Artificial intelligence and colonoscopy experience: lessons from two randomised trials

An artificial intelligence-assisted system versus white light endoscopy alone for adenoma detection in individuals with Lynch syndrome (TIMELY): an international, multicentre, randomised controlled trial

BACKGROUND

Computer-aided detection (CADe) systems for colonoscopy have been shown to increase small polyp detection during colonoscopy in the general population. People with Lynch syndrome represent an ideal target population for CADe-assisted colonoscopy because adenomas, the primary cancer precursor lesions, are characterised by their small size and higher likelihood of showing advanced histology. We aimed to evaluate the performance of CADe-assisted colonoscopy in detecting adenomas in individuals with Lynch syndrome.

METHODS

TIMELY was an international, multicentre, parallel, randomised controlled trial done in 11 academic centres and six community centres in Belgium, Germany, Italy, and Spain. We enrolled individuals aged 18 years or older with pathogenic or likely pathogenic MLH1, MSH2, MSH6, or EPCAM variants. Participants were consecutively randomly assigned (1:1) to either CADe (GI Genius) assisted white light endoscopy (WLE) or WLE alone. A centre-stratified randomisation sequence was generated through a computer-generated system with a separate randomisation list for each centre according to block-permuted randomisation (block size 26 patients per centre). Allocation was automatically provided by the online AEG-REDCap database. Participants were masked to the random assignment but endoscopists were not. The primary outcome was the mean number of adenomas per colonoscopy, calculated by dividing the total number of adenomas detected by the total number of colonoscopies and assessed in the intention-to-treat population. This trial is registered with ClinicalTrials.gov, NCT04909671.

FINDINGS

Between Sept 13, 2021, and April 6, 2023, 456 participants were screened for eligibility, 430 of whom were randomly assigned to receive CADe-assisted colonoscopy (n=214) or WLE (n=216). 256 (60%) participants were female and 174 (40%) were male. In the intention-to-treat analysis, the mean number of adenomas per colonoscopy was 0·64 (SD 1·57) in the CADe group and 0·64 (1·17) in the WLE group (adjusted rate ratio 1·03 [95% CI 0·72-1·47); p=0·87). No adverse events were reported during the trial.

INTERPRETATION

In this multicentre international trial, CADe did not improve the detection of adenomas in individuals with Lynch syndrome. High-quality procedures and thorough inspection and exposure of the colonic mucosa remain the cornerstone in surveillance of Lynch syndrome.

FUNDING

Spanish Gastroenterology Association, Spanish Society of Digestive Endoscopy, European Society of Gastrointestinal Endoscopy, Societat Catalana de Digestologia, Instituto Carlos III, Beca de la Marato de TV3 2020. Co-funded by the European Union.

Assessment of the role of false-positive alerts in computer-aided polyp detection for assistance capabilities

BACKGROUND AND AIM

False positives (FPs) pose a significant challenge in the application of artificial intelligence (AI) for polyp detection during colonoscopy. The study aimed to quantitatively evaluate the impact of computer-aided polyp detection (CADe) systems' FPs on endoscopists.

METHODS

The model's FPs were categorized into four gradients: 0-5, 5-10, 10-15, and 15-20 FPs per minute (FPPM). Fifty-six colonoscopy videos were collected for a crossover study involving 10 endoscopists. Polyp missed rate (PMR) was set as primary outcome. Subsequently, to further verify the impact of FPPM on the assistance capability of AI in clinical environments, a secondary analysis was conducted on a prospective randomized controlled trial (RCT) from Renmin Hospital of Wuhan University in China from July 1 to October 15, 2020, with the adenoma detection rate (ADR) as primary outcome.

RESULTS

Compared with routine group, CADe reduced PMR when FPPM was less than 5. However, with the continuous increase of FPPM, the beneficial effect of CADe gradually weakens. For secondary analysis of RCT, a total of 956 patients were enrolled. In AI-assisted group, ADR is higher when FPPM ≤ 5 compared with FPPM > 5 (CADe group: 27.78% vs 11.90%; P = 0.014; odds ratio [OR], 0.351; 95% confidence interval [CI], 0.152-0.812; COMBO group: 38.40% vs 23.46%, P = 0.029; OR, 0.427; 95% CI, 0.199-0.916). After AI intervention, ADR increased when FPPM ≤ 5 (27.78% vs 14.76%; P = 0.001; OR, 0.399; 95% CI, 0.231-0.690), but no statistically significant difference was found when FPPM > 5 (11.90% vs 14.76%, P = 0.788; OR, 1.111; 95% CI, 0.514-2.403).

CONCLUSION

The level of FPs of CADe does affect its effectiveness as an aid to endoscopists, with its best effect when FPPM is less than 5.

Colonoscopic surveillance in Lynch syndrome: guidelines in perspective

Lynch syndrome predisposes to a high risk of colorectal cancer and colonoscopy remains the primary preventive strategy. The prevention of colorectal cancer through colonoscopy relies on identifying and removing adenomas, the main precursor lesion. Nevertheless, colonoscopy is not an optimal strategy since post-colonoscopy colorectal cancer remains an important issue. In continuation of a 2021 journal review, the present article seeks to offer an updated perspective by examining relevant articles from the past 3 years. We place recent findings in the context of existing guidelines, with a specific focus on colonoscopy surveillance. Key aspects explored include colonoscopy quality standards, timing of initiation, and surveillance intervals. Our review provides a comprehensive analysis of adenoma-related insights in Lynch syndrome, delving into emerging technologies like virtual chromoendoscopy and artificial intelligence-assisted endoscopy. This review aims to contribute valuable insights into the topic of colonoscopy surveillance in Lynch syndrome.

ENDOANGEL improves detection of missed colorectal adenomas in second colonoscopy: A retrospective study

The ENDOANGEL (EN) computer-assisted detection technique has emerged as a promising tool for enhancing the detection rate of colorectal adenomas during colonoscopies. However, its efficacy in identifying missed adenomas during subsequent colonoscopies remains unclear. Thus, we herein aimed to compare the adenoma miss rate (AMR) between EN-assisted and standard colonoscopies. Data from patients who underwent a second colonoscopy (EN-assisted or standard) within 6 months between September 2022 and May 2023 were analyzed. The EN-assisted group exhibited a significantly higher AMR (24.3% vs 11.9%, P = .005) than the standard group. After adjusting for potential confounders, multivariable analysis revealed that the EN-assisted group had a better ability to detect missed adenomas than the standard group (odds ratio = 2.89; 95% confidence interval = 1.14-7.80, P = .029). These findings suggest that EN-assisted colonoscopy represents a valuable advancement in improving AMR compared with standard colonoscopy. The integration of EN-assisted colonoscopy into routine clinical practice may offer significant benefits to patients requiring hospital resection of lesions following adenoma detection during their first colonoscopy.

Enhancing human-AI collaboration: The case of colonoscopy

Diagnostic errors impact patient health and healthcare costs. Artificial Intelligence (AI) shows promise in mitigating this burden by supporting Medical Doctors in decision-making. However, the mere display of excellent or even superhuman performance by AI in specific tasks does not guarantee a positive impact on medical practice. Effective AI assistance should target the primary causes of human errors and foster effective collaborative decision-making with human experts who remain the ultimate decision-makers. In this narrative review, we apply these principles to the specific scenario of AI assistance during colonoscopy. By unraveling the neurocognitive foundations of the colonoscopy procedure, we identify multiple bottlenecks in perception, attention, and decision-making that contribute to diagnostic errors, shedding light on potential interventions to mitigate them. Furthermore, we explored how existing AI devices fare in clinical practice and whether they achieved an optimal integration with the human decision-maker. We argue that to foster optimal Human-AI collaboration, future research should expand our knowledge of factors influencing AI's impact, establish evidence-based cognitive models, and develop training programs based on them. These efforts will enhance human-AI collaboration, ultimately improving diagnostic accuracy and patient outcomes. The principles illuminated in this review hold more general value, extending their relevance to a wide array of medical procedures and beyond.

Artificial Intelligence-assisted colonoscopy and colorectal cancer screening: Where are we going?

