A deep learning-based technique for the diagnosis of epidural spinal cord compression on thoracolumbar CT

• Published in: European spine journal Vol. 32; no. 11; pp. 3815 - 3824
• Main Authors: Hallinan, James Thomas Patrick Decourcy, Zhu, Lei, Tan, Hui Wen Natalie, Hui, Si Jian, Lim, Xinyi, Ong, Bryan Wei Loong, Ong, Han Yang, Eide, Sterling Ellis, Cheng, Amanda J. L., Ge, Shuliang, Kuah, Tricia, Lim, Shi Wei Desmond, Low, Xi Zhen, Teo, Ee Chin, Yap, Qai Ven, Chan, Yiong Huak, Kumar, Naresh, Vellayappan, Balamurugan A., Ooi, Beng Chin, Quek, Swee Tian, Makmur, Andrew, Tan, Jiong Hao
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.11.2023; Springer Nature B.V
• Subjects: Compression; Deep learning; Diagnosis; Epidural; Magnetic resonance imaging; Medicine; Medicine & Public Health; Neurosurgery; Original Article; Spinal cord; Spine; Surgical Orthopedics; Metastatic epidural spinal cord compression; Deep learning; CT; Epidural spinal cord compression; MRI; Artificial intelligence

Purpose To develop a deep learning (DL) model for epidural spinal cord compression (ESCC) on CT, which will aid earlier ESCC diagnosis for less experienced clinicians. Methods We retrospectively collected CT and MRI data from adult patients with suspected ESCC at a tertiary referral institute from 2...