A deep learning approach for radiological detection and classification of radicular cysts and periapical granulomas.

Autor: Ver Berne J; Department of Oral and Maxillofacial Surgery, University Hospitals Leuven, Kapucijnenvoer 33, Leuven 3000, Belgium; OMFS-IMPATH Research Group, Department of Imaging and Pathology, Catholic University Leuven, Belgium., Saadi SB; OMFS-IMPATH Research Group, Department of Imaging and Pathology, Catholic University Leuven, Belgium., Politis C; Department of Oral and Maxillofacial Surgery, University Hospitals Leuven, Kapucijnenvoer 33, Leuven 3000, Belgium; OMFS-IMPATH Research Group, Department of Imaging and Pathology, Catholic University Leuven, Belgium., Jacobs R; Department of Oral and Maxillofacial Surgery, University Hospitals Leuven, Kapucijnenvoer 33, Leuven 3000, Belgium; OMFS-IMPATH Research Group, Department of Imaging and Pathology, Catholic University Leuven, Belgium; Department of Dentistry, Karolinska Institutet, Stockholm, Sweden. Electronic address: reinhilde.jacobs@ki.se.
Jazyk: angličtina
Zdroj: Journal of dentistry [J Dent] 2023 Aug; Vol. 135, pp. 104581. Date of Electronic Publication: 2023 Jun 07.
DOI: 10.1016/j.jdent.2023.104581
Abstrakt: Objectives: Dentists and oral surgeons often face difficulties distinguishing between radicular cysts and periapical granulomas on panoramic imaging. Radicular cysts require surgical removal while root canal treatment is the first-line treatment for periapical granulomas. Therefore, an automated tool to aid clinical decision making is needed.
Methods: A deep learning framework was developed using panoramic images of 80 radicular cysts and 72 periapical granulomas located in the mandible. Additionally, 197 normal images and 58 images with other radiolucent lesions were selected to improve model robustness. The images were cropped into global (affected half of the mandible) and local images (only the lesion) and then the dataset was split into 90% training and 10% testing sets. Data augmentation was performed on the training dataset. A two-route convolutional neural network using the global and local images was constructed for lesion classification. These outputs were concatenated into the object detection network for lesion localization.
Results: The classification network achieved a sensitivity of 1.00 (95% C.I. 0.63-1.00), specificity of 0.95 (0.86-0.99), and AUC (area under the receiver-operating characteristic curve) of 0.97 for radicular cysts and a sensitivity of 0.77 (0.46-0.95), specificity of 1.00 (0.93-1.00), and AUC of 0.88 for periapical granulomas. Average precision for the localization network was 0.83 for radicular cysts and 0.74 for periapical granulomas.
Conclusions: The proposed model demonstrated reliable diagnostic performance for the detection and differentiation of radicular cysts and periapical granulomas. Using deep learning, diagnostic efficacy can be enhanced leading to a more efficient referral strategy and subsequent treatment efficacy.
Clinical Significance: A two-route deep learning approach using global and local images can reliably differentiate between radicular cysts and periapical granulomas on panoramic imaging. Concatenating its output to a localizing network creates a clinically usable workflow for classifying and localizing these lesions, enhancing treatment and referral practices.
Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2023. Published by Elsevier Ltd.)
Databáze: MEDLINE
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