Colorectal cancer is a significant global health concern, necessitating effective screening strategies to reduce its incidence and mortality rates. Colonoscopy plays a crucial role in the detection and removal of colorectal neoplastic precursors. However, there are limitations and variations in the performance of endoscopists, leading to missed lesions and suboptimal outcomes. The emergence of artificial intelligence (AI) in endoscopy offers promising opportunities to improve the quality and efficacy of screening colonoscopies. In particular, AI applications, including computer-aided detection (CADe) and computer-aided characterization (CADx), have demonstrated the potential to enhance adenoma detection and optical diagnosis accuracy. Additionally, AI-assisted quality control systems aim to standardize the endoscopic examination process. This narrative review provides an overview of AI principles and discusses the current knowledge on AI-assisted endoscopy in the context of screening colonoscopies. It highlights the significant role of AI in improving lesion detection, characterization, and quality assurance during colonoscopy. However, further well-designed studies are needed to validate the clinical impact and cost-effectiveness of AI-assisted colonoscopy before its widespread implementation.

A computer-aided detection system in the everyday setting of diagnostic, screening, and surveillance colonoscopy: an international, randomized trial

BACKGROUND

 Computer-aided detection (CADe) has been developed to improve detection during colonoscopy. After initial reports of high efficacy, there has been an increasing recognition of variability in the effectiveness of CADe systems. The aim of this study was to evaluate a CADe system in a varied colonoscopy population.

METHODS

 A multicenter, randomized trial was conducted at seven hospitals (both university and non-university) in Europe and Canada. Participants referred for diagnostic, non-immunochemical fecal occult blood test (iFOBT) screening, or surveillance colonoscopy were randomized (1:1) to undergo CADe-assisted or conventional colonoscopy by experienced endoscopists. Participants with insufficient bowel preparation were excluded from the analysis. The primary outcome was adenoma detection rate (ADR). Secondary outcomes included adenomas per colonoscopy (APC) and sessile serrated lesions (SSLs) per colonoscopy.

RESULTS

 581 participants were enrolled, of whom 497 were included in the final analysis: 250 in the CADe arm and 247 in the conventional colonoscopy arm. The indication was surveillance in 202/497 colonoscopies (40.6 %), diagnostic in 199/497 (40.0 %), and non-iFOBT screening in 96/497 (19.3 %). Overall, ADR (38.4 % vs. 37.7 %; P = 0.43) and APC (0.66 vs. 0.66; P = 0.97) were similar between CADe and conventional colonoscopy. SSLs per colonoscopy was increased (0.30 vs. 0.19; P = 0.049) in the CADe arm vs. the conventional colonoscopy arm.

CONCLUSIONS

 In this study conducted by experienced endoscopists, CADe did not result in a statistically significant increase in ADR. However, the ADR of our control group substantially surpassed our sample size assumptions, increasing the risk of an underpowered trial.

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 application 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 symptoms.

METHODS

AI software (GI Genius, Medtronic) was implemented into the standard procedure 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.

Role of artificial intelligence in colorectal cancer

The sphere of artificial intelligence (AI) is ever expanding. Applications for clinical practice have been emerging over recent years. Although its uptake has been most prominent in endoscopy, this represents only one aspect of holistic patient care. There are a multitude of other potential avenues in which gastrointestinal care may be involved. We aim to review the role of AI in colorectal cancer as a whole. We performed broad scoping and focused searches of the applications of AI in the field of colorectal cancer. All trials including qualitative research were included from the year 2000 onwards. Studies were grouped into pre-operative, intra-operative and post-operative aspects. Pre-operatively, the major use is with endoscopic recognition. Colonoscopy has embraced the use for human derived classifications such as Narrow-band Imaging International Colorectal Endoscopic, Japan Narrow-band Imaging Expert Team, Paris and Kudo. However, novel detection and diagnostic methods have arisen from advances in AI classification. Intra-operatively, adjuncts such as image enhanced identification of structures and assessment of perfusion have led to improvements in clinical outcomes. Post-operatively, monitoring and surveillance have taken strides with potential socioeconomic and environmental savings. The uses of AI within the umbrella of colorectal surgery are multiple. We have identified existing technologies which are already augmenting cancer care. The future applications are exciting and could at least match, if not surpass human standards.

Investigation of the relationship between colonoscopy insertion difficulty factors and endoscope shape using an endoscopic position detection unit

In this study, we investigated the relationship between the cecal intubation time (CIT) and the form and method used for passing through the sigmoid/descending colon junction (SDJ) and the hepatic flexure using an endoscopic position detection unit (UPD), with reference to various factors [age, sex, body mass index (BMI), history of abdominal and pelvic surgery, and diverticulum]. A total of 152 patients underwent colonoscopy with UPD. The mean age was 66.9 ± 12.4 years, and the male to female ratio was 3.6:1. The average CIT time was 14.3 ± 8.2 min. Age, number of experienced endoscopies, history of abdominal and pelvic surgery, BMI, and diverticulum were associated with prolonged CIT; SDJ passage pattern was straight: 8.6 ± 5.0, alpha loop: 11.8 ± 5.6, puzzle ring-like loop: 20.2 ± 5.0, reverse alpha loop: 22.4 ± 9.7, and other loop: 24.7 ± 10.5. The hepatic flexure passing method was in the following order: right rotation maneuver: 12.6 ± 6.6, push maneuver: 15.1 ± 5.9, and right rotation with positional change maneuver: 20.5 ± 7.2. In conclusion, colonoscopy with UPD revealed an association between CIT and SDJ passage pattern and hepatic flexure passing method.

GI genius endoscopy module: a clinical profile

INTRODUCTION

The identification of early-stage colorectal cancers (CRC) and the resection of pre-cancerous neoplastic lesions through colonoscopy allows to decrease both CRC incidence and mortality. However, colonoscopy miss rates up to 26% for adenomas and 9% for advanced adenomas have been reported. In recent years, artificial intelligence (AI) systems have been emerging as easy-to-use tools, potentially lowering the risk of missing lesions.

AREAS COVERED

This review paper focuses on GI Genius device (Medtronic Co. Minneapolis, MN, U.S.A.) a computer-assisted tool designed to assist endoscopists during standard white-light colonoscopies in detecting mucosal lesions.

EXPERT OPINION

Randomized controlled trials (RCTs) suggest that GI Genius is a safe and effective tool for improving adenoma detection, especially in CRC screening and surveillance colonoscopies. However, its impact seems to be less significant among experienced endoscopists and in real-world clinical scenarios compared to the controlled conditions of RCTs. Furthermore, it appears that GI Genius mainly enhances the detection of non-advanced, small polyps, but does not significantly impact the identification of advanced and difficult-to-detect adenoma. When using GI Genius, no complications were documented. Only a small number of studies reported an increased in withdrawal time or the removal of non-neoplastic lesions.

Randomised controlled trials evaluating artificial intelligence in clinical practice: a scoping review

This scoping review of randomised controlled trials on artificial intelligence (AI) in clinical practice reveals an expanding interest in AI across clinical specialties and locations. The USA and China are leading in the number of trials, with a focus on deep learning systems for medical imaging, particularly in gastroenterology and radiology. A majority of trials (70 [81%] of 86) report positive primary endpoints, primarily related to diagnostic yield or performance; however, the predominance of single-centre trials, little demographic reporting, and varying reports of operational efficiency raise concerns about the generalisability and practicality of these results. Despite the promising outcomes, considering the likelihood of publication bias and the need for more comprehensive research including multicentre trials, diverse outcome measures, and improved reporting standards is crucial. Future AI trials should prioritise patient-relevant outcomes to fully understand AI's true effects and limitations in health care.

Evaluating false-positive detection in a computer-aided detection system for colonoscopy

BACKGROUND AND AIM

Computer-aided detection (CADe) systems can efficiently detect polyps during colonoscopy. However, false-positive (FP) activation is a major limitation of CADe. We aimed to compare the rate and causes of FP using CADe before and after an update designed to reduce FP.

METHODS

We analyzed CADe-assisted colonoscopy videos recorded between July 2022 and October 2022. The number and causes of FPs and excessive time spent by the endoscopist on FP (ET) were compared pre- and post-update using 1:1 propensity score matching.

