臼齿
人工智能
计算机科学
骨骼化
射线照相术
诊断准确性
口腔正畸科
牙科
根管
模式识别(心理学)
医学
放射科
作者
Steven Kempers,Pieter van Lierop,Tzu-Ming Harry Hsu,David Ansarri Moin,Stefaan Bergé,Hossein Ghaeminia,Tong Xi,Shankeeth Vinayahalingam
标识
DOI:10.1016/j.jdent.2023.104519
摘要
Objective: The aim of this study is to automatically assess the positional relationship between lower third molars (M3i) and the mandibular canal (MC) based on the panoramic radiograph(s) (PR(s)).Material and methods: A total of 1444 M3s were manually annotated and labeled on 863 PRs as a reference.A deep-learning approach, based on MobileNet-V2 combination with a skeletonization algorithm and a signed distance method, was trained and validated on 733 PRs with 1227 M3s to classify the positional relationship between M3i and MC into three categories.Subsequently, the trained algorithm was applied to a test set consisting of 130 PRs (217 M3s).Accuracy, precision, sensitivity, specificity, negative predictive value, and F1-score were calculated.Results: The proposed method achieved a weighted accuracy of 0.951, precision of 0.943, sensitivity of 0.941, specificity of 0.800, negative predictive value of 0.865 and an F1-score of 0.938.Conclusion: AI-enhanced assessment of PRs can objectively, accurately, and reproducibly determine the positional relationship between M3i and MC.Clinical significance: The use of such an explainable AI system can assist clinicians in the intuitive positional assessment of lower third molars and mandibular canals.Further research is required to automatically assess the risk of alveolar nerve injury on panoramic radiographs.
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