Colonoscopy procedural volume increases adenoma and polyp detection rates in gastroenterology trainees

AIM To investigate changes in polyp detection throughout fellowship training, and estimate colonoscopy volume required to achieve the adenoma detection rate(ADRs) and polyp detection rate(PDRs) of attending gastroenterologists.METHODS We reviewed colonoscopies from July 1, 2009 to June 30, 2014. Fellows' procedural logs were used to retrieve colonoscopy procedural volumes, and these were treated as the time variable. Findings from screening colonoscopies were used to calculate colonoscopy outcomes for each fellow for the prior 50 colonoscopies at each time point. ADR and PDR were plotted against colonoscopy procedural volumes to produce individual longitudinal graphs. Repeated measures linear mixed effects models were used to study the change of ADR and PDR with increasing procedural volume.RESULTS During the study period, 12 fellows completed full three years of training and were included in the analysis. The average ADR and PDR were, respectively, 31.5% and 41.9% for all fellows, and 28.9% and 38.2% for attendings alone. There was a statistically significant increase in ADR with increasing procedural volume(1.8%/100 colonoscopies, P = 0.002). Similarly, PDR increased 2.8%/100 colonoscopies(P = 0.0001), while there was no significant change in advanced ADR(0.04%/100 colonoscopies, P = 0.92). The ADR increase was limited to the right side of the colon, while the PDR increased in both the right and left colon. The adenoma per colon and polyp per colon also increased throughout training. Fellows reached the attendings' ADR and PDR after 265 and 292 colonoscopies, respectively.CONCLUSION We found that the ADR and PDR increase with increasing colonoscopy volume throughout fellowship. Our findings support recent recommendations of ≥ 275 colonoscopies for colonoscopy credentialing.

Association of trainee participation with adenoma and polyp detection rates

To investigate whether adenoma and polyp detection rates (ADR and PDR, respectively) in screening colonoscopies performed in the presence of fellows differ from those performed by attending physicians alone. METHODSWe performed a retrospective review of all patients who underwent a screening colonoscopy at Grady Memorial Hospital between July 1, 2009 and June 30, 2015. Patients with a history of colon polyps or cancer and those with poor colon preparation or failed cecal intubation were excluded from the analysis. Associations of fellowship training level with the ADR and PDR relative to attendings alone were assessed using unconditional multivariable logistic regression. Models were adjusted for sex, age, race, and colon preparation quality. RESULTSA total of 7503 colonoscopies met the inclusion criteria and were included in the analysis. The mean age of the study patients was 58.2 years; 63.1% were women and 88.2% were African American. The ADR was higher in the fellow participation group overall compared to that in the attending group: 34.5% vs 30.7% (P = 0.001), and for third year fellows it was 35.4% vs 30.7% (aOR = 1.23, 95%CI: 1.09-1.39). The higher ADR in the fellow participation group was evident for both the right and left side of the colon. For the PDR the corresponding figures were 44.5% vs 40.1% (P = 0.0003) and 45.7% vs 40.1% (aOR = 1.25, 95%CI: 1.12-1.41). The ADR and PDR increased with increasing fellow training level (P for trend < 0.05). CONCLUSIONThere is a stepwise increase in ADR and PDR across the years of gastroenterology training. Fellow participation is associated with higher adenoma and polyp detection.

Implications of small-bowel transit time in the detection rate of capsule endoscopy: A multivariable multicenter study of patients with obscure gastrointestinal bleeding