RESULTS

During the study period, 191 colonoscopy videos (94 and 97 in the pre- and post-update groups, respectively) were recorded. Propensity score matching resulted in 146 videos (73 in each group). The mean number of FPs and median ET per colonoscopy were significantly lower in the post-update group than those in the pre-update group (4.2 ± 3.7 vs 18.1 ± 11.1; P < 0.001 and 0 vs 16 s; P < 0.001, respectively). Mucosal tags, bubbles, and folds had the strongest association with decreased FP post-update (pre-update vs post-update: 4.3 ± 3.6 vs 0.4 ± 0.8, 0.32 ± 0.70 vs 0.04 ± 0.20, and 8.6 ± 6.7 vs 1.6 ± 1.7, respectively). There was no significant decrease in the true positive rate (post-update vs pre-update: 95.0% vs 99.2%; P = 0.09) or the adenoma detection rate (post-update vs pre-update: 52.1% vs 49.3%; P = 0.87).

CONCLUSIONS

The updated CADe can reduce FP without impairing polyp detection. A reduction in FP may help relieve the burden on endoscopists.

Impact of study design on adenoma detection in the evaluation of artificial intelligence-aided colonoscopy: a systematic review and meta-analysis

BACKGROUND AND AIMS

Randomized controlled trials (RCTs) have reported that artificial intelligence (AI) improves endoscopic polyp detection. Different methodologies-namely, parallel and tandem designs-have been used to evaluate the efficacy of AI-assisted colonoscopy in RCTs. Systematic reviews and meta-analyses have reported a pooled effect that includes both study designs. However, it is unclear whether there are inconsistencies in the reported results of these 2 designs. Here, we aimed to determine whether study characteristics moderate between-trial differences in outcomes when evaluating the effectiveness of AI-assisted polyp detection.

METHODS

A systematic search of Ovid MEDLINE, Embase, Cochrane Central, Web of Science, and IEEE Xplore was performed through March 1, 2023, for RCTs comparing AI-assisted colonoscopy with routine high-definition colonoscopy in polyp detection. The primary outcome of interest was the impact of study type on the adenoma detection rate (ADR). Secondary outcomes included the impact of the study type on adenomas per colonoscopy and withdrawal time, as well as the impact of geographic location, AI system, and endoscopist experience on ADR. Pooled event analysis was performed using a random-effects model.

RESULTS

Twenty-four RCTs involving 17,413 colonoscopies (AI assisted: 8680; non-AI assisted: 8733) were included. AI-assisted colonoscopy improved overall ADR (risk ratio [RR], 1.24; 95% confidence interval [CI], 1.17-1.31; I2 = 53%; P < .001). Tandem studies collectively demonstrated improved ADR in AI-aided colonoscopies (RR, 1.18; 95% CI, 1.08-1.30; I2 = 0%; P < .001), as did parallel studies (RR, 1.26; 95% CI, 1.17-1.35; I2 = 62%; P < .001), with no statistical subgroup difference between study design. Both tandem and parallel study designs revealed improvement in adenomas per colonoscopy in AI-aided colonoscopies, but this improvement was more marked among tandem studies (P < .001). AI assistance significantly increased withdrawal times for parallel (P = .002), but not tandem, studies. ADR improvement was more marked among studies conducted in Asia compared to Europe and North America in a subgroup analysis (P = .007). Type of AI system used or endoscopist experience did not affect overall improvement in ADR.

CONCLUSIONS

Either parallel or tandem study design can capture the improvement in ADR resulting from the use of AI-assisted polyp detection systems. Tandem studies powered to detect differences in endoscopic performance through paired comparison may be a resource-efficient method of evaluating new AI-assisted technologies.

Single Versus Second Observer vs Artificial Intelligence to Increase the ADENOMA Detection Rate of Colonoscopy-A Network Analysis

BACKGROUND AND AIMS

Screening colonoscopy has significantly contributed to the reduction of the incidence of colorectal cancer (CRC) and its associated mortality, with adenoma detection rate (ADR) as the quality marker. To increase the ADR, various solutions have been proposed including the utilization of Artificial Intelligence (AI) and employing second observers during colonoscopies. In the interest of AI improving ADR independently, without a second observer, and the operational similarity between AI and second observer, this network meta-analysis aims at evaluating the effectiveness of AI, second observer, and a single observer in improving ADR.

METHODS

We searched the Medline, Embase, Cochrane, Web of Science Core Collection, Korean Citation Index, SciELO, Global Index Medicus, and Cochrane. A direct head-to-head comparator analysis and network meta-analysis were performed using the random-effects model. The odds ratio (OR) was calculated with a 95% confidence interval (CI) and p-value < 0.05 was considered statistically significant.

RESULTS

We analyzed 26 studies, involving 22,560 subjects. In the direct comparative analysis, AI demonstrated higher ADR (OR: 0.668, 95% CI 0.595-0.749, p < 0.001) than single observer. Dual observer demonstrated a higher ADR (OR: 0.771, 95% CI 0.688-0.865, p < 0.001) than single operator. In network meta-analysis, results were consistent on the network meta-analysis, maintaining consistency. No statistical difference was noted when comparing AI to second observer. (RR 1.1 (0.9-1.2, p = 0.3). Results were consistent when evaluating only RCTs. Net ranking provided higher score to AI followed by second observer followed by single observer.

CONCLUSION

Artificial Intelligence and second-observer colonoscopy showed superior success in Adenoma Detection Rate when compared to single-observer colonoscopy. Although not statistically significant, net ranking model favors the superiority of AI to the second observer.

The Efficacy of Real-time Computer-aided Detection of Colonic Neoplasia in Community Practice: A Pragmatic Randomized Controlled Trial

BACKGROUND & AIMS

The use of computer-aided detection (CADe) has increased the adenoma detection rates (ADRs) during colorectal cancer (CRC) screening/surveillance in randomized controlled trials (RCTs) but has not shown benefit in real-world implementation studies. We performed a single-center pragmatic RCT to evaluate the impact of real-time CADe on ADRs in colonoscopy performed by community gastroenterologists.

METHODS

We enrolled 1100 patients undergoing colonoscopy for CRC screening, surveillance, positive fecal-immunohistochemical tests, and diagnostic indications at one community-based center from September 2022 to March 2023. Patients were randomly assigned (1:1) to traditional colonoscopy or real-time CADe. Blinded pathologists analyzed histopathologic findings. The primary outcome was ADR (the percentage of patients with at least 1 histologically proven adenoma or carcinoma). Secondary outcomes were adenomas detected per colonoscopy (APC), sessile-serrated lesion detection rate, and non-neoplastic resection rate.

RESULTS

The median age was 55.5 years (interquartile range, 50-62 years), 61% were female, 72.7% were of Hispanic ethnicity, and 9.1% had inadequate bowel preparation. The ADR for the CADe group was significantly higher than the traditional colonoscopy group (42.5% vs 34.4%; P = .005). The mean APC was significantly higher in the CADe group compared with the traditional colonoscopy group (0.89 ± 1.46 vs 0.60 ± 1.12; P < .001). The improvement in adenoma detection was driven by increased detection of <5 mm adenomas. CADe had a higher sessile-serrated lesion detection rate than traditional colonoscopy (4.7% vs 2.0%; P = .01). The improvement in ADR with CADe was significantly higher in the first half of the study (47.2% vs 33.7%; P = .002) compared with the second half (38.7% vs 34.9%; P = .33).

CONCLUSIONS

In a single-center pragmatic RCT, real-time CADe modestly improved ADR and APC in average-detector community endoscopists. (ClinicalTrials.gov number, NCT05963724).

Effect of Real-Time Computer-Aided Polyp Detection System (ENDO-AID) on Adenoma Detection in Endoscopists-in-Training: A Randomized Trial

BACKGROUND

The effect of computer-aided polyp detection (CADe) on adenoma detection rate (ADR) among endoscopists-in-training remains unknown.

METHODS

We performed a single-blind, parallel-group, randomized controlled trial in Hong Kong between April 2021 and July 2022 (NCT04838951). Eligible subjects undergoing screening/surveillance/diagnostic colonoscopies were randomized 1:1 to receive colonoscopies with CADe (ENDO-AID[OIP-1]) or not (control) during withdrawal. Procedures were performed by endoscopists-in-training with <500 procedures and <3 years' experience. Randomization was stratified by patient age, sex, and endoscopist experience (beginner vs intermediate level, <200 vs 200-500 procedures). Image enhancement and distal attachment devices were disallowed. Subjects with incomplete colonoscopies or inadequate bowel preparation were excluded. Treatment allocation was blinded to outcome assessors. The primary outcome was ADR. Secondary outcomes were ADR for different adenoma sizes and locations, mean number of adenomas, and non-neoplastic resection rate.

