Objective:To construct and validate a predictive model and application program for stone recurrence after endoscopic retrograde cholangiopancreatography (ERCP) based on machine learning algorithms in patients with common bile duct stones (CBDS).Methods:A multicenter retrospective cohort study was conducted, 862 CBDS patients underwent ERCP from June 2020 to September 2023 in Changshu First People′s Hospital (data set 1, 759 cases, including a training set of 588 cases and a validation set of 171 cases) and Changshu Hospital of Traditional Chinese Medicine (data set 2, 103 cases, used as a test set). The demographics, medical history, ERCP procedural records and laboratory indices were collected. All patients were followed up for 1 year, and the stone recurrence was recorded. In training set, the feature selection was conducted by the least absolute shrinkage and selection operator (LASSO) algorithm, and a conventional Logistic regression model was constructed based on selected features. The 3 machine learning algorithms (gradient boosting machine model, extreme gradient boosting model and random forest model) and a conventional Logistic regression model (LASSO model) were trained to fit predictive models. The model performance was assessed by area under curve (AUC) of receiver operating characteristic curve. The model interpretability was analyzed by feature importance evaluation, Shapley additive explanations (SHAP) and force plots. The best-performing model was deployed as an online application by Streamlit framework (V1.36.0).Results:Among the 862 patients, 158 patients (18.33%) developed stone recurrence after ERCP. There were no statistical difference in demographics, medical history, ERCP procedural records and laboratory indices between training set and a validation set ( P>0.05). LASSO regression analysis result showed that 6 key variables (in descending order of significance: endoscopic sphincterotomy, common bile duct angulation, stone diameter, stone count, common bile duct diameter, and periampullary diverticulum) influencing stone recurrence. ROC curve analysis result showed that the random forest model exhibited the highest predictive performance (it had the largest AUC of 0.900). SHAP analysis result showed that common bile duct angulation, common bile duct diameter, stone diameter, endoscopic sphincterotomy and stone count were the top 5 contributing factors in the random forest model. Using Python, the random forest model was implemented into a Streamlit-based application with a user-friendly visual interface, providing predictive outcomes, confidence levels, SHAP force diagram and health recommendations. In the test set, the application program achieved an accuracy of 84.5% (87/103), sensitivity of 82.6% (19/23), and specificity of 85.0% (68/80). SHAP plots and force diagram intuitively illustrated the impact of key features on stone recurrence prediction, offering a clear visualization of each variable′s role within the model. Conclusions:The predictive model and application program based on the random forest machine learning algorithms demonstrate excellent predictive performance and practical usability in predicting stone recurrence after ERCP in patients with CBDS.

Objective To construct YOLOv10 based artificial intelligence(AI)models for the automatic detection in small bowel capsule endoscopy(SBCE)images.Methods SBCE data from two centers was collected,including 23 115 images and 35 412 annotated labels covering 11 categories of small bowel lesions.The images were annotated using the LabelMe tool and converted into the YOLO format required for deep learning model development.The pre-trained YOLOv10 and YOLOv8 models were used for transfer learning training on the constructed dataset.Model performance was comprehensively evaluated using metrics such as precision,accuracy,sensitivity,specificity,false-positive rate,and detection speed.Finally,the models were deployed on local computers for real-time detection of SBCE images and videos.Results Six different versions of YOLO object detection models were developed,namely YOLOv8n,YOLOv8s,YOLOv8m,YOLOv10n,YOLOv10s,and YOLOv10m.On the validation set,YOLOv10s model achieved the best mAP50(0.795);although its inference latency was not the fastest(4.803 ms/img),it met the requirements for clinical application.On the test set,YOLOv10s performed well,with an accuracy of 92.69%,a sensitivity of 89.23%,and a false-positive rate of 4.78%.Especially,in category-specific inference,the highest sensitivity was for"bleeding"at 96.41%,while the lowest was for"narrowing"at 82.29%.Conclusion The model constructed based on YOLOv10 neural network can rapidly and accurately detect and classify various small bowel lesions,exhibiting significant clinical application potential.

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.

