Advanced International Journal for Research

E-ISSN: 3048-7641   •   Impact Factor: 9.11

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Volume 7 Issue 3
May-June 2026

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Integrating AI with Biomedical Imaging: Advancement in Spine Cancer Diagnosis and Treatment.

Author(s) Sneha Ramrakhyani, Dr. Sohel A. Bhura
Country India
Abstract Spine cancer diagnosis predominantlydepends on magnetic resonance imaging (MRI),which provides detailed visualization of spinalstructures but requires significant expertise andtime for accurate interpretation. The complexanatomy of the spine, variations in imagingprotocols, and subtle appearance of metastaticlesions further increase diagnostic difficulty. Inrecent years, artificial intelligence (AI), particularlydeep learning and transformer-based architectures,has demonstrated considerable potential inautomating spinal image analysis and achievingperformance comparable to experiencedradiologists. This study explores the integration ofAI with biomedical imaging to improve thedetection, localization, and evaluation of spinecancer. Existing AI-based spinal imagingapproaches are reviewed to highlight their strengthsand current limitations, especially in terms ofclinical scalability and generalization. To addressthese challenges, an intelligent multi-stageframework is proposed that incorporates automatedvertebral localization, context-aware featureextraction, and spine cancer classification. Byleveraging anatomical context across multiplevertebral levels and imaging sequences, theproposed approach aims to enhance earlidentification of spinal metastases, support clinical
decision-making, and reduce radiologist workload.Evidence from previously validated AI systemsindicates that such automated pipelines can
improve diagnostic consistency, increase sensitivityto subtle pathological changes, and enhance overallefficiency in spine cancer diagnosis and management. The findings underscore the growingrole of AI as a reliable decision-support tool inmodern spinal oncology imaging.
Keywords Intelligence, Biomedical Imaging, Spine Cancer, MRI, Deep Learning, Transformers, Clinical Decision Support
Published In Volume 7, Issue 3, May-June 2026
Published On 2026-05-28
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E-ISSN 3048-7641
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