RESULTS

A total of 386 and 380 subjects were randomized to CADe and control groups, respectively. The overall ADR was significantly higher in the CADe group than in the control group (57.5% vs 44.5%; adjusted relative risk, 1.41; 95% CI, 1.17-1.72; P < .001). The ADRs for <5 mm (40.4% vs 25.0%) and 5- to 10-mm adenomas (36.8% vs 29.2%) were higher in the CADe group. The ADRs were higher in the CADe group in both the right colon (42.0% vs 30.8%) and left colon (34.5% vs 27.6%), but there was no significant difference in advanced ADR. The ADRs were higher in the CADe group among beginner (60.0% vs 41.9%) and intermediate-level (56.5% vs 45.5%) endoscopists. Mean number of adenomas (1.48 vs 0.86) and non-neoplastic resection rate (52.1% vs 35.0%) were higher in the CADe group.

CONCLUSIONS

Among endoscopists-in-training, the use of CADe during colonoscopies was associated with increased overall ADR. (ClinicalTrials.gov, Number: NCT04838951).

Predicting the effectiveness of drugs used for treating cardiovascular conditions in newborn infants

BACKGROUND

Cardiovascular support (CVS) treatment failure (TF) is associated with a poor prognosis in preterm infants.

METHODS

Medical charts of infants with a birth weight <1500 g who received either dopamine (Dp) or dobutamine (Db), were reviewed. Treatment response (TR) occurred if blood pressure increased >3rd centile for gestational age or superior vena cava flow was maintained >55 ml/kg/min, with decreased lactate or less negative base excess, without additional CVS. A predictive model of Dp and Db on TR was designed and the impact of TR on survival was analyzed.

RESULTS

Sixty-six infants (median gestational age 27.3 weeks, median birth weight 864 g) received Dp (n = 44) or Db (n = 22). TR occurred in 59% of the cases treated with Dp and 31% with Db, p = 0.04. Machine learning identified a model that correctly labeled Db response in 90% of the cases and Dp response in 61.4%. Sixteen infants died (9% of the TR group, 39% of the TF group; p = 0.004). Brain or gut morbidity-free survival was observed in 52% vs 30% in the TR and TF groups, respectively (p = 0.08).

CONCLUSIONS

New predictive models can anticipate Db but not Dp effectiveness in preterm infants. These algorithms may help the clinicians in the decision-making process.

IMPACT

Failure of cardiovascular support treatment increases the risk of mortality in very low birth weight infants. A predictive model built with machine learning techniques can help anticipate treatment response to dobutamine with high accuracy. Predictive models based on artificial intelligence may guide the clinicians in the decision-making process.

A computer-aided polyp detection system in screening and surveillance colonoscopy: an international, multicentre, randomised, tandem trial

BACKGROUND

Studies on the effect of computer-aided detection (CAD) in a daily clinical screening and surveillance colonoscopy population practice are scarce. The aim of this study was to evaluate a novel CAD system in a screening and surveillance colonoscopy population.

METHODS

This multicentre, randomised, controlled trial was done in ten hospitals in Europe, the USA, and Israel by 31 endoscopists. Patients referred for non-immunochemical faecal occult blood test (iFOBT) screening or surveillance colonoscopy were included. Patients were randomomly assigned to CAD-assisted colonoscopy or conventional colonoscopy; a subset was further randomly assigned to undergo tandem colonoscopy: CAD followed by conventional colonoscopy or conventional colonoscopy followed by CAD. Primary objectives included adenoma per colonoscopy (APC) and adenoma per extraction (APE). Secondary objectives included adenoma miss rate (AMR) in the tandem colonoscopies. The study was registered at ClinicalTrials.gov, NCT04640792.

FINDINGS

A total of 916 patients were included in the modified intention-to-treat analysis: 449 in the CAD group and 467 in the conventional colonoscopy group. APC was higher with CAD compared with conventional colonoscopy (0·70 vs 0·51, p=0·015; 314 adenomas per 449 colonoscopies vs 238 adenomas per 467 colonoscopies; poisson effect ratio 1·372 [95% CI 1·068-1·769]), while showing non-inferiority of APE compared with conventional colonoscopy (0·59 vs 0·66; p<0·001 for non-inferiority; 314 of 536 extractions vs 238 of 360 extractions). AMR in the 127 (61 with CAD first, 66 with conventional colonoscopy first) patients completing tandem colonoscopy was 19% (11 of 59 detected during the second pass) in the CAD first group and 36% (16 of 45 detected during the second pass) in the conventional colonoscopy first group (p=0·024).

INTERPRETATION

CAD increased adenoma detection in non-iFOBT screening and surveillance colonoscopies and reduced adenoma miss rates compared with conventional colonoscopy, without an increase in the resection of non-adenomatous lesions.

FUNDING

Magentiq Eye.

Concordance of randomised controlled trials for artificial intelligence interventions with the CONSORT-AI reporting guidelines

The Consolidated Standards of Reporting Trials extension for Artificial Intelligence interventions (CONSORT-AI) was published in September 2020. Since its publication, several randomised controlled trials (RCTs) of AI interventions have been published but their completeness and transparency of reporting is unknown. This systematic review assesses the completeness of reporting of AI RCTs following publication of CONSORT-AI and provides a comprehensive summary of RCTs published in recent years. 65 RCTs were identified, mostly conducted in China (37%) and USA (18%). Median concordance with CONSORT-AI reporting was 90% (IQR 77-94%), although only 10 RCTs explicitly reported its use. Several items were consistently under-reported, including algorithm version, accessibility of the AI intervention or code, and references to a study protocol. Only 3 of 52 included journals explicitly endorsed or mandated CONSORT-AI. Despite a generally high concordance amongst recent AI RCTs, some AI-specific considerations remain systematically poorly reported. Further encouragement of CONSORT-AI adoption by journals and funders may enable more complete adoption of the full CONSORT-AI guidelines.

Lynch Syndrome: From Multidisciplinary Management to Precision Prevention

BACKGROUND AND AIMS

Lynch syndrome (LS) is currently one of the most prevalent hereditary cancer conditions, accounting for 3% of all colorectal cancers and for up to 15% of those with DNA mismatch repair (MMR) deficiency, and it was one of the first historically identified. The understanding of the molecular carcinogenesis of LS tumors has progressed significantly in recent years. We aim to review the most recent advances in LS research and explore genotype-based approaches in surveillance, personalized cancer prevention, and treatment strategies.

METHODS

PubMed was searched to identify relevant studies, conducted up to December 2023, investigating molecular carcinogenesis in LS, surveillance strategies, cancer prevention, and treatment in LS tumors.

RESULTS

Multigene panel sequencing is becoming the benchmark in the diagnosis of LS, allowing for the detection of a pathogenic constitutional variant in one of the MMR genes. Emerging data from randomized controlled trials suggest possible preventive roles of resistant starch and/or aspirin in LS. Vaccination with immunogenic frameshift peptides appears to be a promising approach for both the treatment and prevention of LS-associated cancers, as evidenced by pre-clinical and preliminary phase 1/2a studies.

CONCLUSIONS

Although robust diagnostic algorithms, including prompt testing of tumor tissue for MMR defects and referral for genetic counselling, currently exist for suspected LS in CRC patients, the indications for LS screening in cancer-free individuals still need to be refined and standardized. Investigation into additional genetic and non-genetic factors that may explain residual rates of interval cancers, even in properly screened populations, would allow for more tailored preventive strategies.

Direct comparison of multiple computer-aided polyp detection systems

BACKGROUND AND STUDY AIMS

 Artificial intelligence (AI)-based systems for computer-aided detection (CADe) of polyps receive regular updates and occasionally offer customizable detection thresholds, both of which impact their performance, but little is known about these effects. This study aimed to compare the performance of different CADe systems on the same benchmark dataset.