ObjectiveTo investigate the independent risk factors for poor prognosis in patients with acute pancreatitis （AP） by analyzing inflammatory factors， lung ultrasound （LUS） scores， and CT scores， to establish a nomogram prediction model， and to provide a basis for early clinical intervention. MethodsA total of 409 patients with AP who were admitted to Changshu Hospital Affiliated to Soochow University from January 2021 to October 2023 were enrolled as subjects， and they were divided into modeling group with 288 patients and validation group with 121 patients using the simple random sampling method at a ratio of 7∶3. According to the prognosis， each group was further divided into poor prognosis group and good prognosis group. The levels of C-reactive protein （CRP）， procalcitonin （PCT）， interleukin-6 （IL-6）， interleukin-10 （IL-10）， and tumor necrosis factor-α （TNF-α） were measured for both groups within 72 hours after admission， and LUS scores， modified CT severity index （MCTSI）， and extrapancreatic inflammation on computed tomography （EPIC） scores were assessed within 48‍ ‍—‍ ‍72 hours after admission. The independent-samples t test was used for comparison of normally distributed continuous data between groups， and the Mann-Whitney U rank sum test was used for comparison of non-normally distributed continuous data between groups； the chi-square test was used for comparison of categorical data between groups. A LASSO regression analysis was used to screen for the variables that were included in the multivariate logistic regression model to identify the independent risk factors for the poor prognosis of AP， and then a nomogram prediction model was established. The receiver operating characteristic （ROC） curve and the calibration curve were used to assess the discriminatory ability and goodness of fit of the nomogram model， and a decision curve analysis was used to assess the clinical applicability of the model. ResultsAmong the 288 patients with AP in the modeling group， there were 33 （11.46%） in the poor prognosis group and 255 （88.54%） in the good prognosis group； among the 121 patients with AP in the validation group， there were 13 （10.74%） in the poor prognosis group and 108 （89.26%） in the good prognosis group. Compared with the good prognosis group， the poor prognosis group had significantly higher levels of CRP （Z=3.607， P<0.05）， IL-6 （Z=4.189， P<0.05）， and TNF-α （t=2.584， P<0.05）， and significantly higher scores of LUS （t=8.075， P<0.05）， MCTSI （t=5.929， P<0.05）， and EPIC （t=8.626， P<0.05）. The multivariate logistic regression analysis showed that CRP （odds ratio ［OR］=3.592， 95% confidence interval ［CI］： 1.272‍ ‍—‍ ‍10.138， P<0.05）， IL-6 （OR=4.225， 95%CI： 1.468‍ ‍—‍ ‍12.156， P<0.05）， TNF-α （OR=3.540， 95%CI： 1.205‍ ‍—‍ ‍10.401， P<0.05）， LUS （OR=7.094， 95%CI： 2.398‍ ‍—‍ ‍20.986， P<0.05）， MCTSI （OR=7.612， 95%CI： 2.832‍ ‍—‍ ‍20.462， P<0.05）， and EPIC （OR=11.915， 95%CI： 4.007‍ ‍—‍ ‍35.432， P<0.05） were independent risk factor for poor prognosis in patients with AP. A nomogram prediction model was established based on the above 6 indicators， which had an area under the ROC curve of 0.924 （95%CI： 0.883‍ ‍—‍ ‍0.964）， and the Youden index for the optimal cut-off value was 0.670， with a sensitivity of 0.909 and a specificity of 0.761. The calibration curve showed good consistency between the predicted and observed results in both the modeling group and the validation group. The decision curve analysis showed that the predictive model had certain clinical effectiveness. ConclusionThe nomogram model for predicting the risk of poor prognosis in AP patients based on CRP， IL-6， TNF-α， LUS score， MCTSI score， and EPIC score has relatively good predictive performance and can provide important strategic guidance for developing early intensified treatment regimens for AP patients in clinical practice.

Objective:To develop and validate an artificial intelligence-assisted system based on convolutional neural networks (CNN) for automatic lesion recognition in small intestinal capsule endoscopy.Methods:Three small intestinal capsule endoscopy datasets were used for training ( n=26 638), validating ( n=6 652), and testing ( n=1 013) the deep learning model, covering 12 lesion categories, including vascular malformations, hemorrhage, erosion, erythema, stenosis, lymphangiectasia, submucosal tumors, polyps, lymphoid follicles, foreign bodies, veins, and normal mucosa. CNN performance was measured by area under receiver operating characteristic curve (AUC), sensitivity, specificity, precision, accuracy, and F1 score, with comparisons with endoscopists of different experience levels. Results:The top-performing model (EfficientNet-CE) achieved 86.28% sensitivity, 98.67% specificity, and AUC of 0.987 4 across all categories. It demonstrated high accuracy (86.28%) and a processing speed of 52.43 frames per second, approximately 42.4 times faster than junior endoscopists (<3 years' experience) and 40.3 times faster than senior endoscopists (>5 years' experience).Conclusion:The CNN-based model allows rapid, accurate identification of 12 small intestinal lesion types and effectively supports endoscopists in reviewing capsule endoscopy examinations due to its high sensitivity.

