Automated Diagnosis of Vertebral Fractures Using Radiographs and Machine Learning

Li Wei Cheng, Hsin Hung Chou, Kuo Yuan Huang, Chin Chiang Hsieh, Po Lun Chu, Sun Yuan Hsieh

研究成果: Conference contribution

1 引文 斯高帕斯(Scopus)

摘要

Objective: People often experience spinal fractures. The most common of these are thoracolumbar compression fractures and burst fractures. Burst fractures are usually unstable fractures, often accompanied by neurological symptoms, and thus require prompt and correct diagnosis, usually using computed tomography (CT) or magnetic resonance imaging (MRI). However, X-ray images are the cheapest and most convenient tool for predicting fracture morphological patterns. Therefore, we built a machine learning model architecture to detect and differentiate compression fractures from burst fractures using X-ray images and used CT or MRI to verify the diagnostic outcome.Methods: We used YOLO and ResUNet models to accurately segment vertebral bodies from X-ray images with 390 patients. Subsequently, we extracted features such as anterior, middle, and posterior height; height ratios; and the height ratios in relation to fractures and adjacent vertebral bodies from the segmented images. The model analyzed these features using a random forest approach to determine whether a vertebral body is normal, has a compression fracture or has a burst fracture.Results: The precision for identifying normal bodies, compression fractures, and burst fractures was 99%, 74%, and 94%, respectively. The segmentation and fracture detection results outperformed those of related studies involving X-ray images.Conclusion: We believe that this study can assist in accurate clinical diagnosis, identification, and the differentiation of spine fractures; it may help emergency room physicians in clinical decision-making, thereby improving the quality of medical care.

原文English
主出版物標題Intelligent Computing Theories and Application - 18th International Conference, ICIC 2022, Proceedings
編輯De-Shuang Huang, Kang-Hyun Jo, Junfeng Jing, Prashan Premaratne, Vitoantonio Bevilacqua, Abir Hussain
發行者Springer Science and Business Media Deutschland GmbH
頁面726-738
頁數13
ISBN(列印)9783031138690
DOIs
出版狀態Published - 2022
事件18th International Conference on Intelligent Computing, ICIC 2022 - Xi'an, China
持續時間: 2022 8月 72022 8月 11

出版系列

名字Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
13393 LNCS
ISSN(列印)0302-9743
ISSN(電子)1611-3349

Conference

Conference18th International Conference on Intelligent Computing, ICIC 2022
國家/地區China
城市Xi'an
期間22-08-0722-08-11

All Science Journal Classification (ASJC) codes

  • 理論電腦科學
  • 一般電腦科學

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