AIMTo define the role of small-bowel transit time in the detection rate of significant small-bowel lesions.METHODSSmall-bowel capsule endoscopy records, prospectively collected from 30 participating centers in the Lombardy Registry from October 2011 to December 2013, were included in the study if the clinical indication was obscure gastrointestinal bleeding and the capsule reached the cecum. Based on capsule findings, we created two groups: P2 (significant findings) and P0-1 (normal/negligible findings). Groups were compared for age, gender, small-bowel transit time, type of instrument, modality of capsule performance (outpatients vs inpatients), bowel cleanliness, and center volume.RESULTSWe retrieved and scrutinized 1,433 out of 2,295 capsule endoscopy records (62.4%) fulfilling the inclusion criteria. Patients were 67 &#x000b1; 15 years old, and 815 (57%) were males. In comparison with patients in the P0-1 group, those in the P2 group (n = 776, 54%) were older (P &#x0003c; 0.0001), had a longer small-bowel transit time (P = 0.0015), and were more frequently examined in low-volume centers (P &#x0003c; 0.001). Age and small-bowel transit time were correlated (P &#x0003c; 0.001), with age as the sole independent predictor on multivariable analysis. Findings of the P2 group were artero-venous malformations (54.5%), inflammatory (23.6%) and protruding (10.4%) lesions, and luminal blood (11.5%).CONCLUSIONIn this selected, prospectively collected cohort of small-bowel capsule endoscopy performed for obscure gastrointestinal bleeding, a longer small-bowel transit time was associated with a higher detection rate of significant lesions, along with age and a low center volume, with age serving as an independent predictor.

Use of artificial intelligence in improving adenoma detection rate during colonoscopy:Might both endoscopists and pathologists be further helped

Colonoscopy remains the standard strategy for screening for colorectal cancer around the world due to its efficacy in both detecting adenomatous or precancerous lesions and the capacity to remove them intra-procedurally.Computeraided detection and diagnosis(CAD),thanks to the brand new developed innovations of artificial intelligence,and especially deep-learning techniques,leads to a promising solution to human biases in performance by guarantying decision support during colonoscopy.The application of CAD on real-time colonoscopy helps increasing the adenoma detection rate,and therefore contributes to reduce the incidence of interval cancers improving the effectiveness of colonoscopy screening on critical outcome such as colorectal cancer related mortality.Furthermore,a significant reduction in costs is also expected.In addition,the assistance of the machine will lead to a reduction of the examination time and therefore an optimization of the endoscopic schedule.The aim of this opinion review is to analyze the clinical applications of CAD and artificial intelligence in colonoscopy,as it is reported in literature,addressing evidence,limitations,and future prospects.

Does deep sedation with propofol affect adenoma detection rates in average risk screening colonoscopy exams?

To determine the effect of sedation with propofol on adenoma detection rate (ADR) and cecal intubation rates (CIR) in average risk screening colonoscopies compared to moderate sedation. METHODSWe conducted a retrospective chart review of 2604 first-time average risk screening colonoscopies performed at MD Anderson Cancer Center from 2010-2013. ADR and CIR were calculated in each sedation group. Multivariable regression analysis was performed to adjust for potential confounders of age and body mass index (BMI). RESULTSOne-third of the exams were done with propofol (n = 874). Overall ADR in the propofol group was significantly higher than moderate sedation (46.3% vs 41.2%, P = 0.01). After adjustment for age and BMI differences, ADR was similar between the groups. CIR was 99% for all exams. The mean cecal insertion time was shorter among propofol patients (6.9 min vs 8.2 min; P < 0.0001). CONCLUSIONDeep sedation with propofol for screening colonoscopy did not significantly improve ADR or CIR in our population of average risk patients. While propofol may allow for safer sedation in certain patients (e.g., with sleep apnea), the overall effect on colonoscopy quality metrics is not significant. Given its increased cost, propofol should be used judiciously and without the implicit expectation of a higher quality screening exam.

New endoscopy advances to refine adenoma detection rate for colorectal cancer screening:None is the winner

Colorectal cancer(CRC) is the third most common cancer in males and second in females, and globally the fourth cause for cancer death worldwide. Oncological screening of CRC has a major role in the management of the disease and it is mostly performed by colonoscopy. Anyway, effectiveness of endoscopic screening for CRC strictly depends on adequate detection and removal of potentially precancerous lesions, and accuracy of colonoscopy in detection of adenomas is still suboptimal. For this reason, several technological advances have been implemented in order to improve the diagnostic sensitivity of colonoscopy in adenoma detection. Among these:(1) Visual technologies such as chromoendoscopy and narrow band imaging;(2) optical innovation as high definition endoscopy, full-spectrum endoscopy or Third Eye Retroscope; and(3) mechanical advances as Cap assisted colonoscopy, Endocuff, Endoring and G-Eye endoscope. All these technologies advances have been tested over time by clinical studies with mixed results. Which of them is more likely to be successful in the next future?