Objective:To develop a post competence assessment scale for nurses in the health management (physical examination) center and assess its reliability and validity.Methods:This study adopted an empirical approach. A total of 801 nurses from the health management (physical examination) center were recruited to participate in this study. A research team was formed in August 2024. This team transformed the previously constructed core competence evaluation index system for health management specialist nurses in the health management (physical examination) center (comprising 6 first-level indicators and 70 third-level indicators) into a preliminary post competence assessment scale. Seven experts evaluated the content validity of the scale. In September 2024, a pilot survey was conducted among 27 nurses from the health management (physical examination) center of Sichuan Provincial People′s Hospital using convenience sampling. From October to November 2024, the first main survey was administered to 385 nurses of health management (physical examination) center across 54 cities in China using both convenience sampling and snowball sampling methods, followed by exploratory factor analysis (EFA). Subsequently, utilizing the refined scale obtained after eliminating certain items, a second main survey was conducted among 389 nurses in the health management (physical examination) center, followed by a confirmatory factor analysis (CFA). The reliability of the final scale was assessed using Cronbach′s α coefficient, split-half reliability, composite reliability, and test-retest reliability.Results:The finalized scale for nurses′ post competency in health management (physical examination) center comprises five dimensions—basic nursing service competency, health management practice competency, knowledge integration competency, professional development competency, and professional attitude—with a total of 57 items. The item level content validity index (I-CVI) of the items of the content validity display scale ranged from 0.857 to 1.000, and the content validity index of each dimension ranged from 0.984 to 1.000. The scale-level Content Validity index/average (S-CVI/Ave) was 0.995. The contribution rate of the 6 factors extracted by EFA was 74.07%. After group discussion and CFA, the scale of the 5-factor structural equation model was constructed. The total Cronbach′s α coefficient of the scale was 0.986, the split-half reliability was 0.865, the composite reliability was 0.960-0.980, the total table test-retest reliability was 0.762, and the test-retest reliability of each dimension was 0.681-0.731.Conclusion:The developed assessment scale for assessing the post competence of nurses in the health management (physical examination) center demonstrates excellent reliability and validity.

Objective:To construct and validate a predictive model and application program for stone recurrence after endoscopic retrograde cholangiopancreatography (ERCP) based on machine learning algorithms in patients with common bile duct stones (CBDS).Methods:A multicenter retrospective cohort study was conducted, 862 CBDS patients underwent ERCP from June 2020 to September 2023 in Changshu First People′s Hospital (data set 1, 759 cases, including a training set of 588 cases and a validation set of 171 cases) and Changshu Hospital of Traditional Chinese Medicine (data set 2, 103 cases, used as a test set). The demographics, medical history, ERCP procedural records and laboratory indices were collected. All patients were followed up for 1 year, and the stone recurrence was recorded. In training set, the feature selection was conducted by the least absolute shrinkage and selection operator (LASSO) algorithm, and a conventional Logistic regression model was constructed based on selected features. The 3 machine learning algorithms (gradient boosting machine model, extreme gradient boosting model and random forest model) and a conventional Logistic regression model (LASSO model) were trained to fit predictive models. The model performance was assessed by area under curve (AUC) of receiver operating characteristic curve. The model interpretability was analyzed by feature importance evaluation, Shapley additive explanations (SHAP) and force plots. The best-performing model was deployed as an online application by Streamlit framework (V1.36.0).Results:Among the 862 patients, 158 patients (18.33%) developed stone recurrence after ERCP. There were no statistical difference in demographics, medical history, ERCP procedural records and laboratory indices between training set and a validation set ( P>0.05). LASSO regression analysis result showed that 6 key variables (in descending order of significance: endoscopic sphincterotomy, common bile duct angulation, stone diameter, stone count, common bile duct diameter, and periampullary diverticulum) influencing stone recurrence. ROC curve analysis result showed that the random forest model exhibited the highest predictive performance (it had the largest AUC of 0.900). SHAP analysis result showed that common bile duct angulation, common bile duct diameter, stone diameter, endoscopic sphincterotomy and stone count were the top 5 contributing factors in the random forest model. Using Python, the random forest model was implemented into a Streamlit-based application with a user-friendly visual interface, providing predictive outcomes, confidence levels, SHAP force diagram and health recommendations. In the test set, the application program achieved an accuracy of 84.5% (87/103), sensitivity of 82.6% (19/23), and specificity of 85.0% (68/80). SHAP plots and force diagram intuitively illustrated the impact of key features on stone recurrence prediction, offering a clear visualization of each variable′s role within the model. Conclusions:The predictive model and application program based on the random forest machine learning algorithms demonstrate excellent predictive performance and practical usability in predicting stone recurrence after ERCP in patients with CBDS.

