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AI-Dentify: Deep learning for proximal caries detection on bitewing x-ray -- HUNT4 Oral Health Study

Javier Pérez de Frutos, Ragnhild Holden Helland, Shreya Desai, Line Cathrine Nymoen, Thomas Langø, Theodor Remman, Abhijit Sen

TL;DR

The trained models show an increase in average precision and F1-score, and decrease of false negative rate, with respect to the dental clinicians, and the trained models show an increase in average precision and F1-score, and decrease of false negative rate, with respect to the dental clinicians.

Abstract

Background: Dental caries diagnosis requires the manual inspection of diagnostic bitewing images of the patient, followed by a visual inspection and probing of the identified dental pieces with potential lesions. Yet the use of artificial intelligence, and in particular deep-learning, has the potential to aid in the diagnosis by providing a quick and informative analysis of the bitewing images. Methods: A dataset of 13,887 bitewings from the HUNT4 Oral Health Study were annotated individually by six different experts, and used to train three different object detection deep-learning architectures: RetinaNet (ResNet50), YOLOv5 (M size), and EfficientDet (D0 and D1 sizes). A consensus dataset of 197 images, annotated jointly by the same six dentist, was used for evaluation. A five-fold cross validation scheme was used to evaluate the performance of the AI models. Results: he trained models show an increase in average precision and F1-score, and decrease of false negative rate, with respect to the dental clinicians. When compared against the dental clinicians, the YOLOv5 model shows the largest improvement, reporting 0.647 mean average precision, 0.548 mean F1-score, and 0.149 mean false negative rate. Whereas the best annotators on each of these metrics reported 0.299, 0.495, and 0.164 respectively. Conclusion: Deep-learning models have shown the potential to assist dental professionals in the diagnosis of caries. Yet, the task remains challenging due to the artifacts natural to the bitewing images.

AI-Dentify: Deep learning for proximal caries detection on bitewing x-ray -- HUNT4 Oral Health Study

TL;DR

The trained models show an increase in average precision and F1-score, and decrease of false negative rate, with respect to the dental clinicians, and the trained models show an increase in average precision and F1-score, and decrease of false negative rate, with respect to the dental clinicians.

Abstract

Background: Dental caries diagnosis requires the manual inspection of diagnostic bitewing images of the patient, followed by a visual inspection and probing of the identified dental pieces with potential lesions. Yet the use of artificial intelligence, and in particular deep-learning, has the potential to aid in the diagnosis by providing a quick and informative analysis of the bitewing images. Methods: A dataset of 13,887 bitewings from the HUNT4 Oral Health Study were annotated individually by six different experts, and used to train three different object detection deep-learning architectures: RetinaNet (ResNet50), YOLOv5 (M size), and EfficientDet (D0 and D1 sizes). A consensus dataset of 197 images, annotated jointly by the same six dentist, was used for evaluation. A five-fold cross validation scheme was used to evaluate the performance of the AI models. Results: he trained models show an increase in average precision and F1-score, and decrease of false negative rate, with respect to the dental clinicians. When compared against the dental clinicians, the YOLOv5 model shows the largest improvement, reporting 0.647 mean average precision, 0.548 mean F1-score, and 0.149 mean false negative rate. Whereas the best annotators on each of these metrics reported 0.299, 0.495, and 0.164 respectively. Conclusion: Deep-learning models have shown the potential to assist dental professionals in the diagnosis of caries. Yet, the task remains challenging due to the artifacts natural to the bitewing images.
Paper Structure (11 sections, 5 figures, 7 tables)

This paper contains 11 sections, 5 figures, 7 tables.

Table of Contents

  1. Introduction
  2. Study goals
  3. Methods
  4. Dataset
  5. Data annotation
  6. Object detection models
  7. Validation protocol
  8. Performance evaluation
  9. Results
  10. Discussion
  11. Conclusions

Figures (5)

  • Figure 1: Data workflow
  • Figure 2: Distribution of annotated images in the annotated dataset. In the legend, the number of annotated images for each interval is shown within brackets.
  • Figure 3: Distribution of annotations in the dataset annotated by the six dental clinicians. Enamel proximal caries (Grades 1 and 2, total $19,995$ annotations) are pictured in light green, dentine lesions (Grade 3 to 5, total $17,903$ annotations) are in orange, secondary lesions are depicted in pink, and caries of uncertain grade have been highlighted in grey. Image free of lesions (No caries) are shown in dark blue, here the number of annotations matches the number of images.
  • Figure 4: Bootstrap $95\%$ confidence intervals for the metrics mAP, mF1 and mFNR, for the models and the annotators
  • Figure 5: Detail of bitewing images from the consensus test set with predictions given by the trained models. The ground truth is shown in the bottom row.