NHPP-based software reliability model considering testing effort and multivariate fault detection rate

In recent decades,many software reliability growth models（SRGMs） have been proposed for the engineers and testers in measuring the software reliability precisely.Most of them is established based on the non-homogeneous Poisson process（NHPP）,and it is proved that the prediction accuracy of such models could be improved by adding the describing of characterization of testing effort.However,some research work indicates that the fault detection rate（FDR） is another key factor affects final software quality.Most early NHPPbased models deal with the FDR as constant or piecewise function,which does not fit the different testing stages well.Thus,this paper first incorporates a multivariate function of FDR,which is bathtub-shaped,into the NHPP-based SRGMs considering testing effort in order to further improve performance.A new model framework is proposed,and a stepwise method is used to apply the framework with real data sets to find the optimal model.Experimental studies show that the obtained new model can provide better performance of fitting and prediction compared with other traditional SRGMs.

When should we perform colonoscopy to increase the adenoma detection rate?

The rate of adenoma detection is the most reliable quality indicator of colonoscopy.Studies have reported that colonoscopy performed in morning has a higher adenoma detection rate(ADR)than that performed in the afternoon.These studies have explained that several physician-related factors such as undergoing an emergency procedure the night before colonoscopy,accumulated workload,and increased fatigue level in the afternoon might have led to such finding.However,several opposing articles have indicated that the time of day and ADR is not quite related.Complex confounding factors can impact study results.Colonoscopy withdrawal time and bowel preparation quality are key factors.However,queue list numbers,participation of academic fellows,nurses'assistance,and the number of colonoscopies allocated per hour are also notable factors.Recently,an attempt has been made to homogenize the ADR in the morning and afternoon through artificial intelligence-assisted colonoscopy.This review article introduces the history of this long-debated topic,discusses points to consider in real-world practice,and suggests new ideas for planning future research.By understanding this issue,the rate of adenoma detection during colonoscopy is expected to be improved further.

A Hybrid Intrusion Detection Method Based on Convolutional Neural Network and AdaBoost

To solve the problem of poor detection and limited application range of current intrusion detection methods,this paper attempts to use deep learning neural network technology to study a new type of intrusion detection method.Hence,we proposed an intrusion detection algorithm based on convolutional neural network(CNN)and AdaBoost algorithm.This algorithm uses CNN to extract the characteristics of network traffic data,which is particularly suitable for the analysis of continuous and classified attack data.The AdaBoost algorithm is used to classify network attack data that improved the detection effect of unbalanced data classification.We adopt the UNSW-NB15 dataset to test of this algorithm in the PyCharm environment.The results show that the detection rate of algorithm is99.27%and the false positive rate is lower than 0.98%.Comparative analysis shows that this algorithm has advantages over existing methods in terms of detection rate and false positive rate for small proportion of attack data.

Long-term impact of artificial intelligence on colorectal adenoma detection in high-risk colonoscopy

