OBJECTIVE: To develop an artificial intelligence-assisted system for the automatic detection of the features of common 21 auricular points based on the YOLOv8 neural network. METHODS: A total of 660 human auricular images from three research centers were collected from June 2019 to February 2024. The rectangle boxes and features of images were annotated using the LabelMe5.3.1 tool and converted them into a format compatible with the YOLO model. Using these data, transfer learning and fine-tuning training were conducted on different scales of pretrained YOLO neural network models. The model's performance was evaluated on validation and test sets, including the mean average precision (mAP) at various thresholds, recall rate (recall), frames per second (FPS) and confusion matrices. Finally, the model was deployed on a local computer, and the real-time detection of human auricular images was conducted using a camera. RESULTS: Five different versions of the YOLOv8 key-point detection model were developed, including YOLOv8n, YOLOv8s, YOLOv8m, YOLOv8l, and YOLOv8x. On the validation set, YOLOv8n showed the best performance in terms of speed (225.736 frames per second) and precision (0.998). On the external test set, YOLOv8n achieved the accuracy of 0.991, the sensitivity of 1.0, and the F1 score of 0.995. The localization performance of auricular point features showed the average accuracy of 0.990, the precision of 0.995, and the recall of 0.997 under 50% intersection ration (mAP⁵⁰). CONCLUSION The key-point detection model of 21 common auricular points based on YOLOv8n exhibits the excellent predictive performance, which is capable of rapidly and automatically locating and classifying auricular points.
Humans ; Neural Networks, Computer ; Artificial Intelligence ; Acupuncture Points

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.

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 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 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 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 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 evaluate factors associated with prognosis of tonsil squamous cell carcinoma (TSCC) patients and analyze the competing risks of death in TSCC patients. Methods Data tonsil malignant tumors cases diagnosed between 1975 and 2020 were obtained from the SEER database, and records confirmed as squamous cell carcinoma were selected. A Cox proportional hazards regression model was used to investigate the relationships of gender, race, age, marital status, year of diagnosis, lesion location, pathological evidence, treatment regimen with overall survival rate as well as cause-specific mortality outcomes. The competing risks of cause-specific death outcomes among TSCC patients with different clinical characteristics were assessed. Results This study included 14, 805 TSCC patients, including 11, 650 males, accounting for 78.69%. 93.99% of TSCC cases were diagnosed after the age of 45, with the highest incidence occurring in 45 to 64 age group. Radiotherapy was the most commonly used treatment modality (81.78%), compared to surgery (49.47%) and chemotherapy (47.10%). By the end of the follow-up period, 8, 003 (54.06%) TSCC patients had died, with a median survival time of 2.33 years. Cox proportional hazards regression analysis showed that the HR (95%CI) for TSCC-related deaths among patients not receiving surgery, radiotherapy and chemotherapy were 2.101 (1.972 to 2.239), 1.829 (1.702 to 1.966) and 1.023(0.951 to 1.100), respectively, compared to those who did receive these treatments; the HR (95%CI) for deaths due to other causes were 1.630 (1.513 to 1.756), 1.438 (1.318 to 1.570) and 1.328 (1.212 to 1.456), respectively. Compared to patients < 45 years old, the HR (95%CI) for TSCC-related deaths among patients ≥65 years old were 2.218 (1.933 to 2.545), and for deaths due to other causes were 6.178 (5.133 to 7.436). Conclusion Radiotherapy, surgery and chemotherapy all contribute to improving the prognosis of TSCC patients. For elderly TSCC patients, particular attention should be paid to non-TSCC-related death risks.

Objective To explore the effects of HPV16 E6 on genes and signaling pathways in cervical epithelial cells and to screen genes associated with oncogenic transformation.Methods HUCEC models infected with HPV16 E6 were constructed,and transcriptome sequencing was performed to screen for differentially expressed genes(DEGs),which were subjected to Gene Ontology(GO)and Kyoto Encyclopedia of Genes and Genomes(KEGG)enrichment to analyze the differential signaling pathways.RT-qPCR was used to validate major differentially down-regulated expressed genes.After predicting the major differentially expressed proteins by molecular docking analysis,the expression of major differential genes in HUCEC cell model was verified by RT-qPCR and Western blotting.In addition,RT-qPCR and Western blotting were used to further verify the expression of major differential genes in cervical cancer cell lines,SiHa and CaSki.Results A total of 55 genes with more than two-fold differential expression were screened.The results centering on down-regulated genes showed that the negatively regulated differential gene was mainly enriched in redox processes;KEGG enrichment analysis revealed that it was mainly associated with carbohydrate metabolism and cancer.RT-qPCR results showed that the down-regulated differential expression trends of the selected 10 genes were basically consistent with the sequencing results.Molecular docking analysis predicted an interaction between DHRS2 and HPV16 E6,and RT-qPCR and Western blotting confirmed that HPV16 E6 down-regulated DHRS2 mRNA(P＜0.01)and protein(P＜0.05)and ETV5 protein expression(P＜0.01).In SiHa and CaSki cells,compared with the control group,the mRNA and protein expression of DHRS2 was downregulated and positively correlated with the trend of P53 protein expression(P＜0.05).Conclusion HPV16 E6 can mediate oncogenic transformation of cervical cells and promote cervical carcinogenesis through downregulating DHRS2 expression.