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
相关论文
共 50 条
  • [11] AI-Based Sensor Information Fusion for Supporting Deep Supervised Learning
    Leung, Carson K.
    Braun, Peter
    Cuzzocrea, Alfredo
    SENSORS, 2019, 19 (06)
  • [12] Visualization of deep learning data structures and AI-based classification in histopathology using dimensionality reduction
    Faust, Kevin
    Xie, Quin
    Djuric, Ugljesa
    Diamandis, Phedias
    JOURNAL OF NEUROPATHOLOGY AND EXPERIMENTAL NEUROLOGY, 2018, 77 (06): : 510 - 510
  • [13] AI-based computer vision using deep learning in 6G wireless networks
    Kamruzzaman, M. M.
    Alruwaili, Omar
    COMPUTERS & ELECTRICAL ENGINEERING, 2022, 102
  • [14] Exploring a strategy for the development of AI-based diagnostic tools for rare diseases
    Romero-Campo, Paula
    Guijarro-Berdinas, Bertha
    Jesus Sobrido, Maria
    Alonso-Betanzos, Amparo
    EUROPEAN JOURNAL OF HUMAN GENETICS, 2022, 30 (SUPPL 1) : 486 - 486
  • [15] Prediction of AI-Based Personal Thermal Comfort in a Car Using Machine-Learning Algorithm
    Ju, Yeong Jo
    Lim, Jeong Ran
    Jeon, Euy Sik
    ELECTRONICS, 2022, 11 (03)
  • [16] Machine Learning Pipeline Supports AI-based ApplicationsMachine Learning Pipeline Supports AI-based Applications
    Max Rasumak
    Jan Spaeth
    ATZheavy duty worldwide, 2025, 18 (1) : 20 - 25
  • [17] Development of a Spine X-Ray-Based Fracture Prediction Model Using a Deep Learning Algorithm
    Kong, Sung Hye
    Lee, Jae-Won
    Bae, Byeong Uk
    Sung, Jin Kyeong
    Jung, Kyu Hwan
    Kim, Jung Hee
    Shin, Chan Soo
    ENDOCRINOLOGY AND METABOLISM, 2022, 37 (04) : 674 - 683
  • [18] Explainable AI-based Federated Deep Reinforcement Learning for Trusted Autonomous Driving
    Rjoub, Gaith
    Bentahar, Jamal
    Wahab, Omar Abdel
    2022 INTERNATIONAL WIRELESS COMMUNICATIONS AND MOBILE COMPUTING, IWCMC, 2022, : 318 - 323
  • [19] A Deep Learning Methodology to Detect Trojaned AI-based DDoS Defend Model
    Chen, Yen-Hung
    Lai, Yuan-Cheng
    Lu, Cho-Hsun
    Huang, Yu-Ching
    Chang, Shun-Chieh
    Jan, Pi-Tzong
    2022 8th International Conference on Automation, Robotics and Applications, ICARA 2022, 2022, : 243 - 246
  • [20] AI-Based Automatic Detection and Classification of Diabetic Retinopathy Using U-Net and Deep Learning
    Bilal, Anas
    Zhu, Liucun
    Deng, Anan
    Lu, Huihui
    Wu, Ning
    SYMMETRY-BASEL, 2022, 14 (07):