{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T15:53:19Z","timestamp":1790092399214,"version":"4.0.1"},"reference-count":33,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,9,9]],"date-time":"2025-09-09T00:00:00Z","timestamp":1757376000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Radiol."],"abstract":"<jats:sec><jats:title>Background<\/jats:title><jats:p>Intervertebral disc anomalies, such as degeneration and herniation, are common causes of spinal disorders, often leading to chronic pain and disability. Accurate diagnosis and classification of these anomalies are critical for determining appropriate treatment strategies. Traditional methods, such as manual image analysis, are prone to subjectivity and time-consuming. With the advancements in deep learning, automated and precise classification of intervertebral disc anomalies has become a promising alternative.<\/jats:p><\/jats:sec><jats:sec><jats:title>Objective<\/jats:title><jats:p>This study aims to propose a deep learning-based method for classifying intervertebral disc abnormalities, with the goal of improving diagnostic accuracy and clinical efficiency in spinal health management.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>From August 2021 to March 2024, a dataset consisting of 574 CT images of intervertebral discs was collected and labeled into four clinically relevant categories: normal intervertebral discs, Schmorl's nodes, disc bulges, and disc protrusions. The dataset was divided into 500 images for model training, and 74 images for validation. A YOLOv8-seg network was employed for classification, with multiple preprocessing techniques applied to ensure data consistency and enhance model performance.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The IDAICS demonstrated high accuracy in classifying various intervertebral disc anomalies, including disc degeneration, herniation, and bulging, with a classification accuracy of over 93.2%, with a kappa coefficient of 0.905 (<jats:italic>P<\/jats:italic>\u2009&amp;lt;\u20090.001).<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>This deep learning-based classification approach provides an efficient and reliable alternative to manual assessment, enabling automated diagnosis of intervertebral disc abnormalities. It offers significant potential to enhance clinical decision-making and improve spinal health management outcomes.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fradi.2025.1646008","type":"journal-article","created":{"date-parts":[[2025,9,9]],"date-time":"2025-09-09T05:26:38Z","timestamp":1757395598000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Intervertebral disc anomaly intelligent classification system based on deep learning, 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