BACKGROUND Improved adenoma detection rate(ADR)has been demonstrated with artificial intelligence(AI)-assisted colonoscopy.However,data on the real-world appli-cation of AI and its effect on colorectal cancer(CRC)screening outcomes is limited.AIM To analyze the long-term impact of AI on a diverse at-risk patient population undergoing diagnostic colonoscopy for positive CRC screening tests or sympt-oms.METHODS AI software(GI Genius,Medtronic)was implemented into the standard proced-ure protocol in November 2022.Data was collected on patient demographics,procedure indication,polyp size,location,and pathology.CRC screening outcomes were evaluated before and at different intervals after AI introduction with one year of follow-up.RESULTS We evaluated 1008 colonoscopies(278 pre-AI,255 early post-AI,285 established post-AI,and 190 late post-AI).The ADR was 38.1%pre-AI,42.0%early post-AI(P=0.77),40.0%established post-AI(P=0.44),and 39.5%late post-AI(P=0.77).There were no significant differences in polyp detection rate(PDR,baseline 59.7%),advanced ADR(baseline 16.2%),and non-neoplastic PDR(baseline 30.0%)before and after AI introduction.CONCLUSION In patients with an increased pre-test probability of having an abnormal colonoscopy,the current generation of AI did not yield enhanced CRC screening metrics over high-quality colonoscopy.Although the potential of AI in colonoscopy is undisputed,current AI technology may not universally elevate screening metrics across all situations and patient populations.Future studies that analyze different AI systems across various patient populations are needed to determine the most effective role of AI in optimizing CRC screening in clinical practice.

Study on Real-Time Heart Rate Detection Based on Multi-People

Heart rate is an important vital characteristic which indicates physical and mental health status.Typically heart rate measurement instruments require direct contact with the skin which is time-consuming and costly.Therefore,the study of non-contact heart rate measurement methods is of great importance.Based on the principles of photoelectric volumetric tracing,we use a computer device and camera to capture facial images,accurately detect face regions,and to detect multiple facial images using a multi-target tracking algorithm.Then after the regional segmentation of the facial image,the signal acquisition of the region of interest is further resolved.Finally,frequency detection of the collected Photo-plethysmography(PPG)and Electrocardiography(ECG)signals is completed with peak detection,Fourier analysis,and a Waveletfilter.The experimental results show that the subject’s heart rate can be detected quickly and accurately even when monitoring multiple facial targets simultaneously.

Effect of quality of bowel preparation on quality indicators of adenoma detection rates and colonoscopy completion rates

Background and aim:Adequate bowel preparation is important for safe and effective colonoscopy.Quality indicators(QI)for colonoscopy include achieving at least 95%completion rate and an adenoma detection rate(ADR)of at least 25%in average-risk men and 15%in average-risk women aged over 50.Our aim was to investigate the impact of bowel preparation on ADR and colonoscopy completion rates.Methods:This retrospective cohort study included patients who underwent colonoscopy between January 2008 and December 2009.The main outcome measurements were ADR and colonoscopy completion rates to the cecum.Results:A total of 2519 patients was included;1030(41.0%)had excellent preparation,1145(45.5%)good-,240(9.5%)fair-,and 104(4.1%)poor preparation.Colonoscopy completion rates were significantly lower in patients with poor or fair preparation(72.1%and 75.4%,respectively)than in those with good and excellent preparation(99.7%and 99.9%,respectively;P<0.001),and significantly lower than the QI of 95%(P<0.001).ADR in men and women combined was similar in all four grades of preparation(excellent,good,fair and poor)at 24.2%vs.26.8%vs.32.1%vs.22.1%,respectively;P¼0.06.All the groups had ADR above the QI(25%for men and 15%for women)with evidence of significantly higher ADR in the women with excellent or good preparation and in men with excellent,good or fair preparation.On multivariate analysis,male gender was significantly associated with increased ADR(P<0.001),while the quality of bowel preparation did not influence ADR.Conclusions:Patients with fair and poor standards of preparation have significantly lower colonoscopy completion rates than those with excellent and good preparation.However,there was no difference in ADR between the different grades of preparation.

Efficacy of image-enhanced endoscopy for colorectal adenoma detection:A multicenter,randomized trial

