Artificial intelligence-driven novel tool for tooth detection and segmentation on panoramic radiographs
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ORIGINAL ARTICLE
Artificial intelligence-driven novel tool for tooth detection and segmentation on panoramic radiographs André Ferreira Leite 1,2 & Adriaan Van Gerven 3 & Holger Willems 3 & Thomas Beznik 3 & Pierre Lahoud 1 & Hugo Gaêta-Araujo 1,4 & Myrthel Vranckx 1 & Reinhilde Jacobs 1,5 Received: 22 June 2020 / Accepted: 20 August 2020 # Springer-Verlag GmbH Germany, part of Springer Nature 2020
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. Keywords Artificial intelligence . Machine learning . Panoramic radiography . Tooth . Classification
* André Ferreira Leite [email protected] 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
2
Department of Dentistry, Faculty of Health Sciences, Campus Universitario Darcy Ribeiro, University of Brasília, Brasília 70910-900, Brazil
3
Relu, Innovatie-en incubatiecentrum KU Leuven, Leuven, Belgium
4
Division of Oral Radiology, Department of Oral Diagnosis, Piracicaba Dental School, University of Campinas, Piracicaba, São Paulo, Brazil
5
Department of Dental Medicine, Karolinska Institutet, Stockholm, Sweden
Introduction Panoramic radiographs enable generating a 2D visualization of both dental arches. They are routinely used to
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