ArticleBMC oral health2024
Enhancement of early proximal caries annotations in radiographs: introducing the Diagnostic Insights for Radiographic Early-caries with micro-CT (ACTA-DIRECT) dataset.
Article in BMC oral health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed.
- Accuracy of deep learning-based AI models for early caries lesion detection: the influence of annotation quality and reference choice.Clinical oral investigations · 2025Article
- Revolutionizing the diagnosis of dental caries using artificial intelligence-based methods.Journal of conservative dentistry and endodontics · 2025Review
- Performance evaluation of AI-based caries detection technology and its educational training module: a dual-phase investigation.Frontiers in dental medicine · 2025Article
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5 authors.
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Abstract
backgroundProximal caries datasets for training artificial intelligence (AI) algorithms commonly include clinician-annotated radiographs. These conventional annotations are susceptible to observer variability, and early caries may be missed. Micro-computed tomography (CT), while not feasible in clinical applications, offers a more accurate imaging modality to support the creation of a reference-standard dataset for caries annotations. Herein, we present the Academic Center for Dentistry Amsterdam-Diagnostic Insights for Radiographic Early-caries with micro-CT (ACTA-DIRECT) dataset, which is the first dataset pairing dental radiographs and micro-CT scans to enable higher-quality annotations.
methodsThe ACTA-DIRECT dataset encompasses 179 paired micro-CT scans and radiographs of early proximal carious teeth, along with three types of annotations: conventional annotations on radiographs, micro-CT-assisted annotations on radiographs, and micro-CT annotations (reference standard). Three dentists independently annotated proximal caries on radiographs, both with and without micro-CT assistance, enabling determinations of interobserver agreement and diagnostic accuracy. To establish a reference standard, one dental radiologist annotated all caries on the related micro-CT scans.
resultsMicro-CT support improved interobserver agreement (Cohen's Kappa), averaging 0.64 (95% confidence interval [CI]: 0.59-0.68) versus 0.46 (95% CI: 0.44-0.48) in its absence. Likewise, average sensitivity and specificity increased from 42% (95% CI: 34-51%) to 63% (95% CI: 54-71%) and from 92% (95% CI: 88-95%) to 95% (95% CI: 92-97%), respectively.
conclusionThe ACTA-DIRECT dataset offers high-quality images and annotations to support AI-based early caries diagnostics for training and validation. This study underscores the benefits of incorporating micro-CT scans in lesion assessments, providing enhanced precision and reliability.
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