BACKGROUND Improved adenoma detection at colonoscopy has decreased the risk of developing colorectal cancer.However,whether image-enhanced endoscopy(IEE)further improves the adenoma detection rate(ADR)is controversial.AIM To compare IEE with white-light imaging(WLI)endoscopy for the detection and identification of colorectal adenoma.METHODS This was a multicenter,randomized,controlled trial.Participants were enrolled between September 2019 to April 2021 from 4 hospital in China.Patients were randomly assigned to an IEE group with WLI on entry and IEE on withdrawal(n=2113)or a WLI group with WLI on both entry and withdrawal(n=2098).The primary outcome was the ADR.The secondary endpoints were the polyp detection rate(PDR),adenomas per colonoscopy,adenomas per positive colonoscopy,and factors related to adenoma detection.RESULTS A total of 4211 patients(966 adenomas)were included in the analysis(mean age,56.7 years,47.1%male).There were 2113 patients(508 adenomas)in the IEE group and 2098 patients(458 adenomas)in the WLI group.The ADR in two group were not significantly different[24.0%vs 21.8%,1.10,95%confidence interval(CI):0.99-1.23,P=0.09].The PDR was higher with IEE group(41.7%)than with WLI group(36.1%,1.16,95%CI:1.07-1.25,P=0.01).Differences in mean withdrawal time(7.90±3.42 min vs 7.85±3.47 min,P=0.30)and adenomas per colonoscopy(0.33±0.68 vs 0.28±0.62,P=0.06)were not significant.Subgroup analysis found that with narrowband imaging(NBI),between-group differences in the ADR,were not significant(23.7%vs 21.8%,1.09,95%CI:0.97-1.22,P=0.15),but were greater with linked color imaging(30.9%vs 21.8%,1.42,95%CI:1.04-1.93,P=0.04).the second-generation NBI(2G-NBI)had an advantage of ADR than both WLI and the first-generation NBI(27.0%vs 21.8%,P=0.01;27.0%vs 21.2.0%,P=0.01).CONCLUSION This prospective study confirmed that,among Chinese,IEE didn’t increase the ADR compared with WLI,but 2G-NBI increase the ADR.

An Intelligent Intrusion Detection System in Smart Grid Using PRNN Classifier

Typically,smart grid systems enhance the ability of conventional power system networks as it is vulnerable to several kinds of attacks.These vulnerabil-ities might cause the attackers or intruders to collapse the entire network system thus breaching the confidentiality and integrity of smart grid systems.Thus,for this purpose,Intrusion detection system(IDS)plays a pivotal part in offering a reliable and secured range of services in the smart grid framework.Several exist-ing approaches are there to detect the intrusions in smart grid framework,however they are utilizing an old dataset to detect anomaly thus resulting in reduced rate of detection accuracy in real-time and huge data sources.So as to overcome these limitations,the proposed technique is presented which employs both real-time raw data from the smart grid network and KDD99 dataset thus detecting anoma-lies in the smart grid network.In the grid side data acquisition,the power trans-mitted to the grid is checked and enhanced in terms of power quality by eradicating distortion in transmission lines.In this approach,power quality in the smart grid network is enhanced by rectifying the fault using a FACT device termed UPQC(Unified Power Quality Controller)and thereby storing the data in cloud storage.The data from smart grid cloud storage and KDD99 are pre-pro-cessed and are optimized using Improved Aquila Swarm Optimization(IASO)to extract optimal features.The probabilistic Recurrent Neural Network(PRNN)classifier is then employed for the prediction and classification of intrusions.At last,the performance is estimated and the outcomes are projected in terms of grid voltage,grid current,Total Harmonic Distortion(THD),voltage sag/swell,accu-racy,precision,recall,F-score,false acceptance rate(FAR),and detection rate of the classifier.The analysis is compared with existing techniques to validate the proposed model efficiency.

Diagnostic yield of third eye retroscope on adenoma detection during colonoscopy:A systematic review and meta-analysis

