ItemConference Paper

Advancements in Automated Spine Disorder Detection Using CT Scans: A Decade of Progress (20142024)

Title
Advancements in Automated Spine Disorder Detection Using CT Scans: A Decade of Progress (20142024)
Creator
Manoj, Rakshitha A.; Warrier, Gayathry S.; Kokilavani, T.; Mahalakshmi, J.; Balakrishnan, C.; Stephen, R.
Description
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.
Source
Lecture Notes in Networks and Systems;Volume;1671 LNNS;pp.443-454
Date
01-01-2026
Publisher
Springer Science and Business Media Deutschland GmbH
Subject
CT scans; Deep learning; Medical imaging; Neural networks; Spine segmentation
Coverage
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
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISSN: 23673370; ISBN: 978-981953488-3;
Format
online
Language
English
Type
Conference paper

Citation

Manoj, Rakshitha A.; Warrier, Gayathry S.; Kokilavani, T.; Mahalakshmi, J.; Balakrishnan, C.; Stephen, R., “Advancements in Automated Spine Disorder Detection Using CT Scans: A Decade of Progress (20142024),” CHRIST (Deemed To Be University) Institutional Repository, accessed June 17, 2026, https://archives.christuniversity.in/items/show/25441.

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