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Automatic caries detection in bitewing radiographs: part I—deep learning

Clinical Oral Investigations (2023)Cite this article

Abstract

Objective

The aim of this work was to assemble a large annotated dataset of bitewing radiographs and to use convolutional neural networks to automate the detection of dental caries in bitewing radiographs with human-level performance.

Materials and methods

A dataset of 3989 bitewing radiographs was created, and 7257 carious lesions were annotated using minimal bounding boxes. The dataset was then divided into 3 parts for the training (70%), validation (15%), and testing (15%) of multiple object detection convolutional neural networks (CNN). The tested CNN architectures included YOLOv5, Faster R-CNN, RetinaNet, and EfficientDet. To further improve the detection performance, model ensembling was used, and nested predictions were removed during post-processing. The models were compared in terms of the \(F_1\) score and average precision (AP) with various thresholds of the intersection over union (IoU).

Results

The twelve tested architectures had \(F_1\) scores of 0.72–0.76. Their performance was improved by ensembling which increased the \(F_1\) score to 0.79–0.80. The best-performing ensemble detected caries with the precision of 0.83, recall of 0.77, \(F_1=0.80\), and AP of 0.86 at IoU=0.5. Small carious lesions were predicted with slightly lower accuracy (AP 0.82) than medium or large lesions (AP 0.88).

Conclusions

The trained ensemble of object detection CNNs detected caries with satisfactory accuracy and performed at least as well as experienced dentists (see companion paper, Part II). The performance on small lesions was likely limited by inconsistencies in the training dataset.

Clinical significance

Caries can be automatically detected using convolutional neural networks. However, detecting incipient carious lesions remains challenging.

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Data availability statement

The cource code for the method described here is available at https://github.com/kuntiik/MT/ and the test dataset is available in Mendelay Data [35].

Notes

  1. https://github.com/opencv/cvat

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Funding

This work was supported by the General University Hospital in Prague (project GIP-21-SL-01-232) and by the OP VVV funded project CZ.02.1.01/0.0/0.0/16_019/0000765 “Research Center for Informatics”. The study sponsors had no involvement in the study design, analysis, interpretation of the data, writing, or choosing the publication venue.

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Authors and Affiliations

  1. Faculty of Electrical Engineering, Czech Technical University in Prague, Prague, Czech Republic

    Lukáš Kunt & Jan Kybic

  2. Institute of Dental Medicine, First Faculty of Medicine of the Charles University and General University Hospital, Prague, Czech Republic

    Valéria Nagyová & Antonín Tichý

Authors
  1. Lukáš Kunt
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  2. Jan Kybic
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  3. Valéria Nagyová
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  4. Antonín Tichý
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Contributions

L.K.: implementation, experiments, writing and editing. J.K.: image analysis, machine learning and statistical methodology, supervision of the implementation and experiments, writing and editing. V.N.: data validation and annotation, writing and editing. A.T.: conceptualization, data curation and annotation, writing and editing.

Corresponding author

Correspondence to Jan Kybic.

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Ethics approval

This research was approved by the Ethics Committee of the General University Hospital in Prague, protocol number 82/21. The patients signed a written informed consent, agreeing with the use of their data in anonymized form for research purposes.

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The authors declare no competing interests.

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Kunt, L., Kybic, J., Nagyová, V. et al. Automatic caries detection in bitewing radiographs: part I—deep learning. Clin Oral Invest (2023). https://doi.org/10.1007/s00784-023-05335-1

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