Machine Learning Based Prediction Model for Closed-Loop Small Bowel Obstruction Using Computed Tomography and Clinical Findings

Conclusions A random forest model found clinical factors including prior surgery, age, lactate, and imaging factors including whirl sign, fecalization, and U/C-shaped bowel configuration are helpful in improving the prediction of CLSBO. Individual CT findings in CLSBO had either high sensitivity or specificity, suggesting that accurate diagnosis requires systematic assessment of all CT signs.
Source: Journal of Computer Assisted Tomography - Category: Radiology Tags: Abdominopelvic Imaging: Gastrointestinal Source Type: research