ORIGINAL RESEARCH article
Front. Radiol.
Sec. Artificial Intelligence in Radiology
Volume 5 - 2025 | doi: 10.3389/fradi.2025.1646008
This article is part of the Research TopicApplications and Advances of Artificial Intelligence in Medical Image Analysis: PET, SPECT/ CT, MRI, and Pathology ImagingView all 3 articles
Intervertebral Disc Anomaly Intelligent Classification System Based on Deep Learning, IDAICS
Provisionally accepted
Zhiheng Gao
Yuchen Qian
Rongkang FanYuqing YangYu WangLei Xing
Yu Chen*Yonggang Li*
Haifu Sun*
Yusen Qiao*- The First Affiliated Hospital of Soochow University, Suzhou, China
Select one of your emails
You have multiple emails registered with Frontiers:
Notify me on publication
Please enter your email address:
If you already have an account, please login
You don't have a Frontiers account ? You can register here
【Abstract】 Background: 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. Objective: 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. Methods: 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. Results: 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 (P < 0.001). Conclusion: 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.
Keywords: Intervertebral disc abnormalities, deep learning, YOLOv8-Seg, CT, automateddiagnosis, Classification Accuracy, spinal health
Received: 14 Jun 2025; Accepted: 25 Aug 2025.
Copyright: © 2025 Gao, Qian, Fan, Yang, Wang, Xing, Chen, Li, Sun and Qiao. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Yu Chen, The First Affiliated Hospital of Soochow University, Suzhou, China
Yonggang Li, The First Affiliated Hospital of Soochow University, Suzhou, China
Haifu Sun, The First Affiliated Hospital of Soochow University, Suzhou, China
Yusen Qiao, The First Affiliated Hospital of Soochow University, Suzhou, China
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.