Artificial Intelligence‐Driven Analysis for Locating Fistulas in Sacral Terminal Filar Cysts: Technical Note

Sacral terminal filar cysts often cause pain, numbness, urinary/bowel dysfunction, and sexual dysfunction due to cerebrospinal fluid (CSF) leakage at the filum terminale–dural sac junction. Accurate localization of the fistula is critical for surgical success because misidentification may lead to persistent symptoms and a requirement for reoperation. Although magnetic resonance imaging (MRI) and computed tomography (CT) are standard for diagnosis, manual localization of the fistula relies heavily on expert experience, limiting reproducibility. This study introduces an artificial intelligence (AI)‐driven approach to overcome these limitations.

This study developed a two‐stage AI algorithm using the Qwen large model for MRI–CT pattern matching. First, MRI slices were classified into three anatomical patterns (subarachnoid space, transitional, or cyst level) based on vertebral canal morphology, with the highest‐scoring pattern taken to indicate the fistula level. Second, corresponding CT slices were matched using shape features of the vertebral canal. The algorithm was validated on three typical cases from our department, with the AI‐generated localizations being compared with expert manual annotations.

The AI‐identified MRI slices (slices 12, 6, and 7 for cases 1–3, respectively) matched expert‐annotated levels. The corresponding AI‐identified CT slices for cases 1 and 2 (slices 146 and 183) aligned perfectly with the manual records, whereas that for case 3 (239) differed by only two slices (1.4 mm) from the manual one (237). Three‐dimensional rendering of the CT confirmed the fistula's transitional location between the dural sac and cyst, validating anatomical accuracy. The algorithm demonstrated high robustness in cross‐modal matching.

This study presents an AI‐based approach for locating the fistula in sacral terminal filar cysts, which shows potential for reducing expert reliance and enhancing accessibility. However, the small sample size limits the generalizability of our preliminary results, which are primarily based on descriptive comparisons and lack rigorous statistical analysis.