Objective To comparative study the effect on colorectal cleansing of CT colonography with gulping down 10 mg bisacodyl before or 1 h after oral taking 2 liter polyethylene glycol .Methods Forty participants with informed consent were appor‐tioned to group A ,group B randomly ,20 cases in each group .On the day before CT colonography ,participants in group A oral took 20 mL of 40% W/V barium sulfate prior to 3 mealtime ,and 20 mL of 60% diatrizoate meglumine diluted in 250 mL of water after supper ,then gulped down 10 mg bisacodyl enteric‐coated tablets 1 hour before oral taking 2 liter polyethylene glycol electrolyte so‐lution .Participants in group B were the same as that in group A ,with the exception of gulping down 10 mg bisacodyl enteric‐coated tablets 1 hour after oral taking 2 liter polyethylene glycol electrolyte solution .Cleansing efficacy of stool and fluid ,and attenuation value of remainder fluid between the two groups were analyzed statistically .Results In group A ,score of cleansing efficacy of stool (1 .96 ± 0 .11) was lower than that in group B (2 .01 ± 0 .12) ,segments with good cleansing efficacy of stool (87/120 segments , 72 .50% ) was higher than that in group B (83/120 segments ,69 .17% ) ,the difference was not statistically significant (P>0 .05) .In group A ,score of cleansing efficacy of fluid (1 .50 ± 0 .06) was lower than that in group B (1 .53 ± 0 .06) ,segments with good cleansing efficacy of fluid(113/120 segments ,94 .17% ) was higher than that in group B (111/120 segments ,92 .50% ) ,the differ‐ence was not statistically significant (P>0 .05) .Attenuation value of remainder fluid [(729 ± 29)HU ] in group A was higher than that in group B[(653 ± 25)HU] ,the difference was statistically significant(P<0 .05) .Conclusion Gulping down 10 mg Bisacodyl before or after oral taking 2 liter polyethylene glycol has no effect on cleansing of stool and fluid ,with good cleansing efficacy .The former has better cleansing efficacy of fluid ,is beneficial to detecting polyps for CT colonography .

Objective To study the compliance of examinees,and effectiveness of colorectal distension with partially automated and individualized insufflation of air for dual-energy CT colonography.Methods Forty-six healthy adult volunteers without history of conditions affecting gastrointestinal motor function were enrolled in this study.One day before CT examination,volunteers were asked to orally administered 60 mL 4% diatrizoate meglumine five times for fecal tagging.Air was insufflated by using an inflator in a partially automated and individualized manner.The volunteers were initially asked to assume the right lateral decubitus position,then slowly turn to the supine position.Insufflation rate began at 1.5 L/min,and decreased to 0.5 L/min at later stage.The necessity and volume of air insufflation were decided according to effectiveness of colorectal distension on CT scout images,self-reported sensation of volunteers,and intestinal pressure.Dual-energy CT scanning was performed,and dual-energy blended images were acquired.Compliance of volunteers was statistically analyzed.The effectiveness of colorectal segments distension was statistically analyzed by using Kruskal-Wallis H test.Results No abdominal pain,bloating,nausea or vomiting were noted in the 46 volunteers.All volunteers easily accepted colorectal insufflation of air,with grade 1 compliance.The effectiveness of colorectal distension of grades 1,2,3 and 4 were 0%,2.1%,5.1% and 92.8%,respectively.The difference of effectiveness of colorectal segments distension had no statistical significance(χ2=6.19,P=0.288).The effectiveness of insufflation was poor in 6 colorectal segments,including 2 in sigmoid colon and 2 in rectum.Effectiveness of insufflation was suboptimal in 14 colorectal segments,including 4 in descending colon,4 in sigmoid colon,and 3 in rectum.Conclusion Compliance of examinees with partially automated and individualized insufflation of air for dual-energy CT colonography is excellent,with good effectiveness of colorectal distension.

