Automatic caries detection in bitewing radiographs-Part II: experimental comparison

Clin Oral Investig. 2024 Feb 5;28(2):133. doi: 10.1007/s00784-024-05528-2.


Objective: The objective of this study was to compare the detection of caries in bitewing radiographs by multiple dentists with an automatic method and to evaluate the detection performance in the absence of a reliable ground truth.

Materials and methods: Four experts and three novices marked caries using bounding boxes in 100 bitewing radiographs. The same dataset was processed by an automatic object detection deep learning method. All annotators were compared in terms of the number of errors and intersection over union (IoU) using pairwise comparisons, with respect to the consensus standard, and with respect to the annotator of the training dataset of the automatic method.

Results: The number of lesions marked by experts in 100 images varied between 241 and 425. Pairwise comparisons showed that the automatic method outperformed all dentists except the original annotator in the mean number of errors, while being among the best in terms of IoU. With respect to a consensus standard, the performance of the automatic method was best in terms of the number of errors and slightly below average in terms of IoU. Compared with the original annotator, the automatic method had the highest IoU and only one expert made fewer errors.

Conclusions: The automatic method consistently outperformed novices and performed as well as highly experienced dentists.

Clinical significance: The consensus in caries detection between experts is low. An automatic method based on deep learning can improve both the accuracy and repeatability of caries detection, providing a useful second opinion even for very experienced dentists.

Keywords: Bitewing; Convolutional neural networks; Dental caries detection; Ground truth; X-ray images.

MeSH terms

  • Dental Caries Susceptibility*
  • Dental Caries* / diagnostic imaging
  • Humans
  • Radiography, Bitewing