Objective To develop deep learning models for colonoscopy quality control using various deep learning architectures,and to delve into the decision-making mechanisms.Methods The colonoscopy images were selected from two datasets separately constructed by the HyperKvasir and Changshu Hospital Affiliated to Soochow University,encompassing intestines of varying degrees of cleanliness,polyps,and cecums.After image preprocessing and enhancement,transfer learning was carried out using the pre-trained models based on convolutional neural network(CNN)and Transformer.The model training adopted cross-entropy loss functions and Adam optimizer,and simultaneously implemented learning rate scheduling.To enhance model transparency,a thorough interpretability analysis was conducted using Grad-CAM,Guided Grad-CAM,and SHAP.The final model was converted to ONNX format and deployed on various equipment terminals to achieve real-time colonoscopy quality control.Results In a dataset of 3 831 colonoscopy images,EfficientNet model outperformed the other models on the test set,achieving an accuracy of 0.992 which was higher than those of the other models based on CNN(DenseNet121,ResNet50,VGG19)and Transformer(ViT,Swin,CvT),with a precision,recall rate,and F1 score of 0.991,0.989,and 0.990.On an external test set of 358 images,EfficientNet model had an average AUC,precision,and recall rate of 0.996,0.948,and 0.952,respectively.Although EfficientNet model is high-performing,some misjudgments still occurred.Interpretability analysis highlighted key image areas affecting decision-making.In addition,EfficientNet model was successfully converted to ONNX format and deployed on multiple platforms and devices,and it ensured real-time colonoscopy quality control with an inference speed of over 60 frames per second.Conclusion Among the 7 models developed for colonoscopy quality control based on CNN and Transformer,EfficientNet demonstrated exemplary performance across all categories and is deployed for real-time predictions on multiple terminals,aiming to provide patients with better medical care.

Objective Given the extensive application of machine learning(ML)in medical models and its remarkable learning and generalization capabilities,this study employed automated ML(AutoML)combined with patient demographics and clinical conditions to early assess the risk of failure in bowel preparation prior to colonoscopy.Methods A retrospective analysis was conducted on patients who underwent colonoscopy examinations in Hospital 1 and Hospital 2 from January 2022 to January 2023,and their general and clinical information was collected.According to the Boston bowel preparation scale(BBPS),a BBPS of≤5 was defined as a failure in bowel preparation,>5 was deemed satisfactory.From the data of the two hospitals,we randomly divided the dataset into a training set(n=303)and a validation set(n=76)at an 8∶2 ratio.Least absolute shrinkage and selection operator(LASSO)logistic regression(LR)model was used for feature selection,a nomogram scoring system was constructed,and models were established using AutoML based on five algorithms.Model performance was evaluated through receiver operator characteristic curve(ROC curve),calibration curves,LR-based decision curve analysis(DCA),SHAP plots,and force plots.Results Among the 379 patients,105 cases(27.7%)experienced bowel preparation failure(BBPS≤5).21 study variables were narrowed down to 10 through LASSO with 5-fold cross-validation,resulting in the development of a Nomogram chart with demonstrated reliability via calibration curves.Using the H2O platform and five algorithms[gradient boosting machine(GBM),deep learning(DL),generalized linear model(GLM),Stacked Ensemble and distributed random forest(DRF)],67 models were developed.Stacked Ensemble outperformed the others with an area under the curve(AUC)of 0.871,LogLoss of 0.403,and RMSE of 0.354,surpassing traditional LR model and other models.Variable importance contribution plots indicated significant predictive influences from factors such as the interval between laxative ingestion and examination,history of constipation,completion of laxative regimen,age,and presence of a companion during the procedure.Finally,SHAP plots and force plots revealed variable distribution patterns in binary classification predictions and the impact of variables on predictive outcomes.Conclusion The AutoML model based on the Stacked Ensemble algorithm exhibits clear clinical utility in early prediction of bowel preparation failure risk.Moreover,a clinically applicable column chart scoring tool is constructed.