Objective:To construct a feature visualization system utilizing deep learning for superficial esophageal squamous cell carcinoma (SESCC) under magnifying endoscopy with narrow band imaging (ME-NBI) to predict the infiltration depth of SESCC.Methods:The feature visualization system consisted of four models: two for segmenting the intrapapillary capillary loops (IPCL) area and avascular area (AVA) in ME-NBI images of SESCC lesions (models 1 and 2, respectively), one for obtaining the principal component of color (PCC) in ME-NBI images of SESCC lesions (model 3), and another for automatically predicting the depth of SESCC infiltration based on the features extracted from the first three models (model 4). A total of 2 341 ME-NBI images of SESCC lesions from April 2016 to October 2021 were used to develop the feature visualization system, which was divided into 3 datasets: dataset 1 (1 077 ME-NBI images) was used to train and test models 1-3, dataset 2 (1 069 ME-NBI images) was expanded by 20 times through feature combination to generate 21 380 feature synthetic images to train and test model 4, and dataset 3 (195 ME-NBI images), containing 146 ME-NBI images with lesion invasion depth from the epithelium to the upper 1/3 of the submucosa (EP-SM1), and 49 ME-NBI images with lesion invasion depth from the middle 1/3 to the lower 1/3 of the submucosa (SM2-SM3), was used to validate the diagnostic performance of the feature visualization system in predicting the invasion depth of SESCC (EP-SM1/SM2-SM3). In order to evaluate the superiority of the feature visualization system, the prediction results of dataset 3 of the traditional deep learning system (trained directly with ME-NBI images), single-item feature models (single-item IPCL feature model, single-item AVA feature model and single-item PCC feature model) were compared with the prediction results of the feature visualization system. In order to evaluate the clinical utility of the feature visualization system, 4 expert physicians (with more than 10 years of endoscopic operation, expert physician group) and 5 senior physicians (with more than 5 years of endoscopic operation, senior physician group) were invited to participate in the human-computer competition to diagnose dataset 3, and the results were compared with the feature visualization system.Results:The accuracy, sensitivity and specificity of the feature visualization system in predicting the invasion depth of SESCC (EP-SM1/SM2-SM3) were 83.08% (162/195), 82.88% (121/146) and 83.67% (41/49), respectively. The above indicators were 60.00% (117/195), 52.05% (76/146) and 83.67% (41/49) for the traditional deep learning system, 74.87% (146/195), 75.34% (110/146) and 73.47% (36/49) for the single IPCL feature model, 58.97% (115/195), 60.27% (88/146) and 55.10% (27/49) for single AVA feature model, 71.28% (139/195), 71.23% (104/146) and 71.43% (35/49) for single PCC feature model, respectively. The results were 66.67%, 78.22% and 32.24% in senior physician group, and 72.31%, 85.96% and 31.63% in expert physician group, respectively. The accuracy of the feature visualization system in predicting the invasion depth of SESCC was significantly higher than that of the other 6 groups ( P<0.05). The sensitivity of feature visualization system was slightly higher than that of senior physician group ( χ2=1.59, P=0.21) and single-item IPCL feature model ( χ2=2.51, P=0.11), slightly lower than that of expert physician group ( χ2=0.89, P=0.35), and significantly higher than that of three other groups ( P<0.05). The specificity of the feature visualization system was similar to the traditional deep learning system ( χ2=0.00, P=1.00), slightly higher than that of single-item IPCL feature model ( χ2=1.52, P=0.22) and single-item PCC feature model (χ2=2.11, P=0.15), and significantly higher than that of the single AVA feature model ( χ2=9.42, P<0.01), senior physician group ( χ2=44.71, P<0.01) and expert physician group ( χ2=43.57, P<0.01). Conclusion:The developed deep learning-based feature visualization system using ME-NBI shows excellent diagnostic performance in predicting the infiltration depth of SESCC (EP-SM1/SM2-SM3), surpassing the accuracy levels of experienced endoscopists with over 10 years of experience.