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<p>Manual processing of spinal CTA images for detecting dural arteriovenous fistulas (SDAVF) is laborious and operator-dependent. We developed an automated AI system (SDAVFdoc) that integrates a 3D convolutional neural network with anatomical prior knowledge to both identify SDAVF and localize the fistula site in a multicenter study of 718 patients. The system sequentially segments spinal structures, localizes the fistula region using anatomical priors, and finally classifies SDAVF likelihood via DenseNet within the foramina. The draining vein cluster segmentation model, using a threshold of 42.5, achieved high accuracy in distinguishing SDAVF cases, with F1-scores ranging from 0.932 to 0.960 across test sets. The DenseNet-based fistula detection model showed a high AUC of 0.928-0.954 across test sets. Compared to technicians, the system reduced processing time from 40.75 ± 11.57 min to 1.05 ± 0.29 min (<i>P</i> &lt; 0.001) and clicks from 761.80 ± 202.05 to 9.68 ± 2.12 (<i>P</i> &lt; 0.001), greatly streamlining clinical workflows. This AI-driven approach enables fast, accurate screening and localization of SDAVF.</p>

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Deep learning for fast screening and localization of spinal dural arteriovenous fistulas to enhance clinical workflow

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年2月27日

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  • Fei Zheng,
  • Xuyang Cao,
  • Jianmin Xu,
  • Nana Wang,
  • Futao Zhang,
  • Lingling Zhang,
  • Kewei Liang,
  • Li Yang,
  • Qi Guo,
  • Yali Wang,
  • Ping Yin,
  • Xuedan Feng,
  • Xuzhu Chen,
  • Nan Hong

摘要

Manual processing of spinal CTA images for detecting dural arteriovenous fistulas (SDAVF) is laborious and operator-dependent. We developed an automated AI system (SDAVFdoc) that integrates a 3D convolutional neural network with anatomical prior knowledge to both identify SDAVF and localize the fistula site in a multicenter study of 718 patients. The system sequentially segments spinal structures, localizes the fistula region using anatomical priors, and finally classifies SDAVF likelihood via DenseNet within the foramina. The draining vein cluster segmentation model, using a threshold of 42.5, achieved high accuracy in distinguishing SDAVF cases, with F1-scores ranging from 0.932 to 0.960 across test sets. The DenseNet-based fistula detection model showed a high AUC of 0.928-0.954 across test sets. Compared to technicians, the system reduced processing time from 40.75 ± 11.57 min to 1.05 ± 0.29 min (P < 0.001) and clicks from 761.80 ± 202.05 to 9.68 ± 2.12 (P < 0.001), greatly streamlining clinical workflows. This AI-driven approach enables fast, accurate screening and localization of SDAVF.