Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Manoj, Rakshitha A.; Warrier, Gayathry S.; Kokilavani, T.; Mahalakshmi, J.; Balakrishnan, C.; Stephen, R. Title A name given to the resource Advancements in Automated Spine Disorder Detection Using CT Scans: A Decade of Progress (20142024) Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Lecture Notes in Networks and Systems;Volume;1671 LNNS;pp.443-454 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-95-3489-0_33" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-95-3489-0_33</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105035619108?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105035619108?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Manoj R.A., CHRIST University, Karnataka, Bengaluru, India; Warrier G.S., CHRIST University, Karnataka, Bengaluru, India; Kokilavani T., CHRIST University, Karnataka, Bengaluru, India; Mahalakshmi J., CHRIST University, Karnataka, Bengaluru, India; Balakrishnan C., CHRIST University, Karnataka, Bengaluru, India; Stephen R., CHRIST University, Karnataka, Bengaluru, India Description An account of the resource Automated spine disorder detection has transformed a lot in the last decade, from classic segmentation techniques to advanced deep learning models. Remarkable developments can be noticed in this field, especially in developing hybrid architectures combining CNNs with LSTM networks to increase diagnostic accuracy. Recent implementations reach an accuracy of up to 97.46% and a precision of 99.72%, highlighting the achievement of impressive performance metrics by modern systems in detecting spinal deformity. Integrating U-net architectures for detecting accurate cervical spine fracture and developing two-tier detection pipelines which efficiently balance specificity and sensitivity are significant innovations. Early approaches concentrated on detecting basic anatomical features, and the latest methods comprise advanced deep learning models for comprehensive analysis. From traditional segmentation tasks to managing complicated challenges and iterative random walks, the field of automated spine disorder detection has improved significantly. However, issues regarding data standardization and model generalization persist, despite this growth. Future research should focus on the development of more robust, system-independent frameworks that are capable of handling various imaging conditions and patient populations. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. Subject The topic of the resource CT scans; Deep learning; Medical imaging; Neural networks; Spine segmentation Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 23673370; ISBN: 978-981953488-3; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online