Diagnostic accuracy of artificial intelligence systems for radiographic caries detection and high-quality dataset of annotated radiographs

Project Details


The project seeks to (1) evaluate and synthesize the evidence supporting the use of Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) for caries detection in intraoral radiographs, (2) generate a high quality annotated dataset of radiographic images, (3) generate a model for caries detection in intraoral radiographs using ML/DL with acceptable diagnostic accuracy for clinical use and (4) explore alternatives for the development of a viable clinical production system.

Layman's description

Artificial intelligence algorithms require training. Each research group tests the performance of its system with its own data set, which limits extrapolation or comparison. This project aims to generate an annotated dataset that allows other systems to compare their diagnostic performance for caries detection.
Short titleAI-ML for caries detection
Effective start/end date26/07/2125/07/22


  • Riga Stradins University: €13,022.00


  • Machine Learning
  • Dental caries
  • diagnostic algorithm
  • Artificial intelligence
  • deep learning
  • dataset

Field of Science

  • 3.3 Health sciences

Smart Specialization Area

  • Biomedicine, medical technologies and biotechnology


Explore the research topics touched on by this project. These labels are generated based on the underlying awards/grants. Together they form a unique fingerprint.
  • Deep Learning for Caries Detection: A Systematic Review

    Mohammad-rahimi, H., Motamedian, S. R., Rohban, M. H., Krois, J., Uribe, S., Nia, E. M., Rokhshad, R., Nadimi, M. & Schwendicke, F., Jul 2022, In: Journal of Dentistry. 122, 16 p., 104115.

    Research output: Contribution to journalReview articlepeer-review

    Open Access
    2 Citations (Scopus)
    1 Downloads (Pure)
  • Emulating Clinical Diagnostic Reasoning for Jaw Cysts with Machine Learning

    Feher, B., Kuchler, U., Schwendicke, F., Schneider, L., Cejudo Grano de Oro, J. E., Xi, T., Vinayahalingam, S., Hsu, T-M. H., Brinz, J., Chaurasia, A., Dhingra, K., Gaudin, R. A., Mohammad-Rahimi, H., Pereira, N., Perez-Pastor, F., Tryfonos, O., Uribe, S. E., Hanisch, M. & Krois, J., 19 Aug 2022, In: Diagnostics. 12, 8, 14 p., 1968.

    Research output: Contribution to journalArticlepeer-review

    Open Access