Establishment and validation of a computer-assisted colonic polyp localization system based on deep learning

BACKGROUND Artificial intelligence in colonoscopy is an emerging field,and its application may help colonoscopists improve inspection quality and reduce the rate of missed polyps and adenomas.Several deep learning-based computer-assisted detection(CADe)techniques were established from small single-center datasets,and unrepresentative learning materials might confine their application and generalization in wide practice.Although CADes have been reported to identify polyps in colonoscopic images and videos in real time,their diagnostic performance deserves to be further validated in clinical practice.AIM To train and test a CADe based on multicenter high-quality images of polyps and preliminarily validate it in clinical colonoscopies.METHODS With high-quality screening and labeling from 55 qualified colonoscopists,a dataset consisting of over 71000 images from 20 centers was used to train and test a deep learning-based CADe.In addition,the real-time diagnostic performance of CADe was tested frame by frame in 47 unaltered full-ranged videos that contained 86 histologically confirmed polyps.Finally,we conducted a selfcontrolled observational study to validate the diagnostic performance of CADe in real-world colonoscopy with the main outcome measure of polyps per colonoscopy in Changhai Hospital.RESULTS The CADe was able to identify polyps in the test dataset with 95.0%sensitivity and 99.1%specificity.For colonoscopy videos,all 86 polyps were detected with 92.2%sensitivity and 93.6%specificity in frame-by-frame analysis.In the prospective validation,the sensitivity of CAD in identifying polyps was 98.4%(185/188).Folds,reflections of light and fecal fluid were the main causes of false positives in both the test dataset and clinical colonoscopies.Colonoscopists can detect more polyps(0.90 vs 0.82,P<0.001)and adenomas(0.32 vs 0.30,P=0.045)with the aid of CADe,particularly polyps<5 mm and flat polyps(0.65 vs 0.57,P<0.001;0.74 vs 0.67,P=0.001,respectively).However,high efficacy is not realized in colonoscopies with inadequate bowel preparation and withdrawal time(P=0.32;P=0.16,respectively).CONCLUSION CADe is feasible in the clinical setting and might help endoscopists detect more polyps and adenomas,and further confirmation is warranted.

Establishing a model to measure and predict the quality of gastrointestinal endoscopy

BACKGROUND Tens of millions of gastrointestinal endoscopic procedures are performed every year in China,but the quality varies significantly and related factors are complex.Individual endoscopist-and endoscopy division-related factors may be useful to establish a model to measure and predict the quality of endoscopy.AIM To establish a model to measure and predict the quality of gastrointestinal endoscopic procedures in China's Mainland.METHODS Selected data on endoscopy experience,equipment,facility,qualification of endoscopists,and other relevant variables were collected from the National Database of Digestive Endoscopy of China.The multivariable logistic regression analysis was used to identify the potential predictive variables for occurrence of medical malpractice and patient disturbance.Linear and nonlinear regressions were used to establish models to predict incidence of endoscopic complications.RESULTS In 2012,gastroscopy/colonoscopy-related complications in China's Mainland included bleeding in 4,359 cases(0.02%)and perforation in 914(0.003%).Endoscopic-retrograde-cholangiopancreatography-related complications included severe acute pancreatitis in 593 cases(0.3%),bleeding in 2,151(1.10%),perforation in 257(0.13%)and biliary infection in 4,125(2.11%).Moreover,1,313(5.0%)endoscopists encountered with medical malpractice,and 5,243(20.0%)encountered with the disturbance from patients.The length of endoscopy experience,weekly working hours,weekly night shifts,annual vacation days and job satisfaction were predictors for the occurrence of medical malpractice and patient disturbance.However,the length of endoscopy experience and the ratio of endoscopists to nurses were not adequate to establish an effective predictive model for endoscopy complications.CONCLUSION The workload and job satisfaction of endoscopists are valuable predictors for medical malpractice or patient disturbance.More comprehensive data are needed to establish quality-predictive models for endoscopic complications.

Application of an artificial intelligence system for endoscopic diagnosis of superficial esophageal squamous cell carcinoma

BACKGROUND Upper gastrointestinal endoscopy is critical for esophageal squamous cell carcinoma(ESCC)detection;however,endoscopists require long-term training to avoid missing superficial lesions.AIM To develop a deep learning computer-assisted diagnosis(CAD)system for endoscopic detection of superficial ESCC and investigate its application value.METHODS We configured the CAD system for white-light and narrow-band imaging modes based on the YOLO v5 algorithm.A total of 4447 images from 837 patients and 1695 images from 323 patients were included in the training and testing datasets,respectively.Two experts and two non-expert endoscopists reviewed the testing dataset independently and with computer assistance.The diagnostic performance was evaluated in terms of the area under the receiver operating characteristic curve,accuracy,sensitivity,and specificity.RESULTS The area under the receiver operating characteristics curve,accuracy,sensitivity,and specificity of the CAD system were 0.982[95%confidence interval(CI):0.969-0.994],92.9%(95%CI:89.5%-95.2%),91.9%(95%CI:87.4%-94.9%),and 94.7%(95%CI:89.0%-97.6%),respectively.The accuracy of CAD was significantly higher than that of non-expert endoscopists(78.3%,P<0.001 compared with CAD)and comparable to that of expert endoscopists(91.0%,P=0.129 compared with CAD).After referring to the CAD results,the accuracy of the non-expert endoscopists significantly improved(88.2%vs 78.3%,P<0.001).Lesions with Paris classification type 0-IIb were more likely to be inaccurately identified by the CAD system.CONCLUSION The diagnostic performance of the CAD system is promising and may assist in improving detectability,particularly for inexperienced endoscopists.

Lactam Triterpenoids from the Bark of Toona sinensis

Three new limonoid-type triterpenoids,namely toonasins A–C(1–3)with a rare lactam E ring,along with six known compounds(4–9)were isolated from the barks of Toona sinensis.The structures of new compounds were elucidated by interpretation of spectroscopic data,and the relative configuration of compound 1 was further characterized by X-ray crystallographic analyses.The isolated compounds were evaluated for their cytotoxic activities against five human tumor cell lines(HL-60,SMMC-7721,A-549,MCF-7 and SW480),and compounds 3 and 5 showed weak cytotoxicities.