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Background Renal damage in closed spinal dysraphism (CSD), primarily linked to neurogenic bladder dysfunction, significantly impacts long-term patient outcomes by increasing the risk of chronic kidney disease. Identifying patients at highest risk for renal damage is essential for implementing early interventions, improving bladder management strategies, and preserving renal function. This study aims to develop an effective machine learning model to predict renal damage in children with CSD. Methods This retrospective study included 110 children with CSD. We developed four machine learning models (logistic regression, support vector machine, decision tree, and extreme gradient boosting [XGBoost]), and compared their predictive performances. The area under the receiver operating characteristic curve (AUC), calibration curve, and decision curve analysis were used to evaluate predictive performance. The Shapley additive explanations (SHAP) algorithm and Local Interpretable Model-Agnostic Explanations (LIME) were used to interpret the optimal model. Results The XGBoost model showed the best predictive performance (AUC = 0.957) among the four machine learning models. Through the SHAP analysis, abnormal radiological lower urinary tract findings, female sex, and high-grade vesicoureteral reflux were identified as the three most influential features in predicting renal damage. Conclusion Our study effectively developed a model that accurately predicted renal damage in children with CSD based on the XGBoost algorithm, demonstrating its potential to achieve good predictive performance.

Background X-rays are the most commonly used method for diagnosing Legg–Calvé–Perthes disease (LCPD) in children. However, in early-stage LCPD, X-rays only show subtle changes, making diagnosis highly dependent on the radiologist’s experience. The aim of this study was to develop and validate a combined radiomics and deep learning model based on anteroposterior and frog-leg lateral X-rays for identifying early LCPD patients. Methods We retrospectively collected imaging data of children diagnosed with early LCPD and normal control groups from two centers between January 2013 and December 2023. Logistic regression (LR), Support vector machine (SVM), and Extreme gradient boosting (XGBoost) algorithms were used to build radiomics and Deep Learning (DL) models, and the performance of individual models was compared. The development of the ensemble model involved a stacking strategy to integrate the best radiomics and DL models. The diagnostic performance of the combined model was compared with that of radiologists of varying experience levels. Model evaluation was conducted using metrics such as the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity. Finally, the model was validated using calibration and clinical decision curves. Results A total of 200 early LCPD hips (Center A, n  = 157; Center B, n  = 43) and 236 normal hips (Center A, n  = 188; Center B, n  = 48) were included. Among the individual radiomics and DL models, the XGBoost algorithm performed the best: the radiomics model achieved an AUC of 0.845 (95% CI, 0.758–0.933), and the DL model achieved an AUC of 0.848 (95% CI, 0.766–0.929). The ensemble model’s performance was further improved, with an AUC of 0.878 (95% CI, 0.810, 0.945). The combined model significantly outperformed junior radiologists. Calibration and clinical decision curves demonstrated that the combined model had high predictive value. Conclusion The integrated radiomics and DL model using both anteroposterior and frog-leg lateral X-rays demonstrated superior performance over individual radiomics or DL approaches, highlighting its potential as an effective tool for early screening of LCPD.

Aim To develop a decision-support tool for predicting extubation failure (EF) in neonates with bronchopulmonary dysplasia (BPD) using a set of machine-learning algorithms. Methods A dataset of 284 BPD neonates on mechanical ventilation was used to develop predictive models via machine-learning algorithms, including extreme gradient boosting (XGBoost), random forest, support vector machine, naïve Bayes, logistic regression, and k-nearest neighbor. The top three models were assessed by the area under the receiver operating characteristic curve (AUC), and their performance was tested by decision curve analysis (DCA). Confusion matrix was used to show the high performance of the best model. The importance matrix plot and SHapley Additive exPlanations values were calculated to evaluate the feature importance and visualize the results. The nomogram and clinical impact curves were used to validate the final model. Results According to the AUC values and DCA results, the XGboost model performed best (AUC = 0.873, sensitivity = 0.896, specificity = 0.838). The nomogram and clinical impact curve verified that the XGBoost model possessed a significant predictive value. The following were predictive factors for EF: pO 2 , hemoglobin, mechanical ventilation (MV) rate, pH, Apgar score at 5 min, FiO 2 , C-reactive protein, Apgar score at 1 min, red blood cell count, PIP, gestational age, highest FiO 2 at the first 24 h, heart rate, birth weight, pCO 2 . Further, pO 2 , hemoglobin, and MV rate were the three most important factors for predicting EF. Conclusions The present study indicated that the XGBoost model was significant in predicting EF in BPD neonates with mechanical ventilation, which is helpful in determining the right extubation time among neonates with BPD to reduce the occurrence of complications.

