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Documentation completeness and demographic representativeness of public dental imaging datasets: a cross-sectional analysis of repository metadata

Author

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  • Moreno Benítez, Diana Belkis
  • Vega Bonilla, Roberto Antonio
  • Escalante Freitez, Flor M
  • drflorescalante@gmail.com

Abstract

Introduction: Public dental imaging datasets are the fuel of dental artificial intelligence, but their documentation and their demographic composition are rarely described in the studies that use them. Methods: A cross-sectional sample of public dental imaging datasets was assembled from a public model repository, and each dataset's metadata was assessed for documentation completeness and for the presence of demographic information in its description. Results: Among 13 datasets, 5 (38.5%; 95% CI 17.7 to 64.5) carried a license and 9 (69.2%) carried a readme, while only 5 (38.5%) mentioned any demographic variable, and none mentioned sex or race or ethnicity. Conclusions: Public dental imaging datasets are documented incompletely and almost never describe the demographic composition of their subjects, which limits their reuse and obscures the representativeness of the models trained on them.

Suggested Citation

  • Moreno Benítez, Diana Belkis & Vega Bonilla, Roberto Antonio & Escalante Freitez, Flor M & drflorescalante@gmail.com, 2026. "Documentation completeness and demographic representativeness of public dental imaging datasets: a cross-sectional analysis of repository metadata," SAP Artificial Intelligence in Dentistry, South American Publishing.
    • Handle: RePEc:cwf:aidart:aid2026137
    DOI: 10.62486/aid2026137
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