METHODS

 101 colonoscopy videos were used as benchmark. Each video frame with a visible polyp was manually annotated with bounding boxes, resulting in 129 705 polyp images. The videos were then analyzed by three different CADe systems, representing five conditions: two versions of GI Genius, Endo-AID with detection Types A and B, and EndoMind, a freely available system. Evaluation included an analysis of sensitivity and false-positive rate, among other metrics.

RESULTS

 Endo-AID detection Type A, the earlier version of GI Genius, and EndoMind detected all 93 polyps. Both the later version of GI Genius and Endo-AID Type B missed 1 polyp. The mean per-frame sensitivities were 50.63 % and 67.85 %, respectively, for the earlier and later versions of GI Genius, 65.60 % and 52.95 %, respectively, for Endo-AID Types A and B, and 60.22 % for EndoMind.

CONCLUSIONS

 This study compares the performance of different CADe systems, different updates, and different configuration modes. This might help clinicians to select the most appropriate system for their specific needs.

Effect of artificial intelligence on novice-performed colonoscopy: a multicenter randomized controlled tandem study

BACKGROUND AND AIMS

The efficacy and safety of colonoscopy performed by artificial intelligence (AI)-assisted novices remain unknown. The aim of this study was to compare the lesion detection capability of novices, AI-assisted novices, and experts.

METHODS

This multicenter, randomized, noninferiority tandem study was conducted across 3 hospitals in China from May 1, 2022, to November 11, 2022. Eligible patients were randomized into 1 of 3 groups: the CN group (control novice group, withdrawal performed by a novice independently), the AN group (AI-assisted novice group, withdrawal performed by a novice with AI assistance), or the CE group (control expert group, withdrawal performed by an expert independently). Participants underwent a repeat colonoscopy conducted by an AI-assisted expert to evaluate the lesion miss rate and ensure lesion detection. The primary outcome was the adenoma miss rate (AMR).

RESULTS

A total of 685 eligible patients were analyzed: 229 in the CN group, 227 in the AN group, and 229 in the CE group. Both AMR and polyp miss rate were lower in the AN group than in the CN group (18.82% vs 43.69% [P < .001] and 21.23% vs 35.38% [P < .001], respectively). The noninferiority margin was met between the AN and CE groups of both AMR and polyp miss rate (18.82% vs 26.97% [P = .202] and 21.23% vs 24.10% [P < .249]).

CONCLUSIONS

AI-assisted colonoscopy lowered the AMR of novices, making them noninferior to experts. The withdrawal technique of new endoscopists can be enhanced by AI-assisted colonoscopy. (Clinical trial registration number: NCT05323279.).

Impact of an artificial intelligence-aided endoscopic diagnosis system on improving endoscopy quality for trainees in colonoscopy: Prospective, randomized, multicenter study

OBJECTIVE

This study was performed to evaluate whether the use of CAD EYE (Fujifilm, Tokyo, Japan) for colonoscopy improves colonoscopy quality in gastroenterology trainees.

METHODS

The patients in this multicenter randomized controlled trial were divided into Group A (observation using CAD EYE) and Group B (standard observation). Six trainees performed colonoscopies using a back-to-back method in pairs with gastroenterology experts. The primary end-point was the trainees' adenoma detection rate (ADR), and the secondary end-points were the trainees' adenoma miss rate (AMR) and Assessment of Competency in Endoscopy (ACE) tool scores. Each trainee's learning curve was evaluated using a cumulative sum (CUSUM) control chart.

RESULTS

We analyzed data for 231 patients (Group A, n = 113; Group B, n = 118). The ADR was not significantly different between the two groups. Group A had a significantly lower AMR (25.6% vs. 38.6%, P = 0.033) and number of missed adenomas per patient (0.5 vs. 0.9, P = 0.004) than Group B. Group A also had significantly higher ACE tool scores for pathology identification (2.26 vs. 2.07, P = 0.030) and interpretation and identification of pathology location (2.18 vs. 2.00, P = 0.038). For the CUSUM learning curve, Group A showed a trend toward a lower number of cases of missed multiple adenomas by the six trainees.

CONCLUSION

CAD EYE did not improve ADR but decreased the AMR and improved the ability to accurately locate and identify colorectal adenomas. CAD EYE can be assumed to be beneficial for improving colonoscopy quality in gastroenterology trainees.

TRIAL REGISTRATION

University Hospital Medical Information Network Clinical Trials Registry (UMIN000044031).

Diagnostic accuracy of endocytoscopy via artificial intelligence in colorectal lesions: A systematic review and meta‑analysis

BACKGROUND

Endocytoscopy (EC) is a nuclei and micro-vessels visualization in real-time and can facilitate "optical biopsy" and "virtual histology" of colorectal lesions. This study aimed to investigate the significance of employing artificial intelligence (AI) in the field of endoscopy, specifically in diagnosing colorectal lesions. The research was conducted under the supervision of experienced professionals and trainees.

METHODS

EMBASE, PubMed, Cochrane Library, Web of Science, Chinese National Knowledge Infrastructure (CNKI) database, and other potential databases were surveyed for articles related to the EC with AI published before September 2023. RevMan (5.40), Stata (14.0), and R software (4.1.0) were used for statistical assessment. Studies that measured the accuracy of EC using AI for colorectal lesions were included. Two authors independently assessed the selected studies and their extracted data. This included information such as the country, literature, total study population, study design, characteristics of the fundamental study and control groups, sensitivity, number of samples, assay methodology, specificity, true positives or negatives, and false positives or negatives. The diagnostic accuracy of EC by AI was determined by a bivariate random-effects model, avoiding a high heterogeneity effect. The ANOVA model was employed to determine the more effective approach.

RESULTS

A total of 223 studies were reviewed; 8 articles were selected that included 2984 patients (4241 lesions) for systematic review and meta-analysis. AI assessed 4069 lesions; experts diagnosed 3165 and 5014 by trainees. AI demonstrated high accuracy, sensitivity, and specificity levels in detecting colorectal lesions, with values of 0.93 (95% CI: 0.90, 0.95) and 0.94 (95% CI: 0.73, 0.99). Expert diagnosis was 0.90 (95% CI: 0.85, 0.94), 0.87 (95% CI: 0.78, 0.93), and trainee diagnosis was 0.74 (95% CI: 0.67, 0.79), 0.72 (95% CI: 0.62, 0.80). With the EC by AI, the AUC from SROC was 0.95 (95% CI: 0.93, 0.97), therefore classified as excellent category, expert showed 0.95 (95% CI: 0.93, 0.97), and the trainee had 0.79 (95% CI: 0.75, 0.82). The superior index from the ANOVA model was 4.00 (1.15,5.00), 2.00 (1.15,5.00), and 0.20 (0.20,0.20), respectively. The examiners conducted meta-regression and subgroup analyses to evaluate the presence of heterogeneity. The findings of these investigations suggest that the utilization of NBI technology was correlated with variability in sensitivity and specificity. There was a lack of solid evidence indicating the presence of publishing bias.

CONCLUSIONS

The present findings indicate that using AI in EC can potentially enhance the efficiency of diagnosing colorectal abnormalities. As a valuable instrument, it can enhance prognostic outcomes in ordinary EC procedures, exhibiting superior diagnostic accuracy compared to trainee-level endoscopists and demonstrating comparability to expert endoscopists. The research is subject to certain constraints, namely a limited number of clinical investigations and variations in the methodologies used for identification. Consequently, it is imperative to conduct comprehensive and extensive research to enhance the precision of diagnostic procedures.

ENDOANGEL versus water exchange for the detection of colorectal adenomas

Background

Recently, the ENDOANGEL (EN) system, a computer-assisted detection technique, and water exchange (WE) assisted colonoscopy have both been shown to increase the colorectal adenoma detection rate (ADR).

Objectives

The aim of this study was to compare the ADR between EN- and WE-assisted colonoscopy.

Design

This was a retrospective study.

Methods

Data from patients who underwent either EN- or WE-assisted colonoscopy between October 2021 and August 2022 were analysed consecutively. The primary outcome measure was the ADR.

Results

The ADR was found to be similar between the EN and WE groups, with 80 out of 199 (40.2%) patients in the EN group compared to 78 out of 174 (44.8%) patients in the WE group [1.21; 95% confidence interval (CI), 0.80-1.83]. In the analysis using stabilized inverse probability treatment weighting after adjustment for confounding factors, both colonoscopy methods had similar performance in terms of ADR (1.41; 95% CI, 0.88-2.27).