AIM： To determine the diagnostic yield of the “third eye retroscope”, on adenoma detection rate during screening colonoscopy.METHODS： The “third eye retroscope” when used with standard colonoscopy provides an additional retro-grade view to visualize lesions on the proximal aspects of folds and fexures. We searched MEDLINE （PubMed and Ovid）, SCOPUS （including MEDLINE and EMBASE databases）, Cochrane Database of Systemic Reviews, Google Scholar, and CINAHL Plus databases to identify studies that evaluated diagnostic yield of “third eye retroscope” during screening colonoscopy. DerSimonian Laird random effects model was used to generate the overall effect for each outcome. We evaluated statistical heterogeneity among the studies by using the Cochran Q statistic and quantifed by I2 statistics.RESULTS： Four distinct studies with a total of 920 pa-tients, mean age 59.83 （95%CI： 56.77-62.83） years, were included in the review. The additional adenoma detection rate （AADR） defined as the number of ad-ditional adenomas identified due to “third eye retro-scope” device in comparison to standard colonoscopy alone was 19.9% （95%CI： 7.3-43.9）. AADR for right and left colon were 13.9% （95%CI： 9.4-20） and 10.7 （95%CI： 1.9-42）, respectively. AADR for polyps ≥ 6 mm and ≥ 10 mm were 24.6% （95%CI： 16.6-34.9） and 24.2% （95%CI： 12.9-40.8）, respectively. The ad-ditional polyp detection rate defined as the number of additional polyps identifed due to “third eye retro-scope” device in comparison to standard colonoscopyalone was 19.8% （95%CI： 7.9-41.8）. There were no complications reported with use of “third eye retro-scope” device.CONCLUSION： The “third eye retroscope” device when used with standard colonoscopy is safe and de-tects 19.9% additional adenomas, compared to stan-dard colonoscopy alone.

Evaluation of computer aided detection during colonoscopy among Veterans:Randomized clinical trial

BACKGROUND There has been significant interest in use of computer aided detection(CADe)devices in colonoscopy to improve polyp detection and reduce miss rate.AIM To investigate the use of CADe amongst veterans.METHODS Between September 2020 and December 2021,we performed a randomized controlled trial to evaluate the impact of CADe.Patients at Veterans Affairs Palo Alto Health Care System presenting for screening or low-risk surveillance were randomized to colonoscopy performed with or without CADe.Primary outcomes of interest included adenoma detection rate(ADR),adenomas per colonoscopy(APC),and adenomas per extraction.In addition,we measured serrated polyps per colonoscopy,non-adenomatous,non-serrated polyps per colonoscopy,serrated polyp detection rate,and procedural time.RESULTS A total of 244 patients were enrolled(124 with CADe),with similar patient characteristics(age,sex,body mass index,indication)between the two groups.Use of CADe was found to have decreased number of adenomas(1.79 vs 2.53,P=0.030)per colonoscopy compared to without CADe.There was no significant difference in number of serrated polyps or non-adenomatous non-serrated polyps per colonoscopy between the two groups.Overall,use of CADe was found to have lower ADR(68.5%vs 80.0%,P=0.041)compared to without use of CADe.Serrated polyp detection rate was lower with CADe(3.2%vs 7.5%)compared to without CADe,but this was not statistically significant(P=0.137).There was no significant difference in withdrawal and procedure times between the two groups or in detection of adenomas per extraction(71.4%vs 73.1%,P=0.613).No adverse events were identified.CONCLUSION While several randomized controlled trials have demonstrated improved ADR and APC with use of CADe,in this RCT performed at a center with high ADR,use of CADe was found to have decreased APC and ADR.Further studies are needed to understand the true impact of CADe on performance quality among endoscopists as well as determine criteria for endoscopists to consider when choosing to adopt CADe in their practices.

Adaptive learning rate GMM for moving object detection in outdoor surveillance for sudden illumination changes

A dynamic learning rate Gaussian mixture model（GMM）algorithm is proposed to deal with the problem of slow adaption of GMM in the case of moving object detection in the outdoor surveillance,especially in the presence of sudden illumination changes.The GMM is mostly used for detecting objects in complex scenes for intelligent monitoring systems.To solve this problem,a mixture Gaussian model has been built for each pixel in the video frame,and according to the scene change from the frame difference,the learning rate of GMM can be dynamically adjusted.The experiments show that the proposed method gives good results with an adaptive GMM learning rate when we compare it with GMM method with a fixed learning rate.The method was tested on a certain dataset,and tests in the case of sudden natural light changes show that our method has a better accuracy and lower false alarm rate.

