Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
Showing 3 appraisals
Journal of pediatric surgery
Interpretable deep learning model for pediatric strangulated small bowel obstruction on CT: A multicenter study
OBJECTIVE: To develop and validate a deep learning-based multi-instance learning (MIL) 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. MATERIALS AND METHODS: This multicenter retrospective study, conducted between January 2018 and June 2024, enrolled hospitalized pediatric patients aged 1-14 years with a diagnosis of small bowel obstruction. We developed the clinical, multi-instance learning, 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. RESULTS: 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. CONCLUSIONS: 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.
3 Aug 2026
Read appraisal →European radiology
Deep learning improves image quality in motion-robust and sedation-free pediatric brain MRI
OBJECTIVES: Motion and limited compliance compromise diagnostic MR image quality, particularly in pediatric patients who frequently require sedation. Single-shot sequences offer a time-efficient alternative but suffer from reduced image quality. This study aimed to evaluate the diagnostic performance of a deep learning (DL) framework combining compressed sensing (CS) and convolutional neural networks (CNNs) to enhance T2-weighted single-shot MRI (T2-SSHDL) compared with conventional CS-based reconstruction (T2-SSHconv) and routinely acquired high-resolution T2-weighted sequences. MATERIALS AND METHODS: This prospective single-center study included 62 pediatric patients (mean age, 7.4 ± 4.9 years; 36 males, 26 females), who underwent T2-weighted single-shot brain MRI (29 sedated, 33 awake). Raw data were reconstructed using a DL-based pipeline and compared with conventional CS-based reconstructions. Quantitative metrics included apparent contrast-to-noise ratio (aCNR), apparent signal-to-noise ratio (aSNR), and edge rise distance (ERD). Two radiologists rated images for artifacts, sharpness, lesion conspicuity, and overall quality on a 5-point Likert scale. RESULTS: T2-SSHDL-sequences showed significantly higher aCNR (29.9 ± 22.6 vs. 26.7 ± 16.5; p < 0.001), aSNR (41.6 ± 27.9 vs. 38.2 ± 20.8; p = 0.003), and improved sharpness (ERD 0.90 ± 0.35 mm vs. 1.35 ± 0.42 mm; p < 0.001). Qualitative assessments confirmed superior image quality, lesion conspicuity, and sharpness (p < 0.001). Compared with high-resolution T2-weighted sequences, T2-SSHDL-sequences showed fewer motion artifacts and comparable lesion conspicuity in non-sedated patients. CONCLUSION: DL-based reconstruction significantly enhances the diagnostic quality of T2-weighted single-shot brain MRI in pediatric patients, enabling clinically usable, ultrafast, motion-robust imaging with potential to reduce the need for sedation. KEY POINTS: Question Can deep learning-based reconstruction elevate motion-robust single-shot T2-weighted pediatric brain MRI to diagnostic image quality levels, enabling reliable imaging without sedation? Findings Both quantitative and qualitative evaluations confirmed significantly improved image quality of deep learning-enhanced single-shot T2-weighted brain MRI compared with conventional reconstruction. Clinical relevance Deep learning-enhanced reconstruction improves image quality in ultrafast, motion-robust single-shot pediatric brain MRI, potentially reducing the need for sedation while preserving diagnostic accuracy. This approach may enhance patient safety and shorten examination time in routine neuroimaging.
2 Aug 2026
Read appraisal →Pediatric radiology
Deep learning based gestational age estimation from multi-view fetal brain magnetic resonance imaging
BACKGROUND: Gestational age (GA) is essential for assessing fetal development, but conventional methods such as last menstrual period and ultrasound are often inaccurate, particularly in late pregnancy. Recent advances in deep learning (DL) and MRI offer more reliable and consistent GA estimation by capturing detailed fetal brain development. OBJECTIVE: This study aimed to develop deep learning models for GA prediction using multi-view fetal brain MRI and to compare their performance with conventional biometric regression techniques. MATERIALS AND METHODS: A total of 1,321 fetal MRI scans were used to train and evaluate various DL models, while an additional 80 publicly available MRI scans served as an external test set. Two training strategies were explored: transfer learning versus training from scratch, and single-view versus multi-modality input. RESULTS: The pre-trained ResNet-101 model achieved a mean absolute error (MAE) of 4.47 days and a coefficient of determination (R2) of 0.96 on the internal test set. On the external test set, the model yielded an MAE of 6.57 days, outperforming the biometric regression method, which achieved an MAE of 9.42 days. Explainability analysis revealed that the model predominantly focused on the lateral ventricles, cerebellum, and surrounding brain regions for GA prediction. CONCLUSIONS: The integration of multi-view MRI and transfer learning significantly enhanced the predictive accuracy of DL models for GA estimation. The proposed approach outperformed conventional biometric regression and highlighted clinically relevant anatomical regions, demonstrating its potential for use in prenatal diagnostic applications.
5 May 2026
Read appraisal →