Objective·To compare the performance of various deep learning methods in the segmentation and classification of colorectal polyp endoscopic images,and identify the most effective approach.Methods·Four colorectal polyp datasets were collected from three hospitals,encompassing 1 534 static images and 15 videos.All samples were pathologically validated and categorized into two types:serrated lesions and adenomatous polyps.Polygonal annotations were performed by using the LabelMe tool,and the annotated results were converted into integer mask formats.These data were utilized to train various architectures of deep neural networks,including convolutional neural network(CNN),Transformers,and their fusion,aiming to develop an effective semantic segmentation model.Multiple performance indicators for automatic diagnosis of colon polyps by different architecture models were compared,including mIoU,aAcc,mAcc,mDice,mFscore,mPrecision and mRecall.Results·Four different architectures of semantic segmentation models were developed,including two deep CNN architectures(Fast-SCNN and DeepLabV3plus),one Transformer architecture(Segformer),and one hybrid architecture(KNet).In a comprehensive performance evaluation of 291 test images,KNet achieved the highest mIoU of 84.59％,significantly surpassing Fast-SCNN(75.32％),DeepLabV3plus(78.63％),and Segformer(80.17％).Across the categories of"background","serrated lesions"and"adenomatous polyps",KNet's intersection over union(IoU)were 98.91％,74.12％,and 80.73％,respectively,all exceeding other models.Additionally,KNet performed excellently in key performance metrics,with aAcc,mAcc,mDice,mFscore,and mRecall reaching 98.59％,91.24％,91.31％,91.31％,and 91.24％,respectively,all superior to other models.Although its mPrecision of 91.46％was not the most outstanding,KNet's overall performance remained leading.In inference testing on 80 external test images,KNet maintained an mIoU of 81.53％,demonstrating strong generalization capabilities.Conclusion·The semantic segmentation model of colorectal polyp endoscopic images constructed by deep neural network based on KNet hybrid architecture,exhibits superior predictive performance,demonstrating its potential as an efficient tool for detecting colorectal polyps.

This article interpretsthe expert consensus on venous catheter maintenance, including flushing and locking tube, dressing replacement and catheter maintenance, infusion connectors, catheter removal, education and training, infection control, etc. It is hoped that the expert consensus can provide a reference for clinical nurses to standardize the operations of intravenous catheter maintenance, thereby benefiting patients.

Objective: To evaluate the accuracy of the new non-invasive liver disease model platelet-albumin-bilirubin index (PALBI) in the diagnosis of patients with acute upper gastrointestinal hemorrhage (AUGIB) due to cirrhosis. Methods: 277 patients with AUGIB due to cirrhosis were analyzed retrospectively. The data of platelet, total bilirubin, albumin, creatinine, international standardized ratio and etiology of cirrhosis were collected. Univariate and multivariate logistic regression analysis was used to identify independent risk factors for death in patients with cirrhosis complicated by AUGIB. Analysis of variance was used to compare the differences between the model for end-stage liver disease (MELD) and PALBI grades. Pearson correlation analysis was used to assess the association between MELD and PALBI. The operating characteristic curve (ROC) was used to compare the predictive power of both for short-term and long-term mortality in patients with cirrhosis complicated by AUGIB. Results: The short-term and long-term mortality rates of patients with cirrhosis complicated by AUGIB were 13.7% and 23.5%, respectively. The average hospital stay was (9.1±3.9)days. The high MELD score and high PALBI index were confirmed as independent risk factors of death by single factor and multiple factors [odds ratio (OR)=1.17, 4.43; P<0.05]; the Pearson correlation analysis showed there was a positive correlation between MELD score and PALBI index (r=0.735, P<0.05). The PALBI score was scored in MELD-a patients, further subdivided into PALBI-1a and PALBI-1b. There were statistical difference in the 1-year mortality rate between the two groups (7.0% vs 17.8%, χ²=4.033, P<0.05). The ROC curve was used to compare the predictive power of MELD and PALBI for short-term mortality. The area under curve (AUC) of PALBI was 0.767 (95% CI: 0.712-0.815), while the AUC of the MELD score was 0.651 (95% CI: 0.591-0.707), with statistically significant difference (Z=2.328, P<0.05). The predictive power of PALBI and MELD for long-term mortality were 0.731(95% CI: 0.674-0.782), 0.754 (95% CI: 0.699-0.804), but the difference was not statistically significant (Z=0.828, P>0.05). Conclusions PALBI has a better predictive effect on patients with cirrhosis complicated by AUGIB than MELD scores. PALBI can achieve a more precise prognosis classification for patients with MELD-a, and maintain a good prediction ability on the short-term (within 30 days of hospitalization and discharge) and long-term (within 1 year after discharge) mortality of patients. As a new liver disease model, PALBI can be used as an effective non-invasive means to judge the prognosis of patients with liver cirrhosis complicated by AUGIB .