Conclusion

EN was found to be comparable to WE in terms of ADR during colonoscopy, and both methods may be effectively used in clinical practice.

Application of artificial intelligence in endoscopic gastrointestinal tumors

With an increasing number of patients with gastrointestinal cancer, effective and accurate early diagnostic clinical tools are required provide better health care for patients with gastrointestinal cancer. Recent studies have shown that artificial intelligence (AI) plays an important role in the diagnosis and treatment of patients with gastrointestinal tumors, which not only improves the efficiency of early tumor screening, but also significantly improves the survival rate of patients after treatment. With the aid of efficient learning and judgment abilities of AI, endoscopists can improve the accuracy of diagnosis and treatment through endoscopy and avoid incorrect descriptions or judgments of gastrointestinal lesions. The present article provides an overview of the application status of various artificial intelligence in gastric and colorectal cancers in recent years, and the direction of future research and clinical practice is clarified from a clinical perspective to provide a comprehensive theoretical basis for AI as a promising diagnostic and therapeutic tool for gastrointestinal cancer.

The 2022 top 10 list of endoscopy topics in medical publishing: an annual review by the American Society for Gastrointestinal Endoscopy Editorial Board

Using a systematic literature search of original articles published during 2022 in Gastrointestinal Endoscopy and other high-impact medical and gastroenterology journals, the 10-member Editorial Board of the American Society for Gastrointestinal Endoscopy composed a list of the 10 most significant topic areas in GI endoscopy during the study year. Each Editorial Board member was directed to consider 3 criteria in generating candidate lists-significance, novelty, and global impact on clinical practice-and subject matter consensus was facilitated by the Chair through electronic voting. The 10 identified areas collectively represent advances in the following endoscopic spheres: artificial intelligence, endoscopic submucosal dissection, Barrett's esophagus, interventional EUS, endoscopic resection techniques, pancreaticobiliary endoscopy, management of acute pancreatitis, endoscopic environmental sustainability, the NordICC trial, and spiral enteroscopy. Each board member was assigned a consensus topic area around which to summarize relevant important articles, thereby generating this précis of the "top 10" endoscopic advances of 2022.

Artificial intelligence for colorectal neoplasia detection during colonoscopy: a systematic review and meta-analysis of randomized clinical trials

BACKGROUND

The use of artificial intelligence (AI) in detecting colorectal neoplasia during colonoscopy holds the potential to enhance adenoma detection rates (ADRs) and reduce adenoma miss rates (AMRs). However, varied outcomes have been observed across studies. Thus, this study aimed to evaluate the potential advantages and disadvantages of employing AI-aided systems during colonoscopy.

METHODS

Using Medical Subject Headings (MeSH) terms and keywords, a comprehensive electronic literature search was performed of the Embase, Medline, and the Cochrane Library databases from the inception of each database until October 04, 2023, in order to identify randomized controlled trials (RCTs) comparing AI-assisted with standard colonoscopy for detecting colorectal neoplasia. Primary outcomes included AMR, ADR, and adenomas detected per colonoscopy (APC). Secondary outcomes comprised the poly missed detection rate (PMR), poly detection rate (PDR), and poly detected per colonoscopy (PPC). We utilized random-effects meta-analyses with Hartung-Knapp adjustment to consolidate results. The prediction interval (PI) and I2 statistics were utilized to quantify between-study heterogeneity. Moreover, meta-regression and subgroup analyses were performed to investigate the potential sources of heterogeneity. This systematic review and meta-analysis is registered with PROSPERO (CRD42023428658).

FINDINGS

This study encompassed 33 trials involving 27,404 patients. Those undergoing AI-aided colonoscopy experienced a significant decrease in PMR (RR, 0.475; 95% CI, 0.294-0.768; I2 = 87.49%) and AMR (RR, 0.495; 95% CI, 0.390-0.627; I2 = 48.76%). Additionally, a significant increase in PDR (RR, 1.238; 95% CI, 1.158-1.323; I2 = 81.67%) and ADR (RR, 1.242; 95% CI, 1.159-1.332; I2 = 78.87%), along with a significant increase in the rates of PPC (IRR, 1.388; 95% CI, 1.270-1.517; I2 = 91.99%) and APC (IRR, 1.390; 95% CI, 1.277-1.513; I2 = 86.24%), was observed. This resulted in 0.271 more PPCs (95% CI, 0.144-0.259; I2 = 65.61%) and 0.202 more APCs (95% CI, 0.144-0.259; I2 = 68.15%).

INTERPRETATION

AI-aided colonoscopy significantly enhanced the detection of colorectal neoplasia detection, likely by reducing the miss rate. However, future studies should focus on evaluating the cost-effectiveness and long-term benefits of AI-aided colonoscopy in reducing cancer incidence.

FUNDING

This work was supported by the Heilongjiang Provincial Natural Science Foundation of China (LH2023H096), the Postdoctoral research project in Heilongjiang Province (LBH-Z22210), the National Natural Science Foundation of China's General Program (82072640) and the Outstanding Youth Project of Heilongjiang Natural Science Foundation (YQ2021H023).

CLASSICA: Validating artificial intelligence in classifying cancer in real time during surgery

AIM

Treatment pathways for significant rectal polyps differ depending on the underlying pathology, but pre-excision profiling is imperfect. It has been demonstrated that differences in fluorescence perfusion signals following injection of indocyanine green (ICG) can be analysed mathematically and, with the assistance of artificial intelligence (AI), used to classify tumours endoscopically as benign or malignant. This study aims to validate this method of characterization across multiple clinical sites regarding its generalizability, usability and accuracy while developing clinical-grade software to enable it to become a useful method.

METHODS

The CLASSICA study is a prospective, unblinded multicentre European observational study aimed to validate the use of AI analysis of ICG fluorescence for intra-operative tissue characterization. Six hundred patients undergoing transanal endoscopic evaluation of significant rectal polyps and tumours will be enrolled in at least five clinical sites across the European Union over a 4-year period. Video recordings will be analysed regarding dynamic fluorescence patterns centrally as software is developed to enable analysis with automatic classification to happen locally. AI-based classification and subsequently guided intervention will be compared with the current standard of care including biopsies, final specimen pathology and patient outcomes.

DISCUSSION

CLASSICA will validate the use of AI in the analysis of ICG fluorescence for the purposes of classifying significant rectal polyps and tumours endoscopically. Follow-on studies will compare AI-guided targeted biopsy or, indeed, AI characterization alone with traditional biopsy and AI-guided local excision versus traditional excision with regard to marginal clearance and recurrence.

Third Eye? The Assistance of Artificial Intelligence (AI) in the Endoscopy of Gastrointestinal Neoplasms

Gastrointestinal cancers are characterized by high incidence and mortality. However, there are well-established methods of screening. The endoscopy exam provides the macroscopical image and enables harvesting the tissue samples for further histopathological diagnosis. The efficiency of endoscopies relies not only on proper patient preparation, but also on the skills of the personnel conducting the exam. In recent years, a number of reports concerning the application of artificial intelligence (AI) in medicine have arisen. Numerous studies aimed to assess the utility of deep learning/ neural network systems supporting endoscopies. In this review, we summarized the most recent reports and randomized clinical trials regarding the application of AI in screening and surveillance of gastrointestinal cancers among patients suffering from esophageal, gastric, and colorectal cancer, along with the advantages, limitations, and controversies of those novel solutions.

The Role of Artificial Intelligence in Colorectal Cancer Screening: Lesion Detection and Lesion Characterization

Colorectal cancer remains a leading cause of cancer-related morbidity and mortality worldwide, despite the widespread uptake of population surveillance strategies. This is in part due to the persistent development of 'interval colorectal cancers', where patients develop colorectal cancer despite appropriate surveillance intervals, implying pre-malignant polyps were not resected at a prior colonoscopy. Multiple techniques have been developed to improve the sensitivity and accuracy of lesion detection and characterisation in an effort to improve the efficacy of colorectal cancer screening, thereby reducing the incidence of interval colorectal cancers. This article presents a comprehensive review of the transformative role of artificial intelligence (AI), which has recently emerged as one such solution for improving the quality of screening and surveillance colonoscopy. Firstly, AI-driven algorithms demonstrate remarkable potential in addressing the challenge of overlooked polyps, particularly polyp subtypes infamous for escaping human detection because of their inconspicuous appearance. Secondly, AI empowers gastroenterologists without exhaustive training in advanced mucosal imaging to characterise polyps with accuracy similar to that of expert interventionalists, reducing the dependence on pathologic evaluation and guiding appropriate resection techniques or referrals for more complex resections. AI in colonoscopy holds the potential to advance the detection and characterisation of polyps, addressing current limitations and improving patient outcomes. The integration of AI technologies into routine colonoscopy represents a promising step towards more effective colorectal cancer screening and prevention.

