Interpretable Deep Learning Model for Pediatric Strangulated Small Bowel Obstruction on CT: A Multicenter Study.
Authors
Affiliations (7)
Affiliations (7)
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, Liaoning Province, People's Republic of China.
- Department of Hepatobiliary and Oncology Surgery, Wuhan Children's Hospital (Wuhan Maternal and Child Healthcare Hospital), Tongji Medical College, Huazhong University of Science & Technology, Wuhan, Hubei Province, People's Republic of China.
- Department of General Surgery, Shengjing Hospital of China Medical University, Shenyang, Liaoning Province, People's Republic of China.
- College of Computer Science and Technology, Jilin University, Changchun, Jilin Province, People's Republic of China.
- Department of Radiology, The First Hospital of China Medical University, Shenyang, Liaoning Province, People's Republic of China.
- Department of Pediatric Surgery, Shengjing Hospital of China Medical University, Shenyang, Liaoning Province, People's Republic of China. Electronic address: [email protected].
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, Liaoning Province, People's Republic of China. Electronic address: [email protected].
Abstract
To develop and validate a deep learning-based multi-instance learning model that integrates CT imaging and clinical data to improve the accuracy of discriminating between strangulated small bowel obstruction (StSBO) from simple small bowel obstruction (SiSBO) in pediatric patients. This multicenter retrospective study, conducted between January 2018 and June 2024, enrolled hospitalized pediatric patients aged 1 to 14 years with a diagnosis of small bowel obstruction. We developed the clinical, multi-instance learning (MIL), and combined models based on CT and clinical features. Model performance was evaluated using receiver operating characteristic (ROC) analysis, while SHapley Additive exPlanations (SHAP) interpreted feature contributions. We further assessed whether MIL-assisted diagnosis could enhance physician accuracy in diagnosing StSBO. The study sample comprised 168 patients (mean age, 6.36 ± 3.97, 118 men). Ascites and closed-loop sign were identified as independent predictors of StSBO on multivariate analysis (both p < 0.05). The MIL model achieved the area under the curve (AUC) of 0.86 (95%CI 0.70-1.00), p = 0.01 in the external test cohort. The combined model showed the highest diagnostic performance (AUC 0.87, 95%CI 0.72-1.00, p = 0.01) in the external test cohort, with MIL-derived features showing predominant importance in SHAP analysis. Both junior and experienced radiologists and surgeon demonstrated improved diagnostic performance with MIL assistance, showing AUC increases of 16%, 2%, and 20%, respectively. The MIL model performed well in diagnosing StSBO, and clinical data integration improved its performance. As a decision support tool, the model may aid risk stratification and facilitate timely escalation of care in pediatric StSBO management.