024
14 Oct 2024
Historique:
received:
23
08
2024
accepted:
03
10
2024
medline:
15
10
2024
pubmed:
15
10
2024
entrez:
14
10
2024
Statut:
epublish
Résumé
In recent years, digital dentistry has increasingly utilized advanced image analysis techniques, such as image classification and disease diagnosis, to improve clinical outcomes. Despite these advances, the lack of comprehensive benchmark datasets is a significant barrier. To address this gap, our research team develop LMCD-OR, a substantial collection of oral radiograph images designed to support extensive artificial intelligence (AI)-driven diagnostics. LMCD-OR comprises 3,818 digital imaging and communications in medicine (DICOM) oral X-ray images from local medical institutions that are meticulously annotated to provide broad category information for both primary dental outpatient services and detailed secondary disease diagnoses. This dataset is engineered to train and validate multiclassification models to improve the precision and scope of oral disease diagnostics. To ensure robust dataset validation, we employ four cutting-edge visual neural network classification models as benchmarks. These models are tested against rigorous performance metrics, demonstrating the ability of the dataset to support advanced image classification and disease diagnosis tasks. LMCD-OR is publicly available at http://dentaldataset.zeroacademy.net .
Identifiants
pubmed: 39402640
doi: 10.1186/s12967-024-05741-3
pii: 10.1186/s12967-024-05741-3
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
930Subventions
Organisme : Natural Science Foundation of Liaoning Province
ID : 2023-MS-288
Organisme : Fundamental Research Funds for the Liaoning Universities
ID : Fundamental Research Funds for the Liaoning Universities
Organisme : the Ministry of Education Industry-Academia Talent Development Program
ID : 202101160011
Organisme : the Key Program of Translational Medicine Fund of Wenzhou Research Institute of Shanghai University
ID : SDTMF2023KP04
Informations de copyright
© 2024. The Author(s).
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