Endoscopist-Level and Procedure-Level Factors Associated With Increased Adenoma Detection With the Use of a Computer-Aided Detection Device

INTRODUCTION

To investigate the impact of procedure-related and endoscopist-related factors on the effectiveness of a computer-aided detection (CADe) device in adenomas per colonoscopy (APC) detection.

METHODS

The SKOUT clinical trial was conducted at 5 US sites. We present prespecified analyses of procedure-related and endoscopist-related factors, and association with APC across treatment and control cohorts.

RESULTS

There were numeric increases in APC between SKOUT vs standard colonoscopy in community-based endoscopists, withdrawal time of ≥8 minutes, for endoscopists with >20 years of experience, and endoscopists with baseline adenoma detection rate <45%.

DISCUSSION

The application of CADe devices in clinical practice should be carefully evaluated. Larger studies should explore differences in endoscopist-related factors for CADe.

Real-Time Computer-Aided Detection of Colorectal Neoplasia During Colonoscopy : A Systematic Review and Meta-analysis

BACKGROUND

Artificial intelligence computer-aided detection (CADe) of colorectal neoplasia during colonoscopy may increase adenoma detection rates (ADRs) and reduce adenoma miss rates, but it may increase overdiagnosis and overtreatment of nonneoplastic polyps.

PURPOSE

To quantify the benefits and harms of CADe in randomized trials.

DESIGN

Systematic review and meta-analysis. (PROSPERO: CRD42022293181).

DATA SOURCES

Medline, Embase, and Scopus databases through February 2023.

STUDY SELECTION

Randomized trials comparing CADe-assisted with standard colonoscopy for polyp and cancer detection.

DATA EXTRACTION

Adenoma detection rate (proportion of patients with ≥1 adenoma), number of adenomas detected per colonoscopy, advanced adenoma (≥10 mm with high-grade dysplasia and villous histology), number of serrated lesions per colonoscopy, and adenoma miss rate were extracted as benefit outcomes. Number of polypectomies for nonneoplastic lesions and withdrawal time were extracted as harm outcomes. For each outcome, studies were pooled using a random-effects model. Certainty of evidence was assessed using the GRADE (Grading of Recommendations Assessment, Development and Evaluation) framework.

DATA SYNTHESIS

Twenty-one randomized trials on 18 232 patients were included. The ADR was higher in the CADe group than in the standard colonoscopy group (44.0% vs. 35.9%; relative risk, 1.24 [95% CI, 1.16 to 1.33]; low-certainty evidence), corresponding to a 55% (risk ratio, 0.45 [CI, 0.35 to 0.58]) relative reduction in miss rate (moderate-certainty evidence). More nonneoplastic polyps were removed in the CADe than the standard group (0.52 vs. 0.34 per colonoscopy; mean difference [MD], 0.18 polypectomy [CI, 0.11 to 0.26 polypectomy]; low-certainty evidence). Mean inspection time increased only marginally with CADe (MD, 0.47 minute [CI, 0.23 to 0.72 minute]; moderate-certainty evidence).

LIMITATIONS

This review focused on surrogates of patient-important outcomes. Most patients, however, may consider cancer incidence and cancer-related mortality important outcomes. The effect of CADe on such patient-important outcomes remains unclear.

CONCLUSION

The use of CADe for polyp detection during colonoscopy results in increased detection of adenomas but not advanced adenomas and in higher rates of unnecessary removal of nonneoplastic polyps.

PRIMARY FUNDING SOURCE

European Commission Horizon 2020 Marie Skłodowska-Curie Individual Fellowship.

Comparison of the Ability of Artificial-Intelligence-Based Computer-Aided Detection (CAD) Systems and Endoscopists to Detect Colorectal Neoplastic Lesions on Endoscopy Video

Artificial-intelligence-based computer-aided diagnosis (CAD) systems have developed remarkably in recent years. These systems can help increase the adenoma detection rate (ADR), an important quality indicator in colonoscopies. While there have been many still-image-based studies on the usefulness of CAD, few have reported on its usefulness using actual clinical videos. However, no studies have compared the CAD group and control groups using the exact same case videos. This study aimed to determine whether CAD or endoscopists were superior in identifying colorectal neoplastic lesions in videos. In this study, we examined 34 lesions from 21 cases. CAD performed better than four of the six endoscopists (three experts and three beginners), including all the beginners. The time to lesion detection with beginners and experts was 2.147 ± 1.118 s and 1.394 ± 0.805 s, respectively, with significant differences between beginners and experts (p < 0.001) and between beginners and CAD (both p < 0.001). The time to lesion detection was significantly shorter for experts and CAD than for beginners. No significant difference was found between experts and CAD (p = 1.000). CAD could be useful as a diagnostic support tool for beginners to bridge the experience gap with experts.

The Quality and Utility of Artificial Intelligence in Patient Care

BACKGROUND

Artificial intelligence (AI) is increasingly being used in patient care. In the future, physicians will need to understand not only the basic functioning of AI applications, but also their quality, utility, and risks.

METHODS

This article is based on a selective review of the literature on the principles, quality, limitations, and benefits AI applications in patient care, along with examples of individual applications.

RESULTS

The number of AI applications in patient care is rising, with more than 500 approvals in the United States to date. Their quality and utility are based on a number of interdependent factors, including the real-life setting, the type and amount of data collected, the choice of variables used by the application, the algorithms used, and the goal and implementation of each application. Bias (which may be hidden) and errors can arise at all these levels. Any evaluation of the quality and utility of an AI application must, therefore, be conducted according to the scientific principles of evidence-based medicine-a requirement that is often hampered by a lack of transparency.

CONCLUSION

AI has the potential to improve patient care while meeting the challenge of dealing with an ever-increasing surfeit of information and data in medicine with limited human resources. The limitations and risks of AI applications require critical and responsible consideration. This can best be achieved through a combination of scientific.

Prospective study of computer-aided detection of colorectal adenomas in hospitalized patients

BACKGROUND

Adenoma detection with polypectomy during total colonoscopy reduces the incidence of colorectal cancer (CRC) and colorectal cancer-associated mortality. The adenoma detection rate (ADR) is an established quality indicator, which is associated with a decreased risk for interval cancer. An increase in ADR could be demonstrated for several artificially intelligent, real-time computer-aided detection (CADe) systems in selected patients. Most studies concentrated on outpatient colonoscopies. This sector often lacks funds for applying costly innovations like CADe. Hospitals are more likely to implement CADe and information about the impact of CADe in the distinct patient cohort of hospitalized patients is scarce.

METHODS

In this prospective, randomized-controlled study, we compared colonoscopies performed with or without computer-aided detection (CADe) system (GI Genius, Medtronic) performed at University Medical Center Schleswig-Holstein, Campus Luebeck. The primary endpoint was ADR.

RESULTS

Overall, 232 patients were randomized with n = 122 patients in the CADe arm and n = 110 patients in the control arm. Median age was 66 years (interquartile range 51-77). Indication for colonoscopy was most often workup for gastrointestinal symptoms (88.4%) followed by screening, post-polypectomy and post-CRC surveillance (each 3.9%). Withdrawal time was significantly prolonged (11 vs. 10 min, p = 0.039), without clinical relevance. Complication rate was not different between the arms (0.8% vs. 4.5%; p = 0.072). The ADR was significantly increased in the CADe arm compared to the control (33.6% vs. 18.1%, p = 0.008). ADR increase was particularly strong for the detection in elderly patients aged ≥50 years (OR 6.3, 95% CI 1.7 - 23.1, p = 0.006).

CONCLUSION

The use of CADe is safe and increases ADR in hospitalized patients.

