Development of AI-Based Diagnostic Algorithm for Nasal Bone Fracture Using Deep Learning

被引:4
|
作者
Jeong, Yeonjin [1 ]
Jeong, Chanho [2 ]
Sung, Kun-Yong [2 ]
Moon, Gwiseong [3 ]
Lim, Jinsoo [4 ]
机构
[1] Natl Med Ctr, Dept Plast & Reconstruct Surg, Seoul, South Korea
[2] Kangwon Natl Univ Hosp, Dept Plast & Reconstruct Surg, Kangwon Do, South Korea
[3] Kangwon Natl Univ, Dept Comp Sci & Engn, Kangwon Do, South Korea
[4] Catholic Univ Korea, Coll Med, St Vincents Hosp, Dept Plast & Reconstruct Surg, 93 Jungbu Daero, Suwon 16247, Gyeonggi Do, South Korea
关键词
Artificial intelligence; deep learning; facial bone CT; nasal bone fracture;
D O I
10.1097/SCS.0000000000009856
中图分类号
R61 [外科手术学];
学科分类号
摘要
Facial bone fractures are relatively common, with the nasal bone the most frequently fractured facial bone. Computed tomography is the gold standard for diagnosing such fractures. Most nasal bone fractures can be treated using a closed reduction. However, delayed diagnosis may cause nasal deformity or other complications that are difficult and expensive to treat. In this study, the authors developed an algorithm for diagnosing nasal fractures by learning computed tomography images of facial bones with artificial intelligence through deep learning. A significant concordance with human doctors' reading results of 100% sensitivity and 77% specificity was achieved. Herein, the authors report the results of a pilot study on the first stage of developing an algorithm for analyzing fractures in the facial bone.
引用
收藏
页码:29 / 32
页数:4
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