Background
Neurogenic bladder dysfunction is a common and serious consequence of traumatic conus medullaris syndrome (T-CMS). Despite its clinical importance, predictive data for bladder outcomes after T-CMS remain limited. This study aimed to identify predictors of neurogenic bladder dysfunction at ≥2 years post-injury.
Methods
We retrospectively reviewed 39 patients with acute T-CMS treated at a single level I trauma center from 2004–2017 who underwent spinal surgery and had ≥2 years of follow-up. Bladder function at 2 years was categorized as complete dysfunction, incomplete dysfunction, or normal. Potential predictors included demographic factors, injury mechanisms, ASIA Impairment Scale grades, MRI timing, fracture level and type, canal diameter, occupying ratio, conus signal change (normal, edema, or edema with hemorrhage), edema length, time to surgery, and surgical approach. Univariate and multivariate analyses were performed.
Results
At final follow-up, 14 patients (35.9%) had complete bladder dysfunction, 12 (30.8%) had incomplete dysfunction, and 13 (33.3%) had normal function. Multivariate analysis identified edema with hemorrhage in the conus medullaris as the only independent predictor of bladder dysfunction.
Conclusions
Bladder dysfunction is highly prevalent after T-CMS. Hemorrhagic edema in the conus medullaris significantly increases the risk of long-term neurogenic bladder dysfunction.

Objective
To investigate the utility of a deep learning model in diagnosing traumatic lumbar fractures on computed tomography (CT) images.
Summary of Background
Data: CT scans are widely used as the first choice for detecting spinal fractures in patients with severe trauma. Although CT scans have high diagnostic accuracy, fractures can occasionally be missed. Recently, deep learning has been applied in various fields of medical imaging.
Methods
CT images from 480 patients (3695 vertebrae) who visited a level-one trauma center with lumbar fractures were retrospectively analyzed. The diagnostic results were confirmed by two experienced musculoskeletal radiologists and one experienced spine surgeon using magnetic resonance imaging (MRI). Deep learning networks were employed for diagnosis, with 425 cases used for training and 55 cases for testing. Sensitivity, specificity, accuracy, and the area under the receiver operating characteristic curve (AUROC) were calculated to evaluate diagnostic performance.
Results
The model successfully identified 107 out of 129 vertebrae with fractures, achieving a sensitivity of 82.95%, a specificity of 93.24%, an AUROC of 0.936, and an overall accuracy of 88.45%.
Conclusions
This study demonstrated that the deep learning model showed high accuracy in diagnosing traumatic lumbar fractures. This approach has the potential to assist spine specialists, radiologists, and trauma care experts. Further validation is needed to determine its effectiveness in clinical settings.