轻量型单步深度学习网络自动识别下颌智齿牙根与下颌管位置关系的研究

doi:10.13591/j.cnki.kqyx.2023.06.010

Abstract

Abstract:

Objective To develop a lite one-step deep learning network to detect the topographic proximity of the mandibular third molar(MTM)root to the inferior alveolar canal(IAC)on panoramic radiographs. Methods The samples, which consisted of 1 570 patients with 2 543 MTMs on paired panoramic radiographs and cone-beam computed tomography(CBCT), were randomly divided into the training group (80%), the validation group (10%), and the test group (10%). The evaluation of CBCT was defined as the ground truth. An extension of YOLO(You only look once)network, named as IAC-MTMnet, was trained to detect the proximity of MTM root to IAC on panoramic radiographs. Diagnostic performance analysis used accuracy, sensitivity, specificity, and positive predict value(PPV), and the area under the curve(AUC)was calculated based on the receiver operating characteristic(ROC)curve. Results On CBCT images, direct contact between MTM and IAC was observed on 798(31.38%)sides. The IAC-MTMnet achieved an accuracy of 0.885, a sensitivity of 0.747, a specificity of 0.956, and a PPV of 0.899. The AUC value achieved 0.95 and the test time was 0.059 s. Conclusion IAC-MTMnet is developed as a novel, robust and accurate method for detecting the proximity of MTM/IAC on panoramic radiographs.

Key words: mandibular third molar, inferior alveolar canal, panoramic radiography, CBCT, deep learning

CLC Number:

R814

WANG Zhifan, DAI Xiubin, ZHOU Yanqi, MAO Tianyi, HUANG Hong, SONG Hongcheng, WANG Dongmiao. Automated detection of mandibular third molar root contacting with inferior alveolar canal on panoramic radiographs using a lite one-stage deep learning model[J]. Stomatology, 2023, 43(6): 534-539.

Figures/Tables 6

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References 29

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信息项目	n(%)
性别
男性	667(42.48)
女性	903(57.52)
阻生侧
右侧	321(20.45)
左侧	276(17.58)
双侧	973(61.97)
CBCT诊断(金标准)
接触	798(31.38)
未接触	1 745(68.62)
曲面断层片诊断
接触	1 095(43.06)
未接触	1 448(56.94)
曲面断层片特征性影像
一个或多个	779(71.14)
无	316(28.86)

方法	正确率	灵敏度	特异度	阳性预测值	检测时间/s
IAC-MTMnet	0.885	0.747	0.956	0.899	0.059
YOLOv5	0.881	0.819	0.913	0.829	219.8
人工评估	0.845	0.741	0.892	0.759	-

Automated detection of mandibular third molar root contacting with inferior alveolar canal on panoramic radiographs using a lite one-stage deep learning model

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