Artificial Intelligence (AI)-Driven Molar Angulation Measurements to Predict Third Molar Eruption on Panoramic Radiographs

Myrthel Vranckx, Adriaan Van Gerven, Holger Willems, Arne Vandemeulebroucke, André Ferreira Leite, Constantinus Politis and Reinhilde Jacobs
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Myrthel Vranckx: OMFS-IMPATH Research Group, Department of Imaging and Pathology, Faculty of Medicine, University of Leuven, and Department of Oral and Maxillofacial Surgery, University Hospitals Leuven, 3000 Leuven, Belgium
Adriaan Van Gerven: Relu, R&D, 3000 Leuven, Belgium
Holger Willems: Relu, R&D, 3000 Leuven, Belgium
Arne Vandemeulebroucke: OMFS-IMPATH Research Group, Department of Imaging and Pathology, Faculty of Medicine, University of Leuven, and Department of Oral and Maxillofacial Surgery, University Hospitals Leuven, 3000 Leuven, Belgium
André Ferreira Leite: OMFS-IMPATH Research Group, Department of Imaging and Pathology, Faculty of Medicine, University of Leuven, and Department of Oral and Maxillofacial Surgery, University Hospitals Leuven, 3000 Leuven, Belgium
Constantinus Politis: OMFS-IMPATH Research Group, Department of Imaging and Pathology, Faculty of Medicine, University of Leuven, and Department of Oral and Maxillofacial Surgery, University Hospitals Leuven, 3000 Leuven, Belgium
Reinhilde Jacobs: OMFS-IMPATH Research Group, Department of Imaging and Pathology, Faculty of Medicine, University of Leuven, and Department of Oral and Maxillofacial Surgery, University Hospitals Leuven, 3000 Leuven, Belgium

IJERPH, 2020, vol. 17, issue 10, 1-13

Abstract: The purpose of the presented Artificial Intelligence (AI)-tool was to automatically segment the mandibular molars on panoramic radiographs and extract the molar orientations in order to predict the third molars’ eruption potential. In total, 838 panoramic radiographs were used for training ( n = 588) and validation ( n = 250) of the network. A fully convolutional neural network with ResNet-101 backbone jointly predicted the molar segmentation maps and an estimate of the orientation lines, which was then iteratively refined by regression on the mesial and distal sides of the segmentation contours. Accuracy was quantified as the fraction of correct angulations (with predefined error intervals) compared to human reference measurements. Performance differences between the network and reference measurements were visually assessed using Bland−Altman plots. The quantitative analysis for automatic molar segmentation resulted in mean IoUs approximating 90%. Mean Hausdorff distances were lowest for first and second molars. The network angulation measurements reached accuracies of 79.7% [−2.5°; 2.5°] and 98.1% [−5°; 5°], combined with a clinically significant reduction in user-time of >53%. In conclusion, this study validated a new and unique AI-driven tool for fast, accurate, and consistent automated measurement of molar angulations on panoramic radiographs. Complementing the dental practitioner with accurate AI-tools will facilitate and optimize dental care and synergistically lead to ever-increasing diagnostic accuracies.

Keywords: artificial intelligence; convolutional neural network; segmentation; orientation; third molar; panoramic radiography (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2020
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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