Back pain is one of the major musculoskeletal pain problems that can affect many people and is considered as one of the main causes of disability all over the world. Lower back pain, which is the most common type of back pain, is estimated to affect at least 60% to 80% of the adult population in the United Kingdom at some time in their lives. Some of those patients develop a more serious condition namely Chronic Lower Back Pain in which physicians must carry out a more involved diagnostic procedure to determine its cause. In most cases, this procedure involves a long and laborious task by the physicians to visually identify abnormalities from the patient’s Magnetic Resonance Images. Limited technological advances have been made in the past decades to support this process. This paper presents a comprehensive literature review on these technological advances and presents a framework of a methodology for diagnosing and predicting Chronic Lower Back Pain. This framework will combine current state-of-the-art computing technologies including those in the area of artificial intelligence, physics modelling, and computer graphics, and is argued to be able to improve the diagnosis process.
|Title of host publication||Intelligent Computing Theories and Application|
|Editors||De-Shuang Huang, Vitoantonio Bevilacqua, Prashan Premaratne|
|Number of pages||12|
|Publication status||Published - 2016|
|Name||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
Al Kafri, A. S., Sudirman, S., Hussain, A. J., Fergus, P., Al-Jumeily, D., Al-Jumaily, M., & Al-Askar, H. (2016). A framework on a computer assisted and systematic methodology for detection of chronic lower back pain using artificial intelligence and computer graphics technologies. In D-S. Huang, V. Bevilacqua, & P. Premaratne (Eds.), Intelligent Computing Theories and Application (pp. 843-854). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9771). Springer-Verlag. https://doi.org/10.1007/978-3-319-42291-6_83