ObjectiveTo develop a predictive model and application for spontaneous passage of common bile duct stones using automated machine learning algorithms given the complexity of treatment decision-making for patients with common bile duct stones， and to reduce unnecessary endoscopic retrograde cholangiopancreatography （ERCP） procedures. MethodsA retrospective analysis was performed for the data of 835 patients who were scheduled for ERCP after a confirmed diagnosis of common bile duct stones based on imaging techniques in Changshu First People’s Hospital （dataset 1） and Changshu Traditional Chinese Medicine Hospital （dataset 2）. The dataset 1 was used for the training and internal validation of the machine learning model and the development of an application， and the dataset 2 was used for external testing. A total of 22 potential predictive variables were included for the establishment and internal validation of the LASSO regression model and various automated machine learning models. The area under the receiver operating characteristic curve （AUC）， sensitivity， specificity， and accuracy were used to assess the performance of models and identify the best model. Feature importance plots， force plots， and SHAP plots were used to interpret the model. The Python Dash library and the best model were used to develop a web application， and external testing was conducted using the dataset 2. The Kolmogorov-Smirnov test was used to examine whether the data were normally distributed， and the Mann-Whitney U test was used for comparison between two groups， while the chi-square test or the Fisher’s exact test was used for comparison of categorical data between groups. ResultsAmong the 835 patients included in the study， 152 （18.20%） experienced spontaneous stone passage. The LASSO model achieved an AUC of 0.875 in the training set （n=588） and 0.864 in the validation set （n=171）， and the top five predictive factors in terms of importance were solitary common bile duct stones， non-dilated common bile duct， diameter of common bile duct stones， a reduction in serum alkaline phosphatase （ALP）， and a reduction in gamma-glutamyl transpeptidase （GGT）. A total of 55 models were established using automated machine learning， among which the gradient boosting machine （GBM） model had the best performance， with an AUC of 0.891 （95% confidence interval： 0.859‍ ‍—‍ ‍0.927）， outperforming the extreme randomized tree mode， the deep learning model， the generalized linear model， and the distributed random forest model. The GBM model had an accuracy of 0.855， a sensitivity of 0.846， and a specificity of 0.857 in the test set （n=76）. The variable importance analysis showed that five factors had important influence on the prediction of spontaneous stone passage， i.e.， were solitary common bile duct stones， non-dilated common bile duct， a stone diameter of <8 mm， a reduction in serum ALP， and a reduction in GGT. The SHAP analysis of the GBM model showed a significant increase in the probability of spontaneous stone passage in patients with solitary common bile duct stones， non-dilated common bile duct， a stone diameter of <8 mm， and a reduction in serum ALP or GGT. ConclusionThe GBM model and application developed using automated machine learning algorithms exhibit excellent predictive performance and user-friendliness in predicting spontaneous stone passage in patients with common bile duct stones. This application can help avoid unnecessary ERCP procedures， thereby reducing surgical risks and healthcare costs.