Clinical indications for digital volume tomography in oral and maxillofacial surgery.

Ziegler, C.; Woertche, R; Brief, Jakob; Haßfeld, Stefan

doi:10.1038/sj/dmfr/4600680

Cited by 64 publications

(1 citation statement)

References 11 publications

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“…As a complement, CBCT serves as an excellent imaging modality for the maxillofacial region, offering clear, highly contrasted images of anatomical structures and a valuable tool for bone evaluation [ 44 , 45 ]. Deep learning combining CBCT images also enhanced the quality of radiograph interpretation.…”

Section: Discussionmentioning

confidence: 99%

Differential Diagnosis of OKC and SBC on Panoramic Radiographs: Leveraging Deep Learning Algorithms

Sim,

Hwang,

Ryu

et al. 2024

Diagnostics

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This study aims to determine whether it can distinguish odontogenic keratocyst (OKC) and simple bone cyst (SBC) based solely on preoperative panoramic radiographs through a deep learning algorithm. (1) Methods: We conducted a retrospective analysis of patient data from January 2018 to December 2022 at Pusan National University Dental Hospital. This study included 63 cases of OKC confirmed by histological examination after surgical excision and 125 cases of SBC that underwent surgical curettage. All panoramic radiographs were obtained utilizing the Proline XC system (Planmeca Co., Helsinki, Finland), which already had diagnostic data on them. The panoramic images were cut into 299 × 299 cropped sizes and divided into 80% training and 20% validation data sets for 5-fold cross-validation. Inception-ResNet-V2 system was adopted to train for OKC and SBC discrimination. (2) Results: The classification network for diagnostic performance evaluation achieved 0.829 accuracy, 0.800 precision, 0.615 recall, and a 0.695 F1 score. (4) Conclusions: The deep learning algorithm demonstrated notable accuracy in distinguishing OKC from SBC, facilitated by CAM visualization. This progress is expected to become an essential resource for clinicians, improving diagnostic and treatment outcomes.

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Section: Discussionmentioning

confidence: 99%