Objective:To develop an artificial intelligence (AI) calculation system for the effective withdrawal time of colonoscopy and to evaluate its clinical application value.Methods:First, 17 118 colonoscopy pictures from Renmin Hospital of Wuhan University were used for training and testing to establish a deep convolutional neural network model to recognize various colonoscopy fields. Then this model was integrated with the internal and external recognition model and cecum recognition model developed by the research group to create an AI system for automatic calculation of the effective withdrawal time. Finally, 944 colonoscopy videos from the Endoscopy Center of Renmin Hospital of Wuhan University from July 1, 2020 to October 10, 2020 were included in a retrospective analysis. AI automatic computing system was used to calculate the effective withdrawal time, and 89 of them were manually calculated to evaluate the accuracy of the AI automatic computing system. The remaining 855 cases were divided into two groups according to AI calculations, namely, the effective withdrawal time <6 min group ( n=615) and the effective withdrawal time ≥6 min group ( n=240), and the differences in the overall detection rate of adenoma and polyp were compared and analyzed. Results:The accuracy of AI automatic calculation system for effective withdrawal time reached 92.1% (82/89). The overall adenoma detection rate in the group with effective withdrawal time ≥6 min was 37.5% (90/240), that in the group with effective withdrawal time <6 min was 19.0% (117/615), and the difference was statistically significant ( χ2=32.11, P<0.001). The overall polyp detection rate in the group with effective withdrawal time ≥6 min was 75.0% (180/240), and that in the group with effective withdrawal time <6 min was 45.2% (278/615), with statistical significance ( χ2=61.62, P<0.001). Conclusion:AI automatic computing system can accurately calculate the effective withdrawal time of colonoscopy, and can be used to monitor the effective withdrawal time of clinical colonoscopy. In addition, effective withdrawal time ≥6 min can effectively improve the detection rate of adenoma and polyps.

"Weibing" is a fundamental concept in traditional Chinese medicine (TCM), representing a transitional state characterized by diminished self-regulatory abilities without overt physiological or social dysfunction. This perspective delves into the biological foundations and quantifiable markers of Weibing, aiming to establish a research framework for early disease intervention. Here, we propose the "Health Quadrant Classification" system, which divides the state of human body into health, sub-health, disease-susceptible state, and disease. We suggest the disease-susceptible stage emerges as a pivotal point for TCM interventions. To understand the intrinsic dynamics of this state, we propose laboratory and clinical studies utilizing time-series experiments and stress-induced disease susceptibility models. At the molecular level, bio-omics technologies and bioinformatics approaches are highlighted for uncovering intricate changes during disease progression. Furthermore, we discuss the application of mathematical models and artificial intelligence in developing early warning systems to anticipate and avert the transition from health to disease. This approach resonates with TCM's preventive philosophy, emphasizing proactive health maintenance and disease prevention. Ultimately, our perspective underscores the significance of integrating modern scientific methodologies with TCM principles to propel Weibing research and early intervention strategies forward.

Objective To explore the diagnostic value of CT radiomics combined with prothrombin induced by vitamin K absence or antagonist-Ⅱ(PIVKA-Ⅱ)for hepatocellular carcinoma in the background of liver cirrhosis.Methods The clinical and CT imaging data of patients with liver cirrhosis were analyzed retrospectively.Hepatocellular carcinoma was observed in the observation group,and no hepatocellular carcinoma was found in the control group.Logistic regression was used to analyze the clinical factors of hepatocellular carcinoma and a Clinic model was constructed.Support vector machine(SVM)was used to construct the optimal feature model(Rad model).An artificial neural network model(Combine model)was built based on Softmax policy using Python3.6.Results The degree of liver cirrhosis,aspartate aminotransferase(AST),alpha-fetoprotein(AFP)and PIVKA-Ⅱ were independent factors for predicting hepatocellular carcinoma(P＜0.05).The sensitivity and specificity of the clinical logistic regression model were 73.15％and 68.34％,respectively.The SVM model was used to construct Radiomics score(Radscore)containing 7 optimal features,and there were significant differences between the two groups(P＜0.001).DeLong test showed that the area under the curve(AUC)of Combine model was significantly higher than that of Rad model and Clinic model(P＜0.05).The Hosmer-Lemeshow test showed that the Combine model agrees well.Decision curve analysis showed that the curves of Combine model were significantly higher than Clinic model,Rad model and the extreme curve.Conclusion The Combine model based on CT radiomics combined with clinical factors can accurately predict the occurrence of hepatocellular carcinoma in the background of liver cirrhosis.

Objective To evaluate the clinical utility of the f-wave to QRS complex amplitude ratio(f/R ratio)in intracardiac electrogram(IC-ECG)-guided positioning of peripherally inserted central catheter(PICC)tips in patients with atrial fibrillation(AF),providing evidence to enhance clinical practice.Methods This study employed a conve-nience sampling method to enroll eligible AF patients admitted to a tertiary hospital in Suzhou from July 2023 to July 2024.During PICC placement,IC-ECG was utilized to monitor f-wave and QRS complex amplitude variations.Following successful catheterization,the f/R ratio was measured,and chest X-ray was performed to confirm the catheter tip position.The accuracy of PICC tip positioning across different f/R ratio ranges was analyzed,and the incidence of arrhythmias was recorded.A receiver operating characteristic curve was constructed to assess the diag-nostic performance of the f/R ratio in PICC tip localization.Results A total of 68 AF patients were included,with f/R ratios ranging from 20.63％to 91.24％.PICC tip positioning accuracy varied significantly across different f/R ratio ranges(P=0.006).The area under the ROC curve(AUC)for f/R ratio in PICC tip positioning was 0.784(P=0.009),with a maximum Youden index of 0.567,an optimal diagnostic threshold of 40.00％,a sensitivity of 81.7％,a speci-ficity of 75.0％,a positive predictive value of 96.1％,and a negative predictive value of 35.3％.No arrhythmias other than AF occurred during the procedure.Conclusion The f/R ratio provides reliable and safe guidance for PICC tip positioning in AF patients.An f/R ratio ≥40％is associated with higher accuracy in identifying the optimal catheter tip position.

