A Two-Stage Deep-Learning Model for Determination of the Contact of Mandibular Third Molars With the Mandibular Canal on Panoramic Radiographs Publisher Pubmed



Soltani P^{1, 2} ; Sohrabniya F³ ; Mohammadrahimi H³ ; Mehdizadeh M¹ ; Mohammadreza Mousavi S⁴ ; Moaddabi A⁵ ; Mohammadmahdi Mousavi S⁶ ; Spagnuolo G² ; Yavari A⁴ ; Schwendicke F⁷ Show Affiliations
Source: BMC Oral Health Published:2024

Abstract

Objectives: This study aimed to assess the accuracy of a two-stage deep learning (DL) model for (1) detecting mandibular third molars (MTMs) and the mandibular canal (MC), and (2) classifying the anatomical relationship between these structures (contact/no contact) on panoramic radiographs. Method: MTMs and MCs were labeled on panoramic radiographs by a calibrated examiner using bounding boxes. Each bounding box contained MTM and MC on one side. The relationship of MTMs with the MC was assessed on CBCT scans by two independent examiners without the knowledge of the condition of MTM and MC on the corresponding panoramic image, and dichotomized as contact/no contact. Data were split into training, validation, and testing sets with a ratio of 80:10:10. Faster R-CNN was used for detecting MTMs and MCs and ResNeXt for classifying their relationship. AP50 and AP75 were used as outcomes for detecting MTMs and MCs, and accuracy, precision, recall, F1-score, and the area-under-the-receiver-operating-characteristics curve (AUROC) were used to assess classification performance. The training and validation of the models were conducted using the Python programming language with the PyTorch framework. Results: Three hundred eighty-seven panoramic radiographs were evaluated. MTMs were present bilaterally on 232 and unilaterally on 155 radiographs. In total, 619 images were collected which included MTMs and MCs. AP50 and AP75 indicating accuracy for detecting MTMs and MCs were 0.99 and 0.90 respectively. Classification accuracy, recall, specificity, F1-score, precision, and AUROC values were 0.85, 0.85, 0.93, 0.84, 0.86, and 0.91, respectively. Conclusion: DL can detect MTMs and MCs and accurately assess their anatomical relationship on panoramic radiographs. © The Author(s) 2024.