Objective To construct YOLOv10 based artificial intelligence(AI)models for the automatic detection in small bowel capsule endoscopy(SBCE)images.Methods SBCE data from two centers was collected,including 23 115 images and 35 412 annotated labels covering 11 categories of small bowel lesions.The images were annotated using the LabelMe tool and converted into the YOLO format required for deep learning model development.The pre-trained YOLOv10 and YOLOv8 models were used for transfer learning training on the constructed dataset.Model performance was comprehensively evaluated using metrics such as precision,accuracy,sensitivity,specificity,false-positive rate,and detection speed.Finally,the models were deployed on local computers for real-time detection of SBCE images and videos.Results Six different versions of YOLO object detection models were developed,namely YOLOv8n,YOLOv8s,YOLOv8m,YOLOv10n,YOLOv10s,and YOLOv10m.On the validation set,YOLOv10s model achieved the best mAP50(0.795);although its inference latency was not the fastest(4.803 ms/img),it met the requirements for clinical application.On the test set,YOLOv10s performed well,with an accuracy of 92.69%,a sensitivity of 89.23%,and a false-positive rate of 4.78%.Especially,in category-specific inference,the highest sensitivity was for"bleeding"at 96.41%,while the lowest was for"narrowing"at 82.29%.Conclusion The model constructed based on YOLOv10 neural network can rapidly and accurately detect and classify various small bowel lesions,exhibiting significant clinical application potential.

Objective:To develop a post competence assessment scale for nurses in the health management (physical examination) center and assess its reliability and validity.Methods:This study adopted an empirical approach. A total of 801 nurses from the health management (physical examination) center were recruited to participate in this study. A research team was formed in August 2024. This team transformed the previously constructed core competence evaluation index system for health management specialist nurses in the health management (physical examination) center (comprising 6 first-level indicators and 70 third-level indicators) into a preliminary post competence assessment scale. Seven experts evaluated the content validity of the scale. In September 2024, a pilot survey was conducted among 27 nurses from the health management (physical examination) center of Sichuan Provincial People′s Hospital using convenience sampling. From October to November 2024, the first main survey was administered to 385 nurses of health management (physical examination) center across 54 cities in China using both convenience sampling and snowball sampling methods, followed by exploratory factor analysis (EFA). Subsequently, utilizing the refined scale obtained after eliminating certain items, a second main survey was conducted among 389 nurses in the health management (physical examination) center, followed by a confirmatory factor analysis (CFA). The reliability of the final scale was assessed using Cronbach′s α coefficient, split-half reliability, composite reliability, and test-retest reliability.Results:The finalized scale for nurses′ post competency in health management (physical examination) center comprises five dimensions—basic nursing service competency, health management practice competency, knowledge integration competency, professional development competency, and professional attitude—with a total of 57 items. The item level content validity index (I-CVI) of the items of the content validity display scale ranged from 0.857 to 1.000, and the content validity index of each dimension ranged from 0.984 to 1.000. The scale-level Content Validity index/average (S-CVI/Ave) was 0.995. The contribution rate of the 6 factors extracted by EFA was 74.07%. After group discussion and CFA, the scale of the 5-factor structural equation model was constructed. The total Cronbach′s α coefficient of the scale was 0.986, the split-half reliability was 0.865, the composite reliability was 0.960-0.980, the total table test-retest reliability was 0.762, and the test-retest reliability of each dimension was 0.681-0.731.Conclusion:The developed assessment scale for assessing the post competence of nurses in the health management (physical examination) center demonstrates excellent reliability and validity.

Objective:To develop and validate an artificial intelligence-assisted system based on convolutional neural networks (CNN) for automatic lesion recognition in small intestinal capsule endoscopy.Methods:Three small intestinal capsule endoscopy datasets were used for training ( n=26 638), validating ( n=6 652), and testing ( n=1 013) the deep learning model, covering 12 lesion categories, including vascular malformations, hemorrhage, erosion, erythema, stenosis, lymphangiectasia, submucosal tumors, polyps, lymphoid follicles, foreign bodies, veins, and normal mucosa. CNN performance was measured by area under receiver operating characteristic curve (AUC), sensitivity, specificity, precision, accuracy, and F1 score, with comparisons with endoscopists of different experience levels. Results:The top-performing model (EfficientNet-CE) achieved 86.28% sensitivity, 98.67% specificity, and AUC of 0.987 4 across all categories. It demonstrated high accuracy (86.28%) and a processing speed of 52.43 frames per second, approximately 42.4 times faster than junior endoscopists (<3 years' experience) and 40.3 times faster than senior endoscopists (>5 years' experience).Conclusion:The CNN-based model allows rapid, accurate identification of 12 small intestinal lesion types and effectively supports endoscopists in reviewing capsule endoscopy examinations due to its high sensitivity.