Please use this identifier to cite or link to this item: https://doi.org/10.3390/cancers14164025
Title: Application of Artificial Intelligence Methods for Imaging of Spinal Metastasis
Authors: Ong, Wilson
Zhu, Lei 
Zhang, Wenqiao 
Kuah, Tricia
Lim, Desmond Shi Wei
Low, Xi Zhen
Thian, Yee Liang 
Teo, Ee Chin
Tan, Jiong Hao
Kumar, Naresh 
Vellayappan, Balamurugan A 
Ooi, Beng Chin 
Quek, Swee Tian 
Makmur, Andrew
Hallinan, James Thomas Patrick Decourcy 
Keywords: Science & Technology
Life Sciences & Biomedicine
Oncology
artificial intelligence
machine learning
deep learning
spinal metastasis
imaging
applications
APPARENT DIFFUSION-COEFFICIENT
STEREOTACTIC BODY RADIOTHERAPY
PROMOTER METHYLATION STATUS
BONE METASTASES
CORD COMPRESSION
PREOPERATIVE EVALUATION
THORACOLUMBAR SPINE
CLINICAL-FEATURES
CANCER PATIENTS
SCORING SYSTEM
Issue Date: 1-Aug-2022
Publisher: MDPI
Citation: Ong, Wilson, Zhu, Lei, Zhang, Wenqiao, Kuah, Tricia, Lim, Desmond Shi Wei, Low, Xi Zhen, Thian, Yee Liang, Teo, Ee Chin, Tan, Jiong Hao, Kumar, Naresh, Vellayappan, Balamurugan A, Ooi, Beng Chin, Quek, Swee Tian, Makmur, Andrew, Hallinan, James Thomas Patrick Decourcy (2022-08-01). Application of Artificial Intelligence Methods for Imaging of Spinal Metastasis. CANCERS 14 (16). ScholarBank@NUS Repository. https://doi.org/10.3390/cancers14164025
Abstract: Spinal metastasis is the most common malignant disease of the spine. Recently, major advances in machine learning and artificial intelligence technology have led to their increased use in oncological imaging. The purpose of this study is to review and summarise the present evidence for artificial intelligence applications in the detection, classification and management of spinal metastasis, along with their potential integration into clinical practice. A systematic, detailed search of the main electronic medical databases was undertaken in concordance with the PRISMA guidelines. A total of 30 articles were retrieved from the database and reviewed. Key findings of current AI applications were compiled and summarised. The main clinical applications of AI techniques include image processing, diagnosis, decision support, treatment assistance and prognostic outcomes. In the realm of spinal oncology, artificial intelligence technologies have achieved relatively good performance and hold immense potential to aid clinicians, including enhancing work efficiency and reducing adverse events. Further research is required to validate the clinical performance of the AI tools and facilitate their integration into routine clinical practice.
Source Title: CANCERS
URI: https://scholarbank.nus.edu.sg/handle/10635/231089
ISSN: 20726694
DOI: 10.3390/cancers14164025
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