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Artificial intelligence-driven novel tool for tooth detection and segmentation on panoramic radiographs

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Clinical Oral Investigations Aims and scope Submit manuscript

Abstract

Objective

To evaluate the performance of a new artificial intelligence (AI)-driven tool for tooth detection and segmentation on panoramic radiographs.

Materials and methods

In total, 153 radiographs were collected. A dentomaxillofacial radiologist labeled and segmented each tooth, serving as the ground truth. Class-agnostic crops with one tooth resulted in 3576 training teeth. The AI-driven tool combined two deep convolutional neural networks with expert refinement. Accuracy of the system to detect and segment teeth was the primary outcome, time analysis secondary. The Kruskal-Wallis test was used to evaluate differences of performance metrics among teeth groups and different devices and chi-square test to verify associations among the amount of corrections, presence of false positive and false negative, and crown and root parts of teeth with potential AI misinterpretations.

Results

The system achieved a sensitivity of 98.9% and a precision of 99.6% for tooth detection. For segmenting teeth, lower canines presented best results with the following values for intersection over union, precision, recall, F1-score, and Hausdorff distances: 95.3%, 96.9%, 98.3%, 97.5%, and 7.9, respectively. Although still above 90%, segmentation results for both upper and lower molars were somewhat lower. The method showed a clinically significant reduction of 67% of the time consumed for the manual.

Conclusions

The AI tool yielded a highly accurate and fast performance for detecting and segmenting teeth, faster than the ground truth alone.

Clinical significance

An innovative clinical AI-driven tool showed a faster and more accurate performance to detect and segment teeth on panoramic radiographs compared with manual segmentation.

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Funding

The authors gratefully acknowledge financial support from Fundação de Apoio à Pesquisa do Distrito Federal– FAP-DF (protocol #23106.013588/2019-05).

Author information

Authors and Affiliations

  1. OMFS IMPATH Research Group, Department of Imaging and Pathology, Faculty of Medicine, University of Leuven and Oral & Maxillofacial Surgery, University Hospitals Leuven, KU Leuven, Kapucijnenvoer 33, 3000, Leuven, Belgium

    André Ferreira Leite, Pierre Lahoud, Hugo Gaêta-Araujo, Myrthel Vranckx & Reinhilde Jacobs

  2. Department of Dentistry, Faculty of Health Sciences, Campus Universitario Darcy Ribeiro, University of Brasília, Brasília, 70910-900, Brazil

    André Ferreira Leite

  3. Relu, Innovatie-en incubatiecentrum KU Leuven, Leuven, Belgium

    Adriaan Van Gerven, Holger Willems & Thomas Beznik

  4. Division of Oral Radiology, Department of Oral Diagnosis, Piracicaba Dental School, University of Campinas, Piracicaba, São Paulo, Brazil

    Hugo Gaêta-Araujo

  5. Department of Dental Medicine, Karolinska Institutet, Stockholm, Sweden

    Reinhilde Jacobs

Authors
  1. André Ferreira Leite
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  2. Adriaan Van Gerven
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Correspondence to André Ferreira Leite.

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Cite this article

Leite, A.F., Gerven, A.V., Willems, H. et al. Artificial intelligence-driven novel tool for tooth detection and segmentation on panoramic radiographs. Clin Oral Invest 25, 2257–2267 (2021). https://doi.org/10.1007/s00784-020-03544-6

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  • DOI: https://doi.org/10.1007/s00784-020-03544-6

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