The influence of age, sex, and tooth type on the anatomical relationship between tooth roots and the mandibular canal

Puciło, Michał; Puciło, Aleksandra; Safranow, Krzysztof; Nowicka, Alicja

doi:10.5624/isd.20210031

Cited by 3 publications

(1 citation statement)

References 29 publications

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“…Panoramic radiographs typically display low contrast due to the inclusion of excessive non-target tissue during the image reconstruction process, which can impact inter-observer variability. 8 Consequently, an automated mandibular canal segmentation system must be developed to alleviate the burden on radiologists.…”

Section: Introductionmentioning

confidence: 99%

Deep learning-based automatic segmentation of the mandibular canal on panoramic radiographs: A multi-device study

Aung,

Lim,

Han

et al. 2024

Imaging Sci Dent

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Purpose The objective of this study was to propose a deep-learning model for the detection of the mandibular canal on dental panoramic radiographs. Materials and Methods A total of 2,100 panoramic radiographs (PANs) were collected from 3 different machines: RAYSCAN Alpha (n=700, PAN A), OP-100 (n=700, PAN B), and CS8100 (n=700, PAN C). Initially, an oral and maxillofacial radiologist coarsely annotated the mandibular canals. For deep learning analysis, convolutional neural networks (CNNs) utilizing U-Net architecture were employed for automated canal segmentation. Seven independent networks were trained using training sets representing all possible combinations of the 3 groups. These networks were then assessed using a hold-out test dataset. Results Among the 7 networks evaluated, the network trained with all 3 available groups achieved an average precision of 90.6%, a recall of 87.4%, and a Dice similarity coefficient (DSC) of 88.9%. The 3 networks trained using each of the 3 possible 2-group combinations also demonstrated reliable performance for mandibular canal segmentation, as follows: 1) PAN A and B exhibited a mean DSC of 87.9%, 2) PAN A and C displayed a mean DSC of 87.8%, and 3) PAN B and C demonstrated a mean DSC of 88.4%. Conclusion This multi-device study indicated that the examined CNN-based deep learning approach can achieve excellent canal segmentation performance, with a DSC exceeding 88%. Furthermore, the study highlighted the importance of considering the characteristics of panoramic radiographs when developing a robust deep-learning network, rather than depending solely on the size of the dataset.

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Section: Introductionmentioning

confidence: 99%