Objective To explore the diagnostic value of CT radiomics combined with prothrombin induced by vitamin K absence or antagonist-Ⅱ(PIVKA-Ⅱ)for hepatocellular carcinoma in the background of liver cirrhosis.Methods The clinical and CT imaging data of patients with liver cirrhosis were analyzed retrospectively.Hepatocellular carcinoma was observed in the observation group,and no hepatocellular carcinoma was found in the control group.Logistic regression was used to analyze the clinical factors of hepatocellular carcinoma and a Clinic model was constructed.Support vector machine(SVM)was used to construct the optimal feature model(Rad model).An artificial neural network model(Combine model)was built based on Softmax policy using Python3.6.Results The degree of liver cirrhosis,aspartate aminotransferase(AST),alpha-fetoprotein(AFP)and PIVKA-Ⅱ were independent factors for predicting hepatocellular carcinoma(P＜0.05).The sensitivity and specificity of the clinical logistic regression model were 73.15％and 68.34％,respectively.The SVM model was used to construct Radiomics score(Radscore)containing 7 optimal features,and there were significant differences between the two groups(P＜0.001).DeLong test showed that the area under the curve(AUC)of Combine model was significantly higher than that of Rad model and Clinic model(P＜0.05).The Hosmer-Lemeshow test showed that the Combine model agrees well.Decision curve analysis showed that the curves of Combine model were significantly higher than Clinic model,Rad model and the extreme curve.Conclusion The Combine model based on CT radiomics combined with clinical factors can accurately predict the occurrence of hepatocellular carcinoma in the background of liver cirrhosis.

Objective To evaluate the clinical utility of the f-wave to QRS complex amplitude ratio(f/R ratio)in intracardiac electrogram(IC-ECG)-guided positioning of peripherally inserted central catheter(PICC)tips in patients with atrial fibrillation(AF),providing evidence to enhance clinical practice.Methods This study employed a conve-nience sampling method to enroll eligible AF patients admitted to a tertiary hospital in Suzhou from July 2023 to July 2024.During PICC placement,IC-ECG was utilized to monitor f-wave and QRS complex amplitude variations.Following successful catheterization,the f/R ratio was measured,and chest X-ray was performed to confirm the catheter tip position.The accuracy of PICC tip positioning across different f/R ratio ranges was analyzed,and the incidence of arrhythmias was recorded.A receiver operating characteristic curve was constructed to assess the diag-nostic performance of the f/R ratio in PICC tip localization.Results A total of 68 AF patients were included,with f/R ratios ranging from 20.63％to 91.24％.PICC tip positioning accuracy varied significantly across different f/R ratio ranges(P=0.006).The area under the ROC curve(AUC)for f/R ratio in PICC tip positioning was 0.784(P=0.009),with a maximum Youden index of 0.567,an optimal diagnostic threshold of 40.00％,a sensitivity of 81.7％,a speci-ficity of 75.0％,a positive predictive value of 96.1％,and a negative predictive value of 35.3％.No arrhythmias other than AF occurred during the procedure.Conclusion The f/R ratio provides reliable and safe guidance for PICC tip positioning in AF patients.An f/R ratio ≥40％is associated with higher accuracy in identifying the optimal catheter tip position.

Objective:To develop an artificial intelligence (AI) calculation system for the effective withdrawal time of colonoscopy and to evaluate its clinical application value.Methods:First, 17 118 colonoscopy pictures from Renmin Hospital of Wuhan University were used for training and testing to establish a deep convolutional neural network model to recognize various colonoscopy fields. Then this model was integrated with the internal and external recognition model and cecum recognition model developed by the research group to create an AI system for automatic calculation of the effective withdrawal time. Finally, 944 colonoscopy videos from the Endoscopy Center of Renmin Hospital of Wuhan University from July 1, 2020 to October 10, 2020 were included in a retrospective analysis. AI automatic computing system was used to calculate the effective withdrawal time, and 89 of them were manually calculated to evaluate the accuracy of the AI automatic computing system. The remaining 855 cases were divided into two groups according to AI calculations, namely, the effective withdrawal time <6 min group ( n=615) and the effective withdrawal time ≥6 min group ( n=240), and the differences in the overall detection rate of adenoma and polyp were compared and analyzed. Results:The accuracy of AI automatic calculation system for effective withdrawal time reached 92.1% (82/89). The overall adenoma detection rate in the group with effective withdrawal time ≥6 min was 37.5% (90/240), that in the group with effective withdrawal time <6 min was 19.0% (117/615), and the difference was statistically significant ( χ2=32.11, P<0.001). The overall polyp detection rate in the group with effective withdrawal time ≥6 min was 75.0% (180/240), and that in the group with effective withdrawal time <6 min was 45.2% (278/615), with statistical significance ( χ2=61.62, P<0.001). Conclusion:AI automatic computing system can accurately calculate the effective withdrawal time of colonoscopy, and can be used to monitor the effective withdrawal time of clinical colonoscopy. In addition, effective withdrawal time ≥6 min can effectively improve the detection rate of adenoma and polyps.