Purposes To develop a predictive model for recurrent intussusception based on abdominal ultrasound (US) images and abdominal radiographs. Methods A total of 3665 cases of intussusception were retrospectively collected from January 2017 to December 2022. The cohort was randomly assigned to training and validation sets at a 6:4 ratio. Two types of images were processed: abdominal grayscale US images and abdominal radiographs. These images served as inputs for the deep learning algorithm and were individually processed by five detection models for training, with each model predicting its respective categories and probabilities. The optimal models were selected individually for decision fusion to obtain the final predicted categories and their probabilities. Results With US, the VGG11 model showed the best performance, achieving an area under the receiver operating characteristic curve (AUC) of 0.669 (95% CI: 0.635–0.702). In contrast, with radiographs, the ResNet18 model excelled with an AUC of 0.809 (95% CI: 0.776–0.841). We then employed two fusion methods. In the averaging fusion method, the two models were combined to reach a diagnostic decision. Specifically, a soft voting scheme was used to average the probabilities predicted by each model, resulting in an AUC of 0.877 (95% CI: 0.846–0.908). In the stacking fusion method, a meta-model was built based on the predictions of the two optimal models. This approach notably enhanced the overall predictive performance, with LightGBM emerging as the top performer, achieving an AUC of 0.897 (95% CI: 0.869–0.925). Both fusion methods demonstrated excellent performance. Conclusions Deep learning algorithms developed using multimodal medical imaging may help predict recurrent intussusception. Clinical trial number Not applicable.

Background Correctly diagnosing and accurately distinguishing mycoplasma pneumonia in children has consistently posed a challenge in clinical practice, as it can directly impact the prognosis of affected children. To address this issue, we analyzed chest X-rays (CXR) using various deep learning models to diagnose pediatric mycoplasma pneumonia. Methods We collected 578 cases of children with mycoplasma infection and 191 cases of children with virus infection, with available CXR sets. Three deep convolutional neural networks (ResNet50, DenseNet121, and EfficientNetv2-S) were used to distinguish mycoplasma pneumonia from viral pneumonia based on CXR. Accuracy, area under the curve (AUC), sensitivity, and specificity were used to evaluate the performance of the model. Visualization was also achieved through the use of Class Activation Mapping (CAM), providing more transparent and interpretable classification results. Results Of the three models evaluated, ResNet50 outperformed the others. Pretrained with the ZhangLabData dataset, the ResNet50 model achieved 80.00% accuracy in the validation set. The model also showed robustness in two test sets, with accuracy of 82.65 and 83.27%, and AUC values of 0.822 and 0.758. In the test results using ImageNet pre-training weights, the accuracy of the ResNet50 model in the validation set was 80.00%; the accuracy in the two test sets was 81.63 and 62.91%; and the corresponding AUC values were 0.851 and 0.776. The sensitivity values were 0.884 and 0.595, and the specificity values were 0.655 and 0.814. Conclusions This study demonstrates that deep convolutional networks utilizing transfer learning are effective in detecting mycoplasma pneumonia based on chest X-rays (CXR). This suggests that, in the near future, such computer-aided detection approaches can be employed for the early screening of pneumonia pathogens. This has significant clinical implications for the rapid diagnosis and appropriate medical intervention of pneumonia, potentially enhancing the prognosis for affected children.

