基于全景片评估下颌第一磨牙牙根形态的深度学习人工智能系统。
A deep-learning artificial intelligence system for assessment of root morphology of the mandibular first molar on panoramic radiography.
机构信息
1 Department of Oral and Maxillofacial Radiology, Aichi-Gakuin University School of Dentistry , Nagoya , Japan.
2 Department of Endodontics, Aichi-Gakuin University School of Dentistry , Nagoya , Japan.
出版信息
Dentomaxillofac Radiol. 2019 Mar;48(3):20180218. doi: 10.1259/dmfr.20180218. Epub 2018 Nov 9.
OBJECTIVES
: The distal root of the mandibular first molar occasionally has an extra root, which can directly affect the outcome of endodontic therapy. In this study, we examined the diagnostic performance of a deep learning system for classification of the root morphology of mandibular first molars on panoramic radiographs. Dental cone-beam CT (CBCT) was used as the gold standard.
METHODS
: CBCT images and panoramic radiographs of 760 mandibular first molars from 400 patients who had not undergone root canal treatments were analyzed. Distal roots were examined on CBCT images to determine the presence of a single or extra root. Image patches of the roots were segmented from panoramic radiographs and applied to a deep learning system, and its diagnostic performance in the classification of root morphplogy was examined.
RESULTS
: Extra roots were observed in 21.4% of distal roots on CBCT images. The deep learning system had diagnostic accuracy of 86.9% for the determination of whether distal roots were single or had extra roots.
CONCLUSIONS
: The deep learning system showed high accuracy in the differential diagnosis of a single or extra root in the distal roots of mandibular first molars.
目的
下颌第一磨牙的远中根偶尔会有额外的根，这可能直接影响根管治疗的效果。本研究旨在评估深度学习系统在基于曲面体层片（panoramic radiographs）对下颌第一磨牙远中根形态分类中的诊断性能，以锥形束 CT（cone-beam CT，CBCT）为金标准。
方法
对 400 名未经根管治疗的患者的 760 个下颌第一磨牙的 CBCT 图像和全景片进行分析。在 CBCT 图像上检查远中根是否存在单根或额外根。从全景片上提取根的图像块，并应用于深度学习系统，以检查其在分类根形态方面的诊断性能。
结果
在 CBCT 图像上，21.4%的远中根存在额外根。深度学习系统对确定远中根是否为单根或有额外根的诊断准确率为 86.9%。
结论
深度学习系统在鉴别下颌第一磨牙远中根的单根或额外根方面具有较高的准确性。
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