Heart Rate Detection Based on Facial Video

Heart rate is an important data reflecting human vital characteristics and an important reference index to describe human physical and mental state.Currently,widely used heart rate measurement devices require direct contact with a person’s skin,which is not suitable for people with burns,delicate skin,newborns and the elderly.Therefore,the research of non-contact heart rate measurement method is of great significance.Based on the basic principle of Photoplethysmography,we use the camera of computer equipment to capture the face image,detect the face region accurately,and detect multiple faces in the image based on multi-target tracking algorithm.Then the region segmentation of the face image is carried out to further realize the signal acquisition of the region of interest.Finally,peak detection,Fourier analysis and wavelet analysis were used to detect the frequency of PPG and ECG signals.The experimental results show that the heart rate information can be quickly and accurately detected even in the case of monitoring multiple face targets.

Standard-definition White-light,High-definition White-light versus Narrow-band Imaging Endoscopy for Detecting Colorectal Adenomas:A Multicenter Randomized Controlled Trial

Objective This study aimed to compare the performance of standard-definition white-light endoscopy(SD-WL),high-definition white-light endoscopy(HD-WL),and high-definition narrow-band imaging(HD-NBI)in detecting colorectal lesions in the Chinese population.Methods This was a multicenter,single-blind,randomized,controlled trial with a non-inferiority design.Patients undergoing endoscopy for physical examination,screening,and surveillance were enrolled from July 2017 to December 2020.The primary outcome measure was the adenoma detection rate(ADR),defined as the proportion of patients with at least one adenoma detected.The associated factors for detecting adenomas were assessed using univariate and multivariate logistic regression.Results Out of 653 eligible patients enrolled,data from 596 patients were analyzed.The ADRs were 34.5%in the SD-WL group,33.5%in the HD-WL group,and 37.5%in the HD-NBI group(P=0.72).The advanced neoplasm detection rates(ANDRs)in the three arms were 17.1%,15.5%,and 10.4%(P=0.17).No significant differences were found between the SD group and HD group regarding ADR or ANDR(ADR:34.5%vs.35.6%,P=0.79;ANDR:17.1%vs.13.0%,P=0.16,respectively).Similar results were observed between the HD-WL group and HD-NBI group(ADR:33.5%vs.37.7%,P=0.45;ANDR:15.5%vs.10.4%,P=0.18,respectively).In the univariate and multivariate logistic regression analyses,neither HD-WL nor HD-NBI led to a significant difference in overall adenoma detection compared to SD-WL(HD-WL:OR 0.91,P=0.69;HD-NBI:OR 1.15,P=0.80).Conclusion HD-NBI and HD-WL are comparable to SD-WL for overall adenoma detection among Chinese outpatients.It can be concluded that HD-NBI or HD-WL is not superior to SD-WL,but more effective instruction may be needed to guide the selection of different endoscopic methods in the future.Our study’s conclusions may aid in the efficient allocation and utilization of limited colonoscopy resources,especially advanced imaging technologies.

Human-artificial intelligence interaction in gastrointestinal endoscopy

The number and variety of applications of artificial intelligence(AI)in gastr-ointestinal(GI)endoscopy is growing rapidly.New technologies based on machine learning(ML)and convolutional neural networks(CNNs)are at various stages of development and deployment to assist patients and endoscopists in preparing for endoscopic procedures,in detection,diagnosis and classification of pathology during endoscopy and in confirmation of key performance indicators.Platforms based on ML and CNNs require regulatory approval as medical devices.Interactions between humans and the technologies we use are complex and are influenced by design,behavioural and psychological elements.Due to the substantial differences between AI and prior technologies,important differences may be expected in how we interact with advice from AI technologies.Human-AI interaction(HAII)may be optimised by developing AI algorithms to minimise false positives and designing platform interfaces to maximise usability.Human factors influencing HAII may include automation bias,alarm fatigue,algorithm aversion,learning effect and deskilling.Each of these areas merits further study in the specific setting of AI applications in GI endoscopy and professional societies should engage to ensure that sufficient emphasis is placed on human-centred design in development of new AI technologies.