ObjectiveThe aim of this study was to assess the frequency of clinical features and pathological lead points in recurrent intussusception, with a special focus on the risk factors that lead to recurrent intussusception.DesignThis is a retrospective cohort study. A 5-year retrospective study was performed between January 2012 and July 2016 in the Children’s Hospital of Soochow University, Suzhou, China, to determine the clinical features and pathological lead points of recurrent intussusception.SettingThis is a retrospective chart review of recurrent intussusception cases in a large university teaching hospital.ParticipantsThe medical records were obtained for 1007 cases with intussusception, including demographics, clinical signs and symptoms, imaging and recurrence times if available.InterventionsUnivariate and multivariate logistic regression analyses were used to measure significant factors affecting recurrent intussusception and recurrent intussusception with pathological lead points.ResultsThere were 481 total episodes of recurrence in 191 patients. Among these, 87 had one recurrence and 104 had multiple recurrences. After comparing recurrent and non-recurrent intussusception cases using univariate analysis, it was determined that the factors associated with recurrent intussusception were age (>1 year), duration of symptoms (≤12 hours), the lack of bloody stool, paroxysmal crying or vomiting, the mass location (right abdomen) and pathological lead point (P<0.05). Age (>1 year), duration of symptoms (≤12 hours), the absence of vomiting, mass location (right abdomen) and pathological lead point were significantly independently predictive of recurrent intussusception. The factors associated with recurrent intussusception with lead points present were vomiting and mass location in the right abdomen (P<0.05). Vomiting and mass location (left abdomen) were significantly predictive of recurrent intussusception with lead points.ConclusionsAge (>1 year), symptom duration (≤12 hours), the absence of vomiting, mass location (right abdomen) and pathological lead points were significantly predictive of recurrent intussusception. Vomiting and mass location (left abdomen) were significantly predictive of recurrent intussusception with lead points.

ObjectivesThe objective of this study was to identify risk factors for enema reduction failure and to establish a combined model that integrates deep learning (DL) features and clinical features for predicting surgical intervention in intussusception in children younger than 8 months of age.DesignA retrospective study with a prospective validation cohort of intussusception.Setting and dataThe retrospective data were collected from two hospitals in south east China between January 2017 and December 2022. The prospective data were collected between January 2023 and July 2024.ParticipantsA total of 415 intussusception cases in patients younger than 8 months were included in the study.Methods280 cases collected from Centre 1 were randomly divided into two groups at a 7:3 ratio: the training cohort (n=196) and the internal validation cohort (n=84). 85 cases collected from Centre 2 were designed as external validation cohort. Pretrained DL networks were used to extract deep transfer learning features, with least absolute shrinkage and selection operator regression selecting the non-zero coefficient features. The clinical features were screened by univariate and multivariate logistic regression analyses. We constructed a combined model that integrated the selected two types of features, along with individual clinical and DL models for comparison. Additionally, the combined model was validated in a prospective cohort (n=50) collected from Centre 1.ResultsIn the internal and external validation cohorts, the combined model (area under curve (AUC): 0.911 and 0.871, respectively) demonstrated better performance for predicting surgical intervention in intussusception in children younger than 8 months of age than the clinical model (AUC: 0.776 and 0.740, respectively) and the DL model (AUC: 0.828 and 0.793, respectively). In the prospective validation cohort, the combined model also demonstrated impressive performance with an AUC of 0.890.ConclusionThe combined model, integrating DL and clinical features, demonstrated stable predictive accuracy, suggesting its potential for improving clinical therapeutic strategies for intussusception.

Background Pulmonary hemorrhage (PH) in respiratory distress syndrome (RDS) in extremely preterm infants exhibits a high mortality rate and poor long-term outcomes. The aim of the present study was to develop a machine learning (ML) predictive model for RDS with PH in extremely preterm infants. Methods We performed a retrospective analysis of extremely preterm infants with RDS at the Children’s Hospital of Soochow University between January 2015 and January 2021. We applied three ML algorithms—logistic regression (LR), random forest (RF), and extreme gradient boosting (XGBoost)—to evaluate the performance of each model using the area under the curve (AUC), and developed a predictive model based on the optimal model. We calculated SHapley Additive exPlanations (SHAP) values to determine variables importance and show visualization results, and constructed a nomogram for individualized prediction. Results A total of 309 patients with RDS were enrolled, including 48 (15.5%) with PH. A total of 29 variables were collected, including demographic and clinical characteristics, laboratory data, and image classification. According to the AUC values, the RF model performed best (AUC = 0.868). Based on the SHAP values, the top five important variables in the RF model were gestational age, PaO 2 /FiO 2 , birth weight, mean platelet volume, and Apgar score at 5 min. Conclusions Our study showed that the RF model could be used to predict the risk of PH in RDS in extremely preterm infants. The nomogram provides clinicians with an effective tool for early warning and timely management.

Background To identify radiomic features that can predict the pathological type of neuroblastic tumor in children. Methods Data on neuroblastic tumors in 104 children were retrospectively analyzed. There were 14 cases of ganglioneuroma, 24 cases of ganglioneuroblastoma, and 65 cases of neuroblastoma. Stratified sampling was used to randomly allocate the cases into the training and validation sets in a ratio of 3:1. The maximum relevance–minimum redundancy algorithm was used to identify the top 10 of two clinical features and 851 radiomic features in portal venous–phase contrast-enhanced computed tomography images. Least absolute shrinkage and selection operator regression was used to classify tumors in two binary steps: first as ganglioneuroma compared to the other two types, then as ganglioneuroblastoma compared to neuroblastoma. Results Based on 10 clinical-radiomic features, the classifier identified ganglioneuroma compared to the other two tumor types in the validation dataset with sensitivity of 100.0%, specificity of 81.8%, and an area under the receiver operating characteristic curve (AUC) of 0.875. The classifier identified ganglioneuroblastoma versus neuroblastoma with a sensitivity of 83.3%, a specificity of 87.5%, and an AUC of 0.854. The overall accuracy of the classifier across all three types of tumors was 80.8%. Conclusion Radiomic features can help predict the pathological type of neuroblastic tumors in children.

PurposeThe aim of the present study was to identify the hepatic vascular variations with visual three-dimensional (3D) reconstruction of vessels in pediatric patients with choledochal cyst (CDC).MethodsWe retrospectively analyzed the data of 84 children with pathologically confirmed CDCs treated in the Children's Hospital of Soochow University. 180 patients without CDCs as a control to analysis the hepatic artery and portal vein anatomy. All patients were examined by multi-slice spiral CT (MSCT) and the images of children with CDC were reconstructed by Hisense computer-assisted surgery system (Hisense CAS) to obtain visual 3D images.ResultsThere were 71 females and 13 males diagnosed with CDC. According to Todani classification of CDC, there were 42 cases of type Ia, 10 cases of type Ic and 32 cases of type IVa. There were 10 (11.9%) patients with hepatic artery variations, 14 (16.7%) patients with right hepatic artery located on the ventral side of the CDC, and 16 (19.0%) patients with portal vein variations. Sex, age and types of the cyst were not associated with the presence of vascular variations. There was no significant difference in hepatic vascular variation between CDCs and control groups. Visual 3D images clearly displayed the hepatic vascular variations and the spatial structure of the CDC in pediatric patients with CDC.ConclusionsHepatic artery and portal vein variations can be detected in pediatric patients with CDC. Visual 3D technique can visually and stereoscopically display the anatomical variations of the hepatic artery and portal vein.