Effectiveness and application of artificial intelligence for endoscopic screening of colorectal cancer: the future is now

INTRODUCTION

Artificial intelligence (AI) in gastrointestinal endoscopy includes systems designed to interpret medical images and increase sensitivity during examination. This may be a promising solution to human biases and may provide support during diagnostic endoscopy.

AREAS COVERED

This review aims to summarize and evaluate data supporting AI technologies in lower endoscopy, addressing their effectiveness, limitations, and future perspectives.

EXPERT OPINION

Computer-aided detection (CADe) systems have been studied with promising results, allowing for an increase in adenoma detection rate (ADR), adenoma per colonoscopy (APC), and a reduction in adenoma miss rate (AMR). This may lead to an increase in the sensitivity of endoscopic examinations and a reduction in the risk of interval-colorectal cancer. In addition, computer-aided characterization (CADx) has also been implemented, aiming to distinguish adenomatous and non-adenomatous lesions through real-time assessment using advanced endoscopic imaging techniques. Moreover, computer-aided quality (CADq) systems have been developed with the aim of standardizing quality measures in colonoscopy (e.g. withdrawal time and adequacy of bowel cleansing) both to improve the quality of examinations and set a reference standard for randomized controlled trials.

Recent advances in devices and technologies that might prove revolutionary for colonoscopy procedures

INTRODUCTION

Colorectal cancer (CRC) is the third most common malignancy and second leading cause of cancer-related mortality in the world. Adenoma detection rate (ADR), a quality indicator for colonoscopy, has gained prominence as it is inversely related to CRC incidence and mortality. As such, recent efforts have focused on developing novel colonoscopy devices and technologies to improve ADR.

AREAS COVERED

The main objective of this paper is to provide an overview of advancements in the fields of colonoscopy mechanical attachments, artificial intelligence-assisted colonoscopy, and colonoscopy optical enhancements with respect to ADR. We accomplished this by performing a comprehensive search of multiple electronic databases from inception to September 2023. This review is intended to be an introduction to colonoscopy devices and technologies.

EXPERT OPINION

Numerous mechanical attachments and optical enhancements have been developed that have the potential to improve ADR and AI has gone from being an inaccessible concept to a feasible means for improving ADR. While these advances are exciting and portend a change in what will be considered standard colonoscopy, they continue to require refinement. Future studies should focus on combining modalities to further improve ADR and exploring the use of these technologies in other facets of colonoscopy.

Adenoma detection rate in colonoscopy: how can it be improved?

INTRODUCTION

The introduction of widespread colonoscopy screening programs has helped in decreasing the incidence of Colorectal Cancer (CRC). However, 'back-to-back' colonoscopies revealed relevant percentage of missed adenomas. Quality indicators were created to further homogenize detection performances and decrease the incidence of post-colonoscopy CRC. Among them, the Adenoma Detection Rate (ADR), defined as the percentage obtained by dividing the number of endoscopic procedures in which at least one adenoma was resected, by the total number of procedures, was found to be inversely associated with the risks of interval colorectal cancer, advanced-stage interval cancer, and fatal interval cancer.

AREAS COVERED

In this paper, we performed a comprehensive review of the literature focusing on promising new devices and technologies, which are meant to positively affect the endoscopist performance in detecting adenomas, therefore increasing ADR.

EXPERT OPINION

Considering the current knowledge, although several devices and technologies have been proposed with this intent, the recent implementation of AI ranked over all of the other strategies and it is likely to become the new standard within few years. However, the combination of different device/technologies need to be investigated in the future aiming at even further increasing of endoscopist detection performances.

Combination of Mucosa-Exposure Device and Computer-Aided Detection for Adenoma Detection During Colonoscopy: A Randomized Trial

BACKGROUND & AIMS

Both computer-aided detection (CADe)-assisted and Endocuff-assisted colonoscopy have been found to increase adenoma detection. We investigated the performance of the combination of the 2 tools compared with CADe-assisted colonoscopy alone to detect colorectal neoplasias during colonoscopy in a multicenter randomized trial.

METHODS

Men and women undergoing colonoscopy for colorectal cancer screening, polyp surveillance, or clincial indications at 6 centers in Italy and Switzerland were enrolled. Patients were assigned (1:1) to colonoscopy with the combinations of CADe (GI-Genius; Medtronic) and a mucosal exposure device (Endocuff Vision [ECV]; Olympus) or to CADe-assisted colonoscopy alone (control group). All detected lesions were removed and sent to histopathology for diagnosis. The primary outcome was adenoma detection rate (percentage of patients with at least 1 histologically proven adenoma or carcinoma). Secondary outcomes were adenomas detected per colonoscopy, advanced adenomas and serrated lesions detection rate, the rate of unnecessary polypectomies (polyp resection without histologically proven adenomas), and withdrawal time.

RESULTS

From July 1, 2021 to May 31, 2022, there were 1316 subjects randomized and eligible for analysis; 660 to the ECV group, 656 to the control group). The adenoma detection rate was significantly higher in the ECV group (49.6%) than in the control group (44.0%) (relative risk, 1.12; 95% CI, 1.00-1.26; P = .04). Adenomas detected per colonoscopy were significantly higher in the ECV group (mean ± SD, 0.94 ± 0.54) than in the control group (0.74 ± 0.21) (incidence rate ratio, 1.26; 95% CI, 1.04-1.54; P = .02). The 2 groups did not differ in term of detection of advanced adenomas and serrated lesions. There was no significant difference between groups in mean ± SD withdrawal time (9.01 ± 2.48 seconds for the ECV group vs 8.96 ± 2.24 seconds for controls; P = .69) or proportion of subjects undergoing unnecessary polypectomies (relative risk, 0.89; 95% CI, 0.69-1.14; P = .38).

CONCLUSIONS

The combination of CADe and ECV during colonoscopy increases adenoma detection rate and adenomas detected per colonoscopy without increasing withdrawal time compared with CADe alone.

CLINICALTRIALS

gov, Number: NCT04676308.

Spatiotemporal analysis of small bowel capsule endoscopy videos for outcomes prediction in Crohn's disease

Background

Deep learning techniques can accurately detect and grade inflammatory findings on images from capsule endoscopy (CE) in Crohn's disease (CD). However, the predictive utility of deep learning of CE in CD for disease outcomes has not been examined.

Objectives

We aimed to develop a deep learning model that can predict the need for biological therapy based on complete CE videos of newly-diagnosed CD patients.

Design

This was a retrospective cohort study. The study cohort included treatment-naïve CD patients that have performed CE (SB3, Medtronic) within 6 months of diagnosis. Complete small bowel videos were extracted using the RAPID Reader software.

Methods

CE videos were scored using the Lewis score (LS). Clinical, endoscopic, and laboratory data were extracted from electronic medical records. Machine learning analysis was performed using the TimeSformer computer vision algorithm developed to capture spatiotemporal characteristics for video analysis.

Results

The patient cohort included 101 patients. The median duration of follow-up was 902 (354-1626) days. Biological therapy was initiated by 37 (36.6%) out of 101 patients. TimeSformer algorithm achieved training and testing accuracy of 82% and 81%, respectively, with an Area under the ROC Curve (AUC) of 0.86 to predict the need for biological therapy. In comparison, the AUC for LS was 0.70 and for fecal calprotectin 0.74.

Conclusion

Spatiotemporal analysis of complete CE videos of newly-diagnosed CD patients achieved accurate prediction of the need for biological therapy. The accuracy was superior to that of the human reader index or fecal calprotectin. Following future validation studies, this approach will allow for fast and accurate personalization of treatment decisions in CD.

Artificial Intelligence in Colonoscopy

PURPOSE OF REVIEW

Artificial intelligence (AI) is a rapidly growing field in gastrointestinal endoscopy, and its potential applications are virtually endless, with studies demonstrating use of AI for early gastric cancer, inflammatory bowel disease, Barrett's esophagus, capsule endoscopy, as well as other areas in gastroenterology. Much of the early studies and applications of AI in gastroenterology have revolved around colonoscopy, particularly with regards to real-time polyp detection and characterization. This review will cover much of the existing data on computer-aided detection (CADe), computer-aided diagnosis (CADx), and briefly discuss some other interesting applications of AI for colonoscopy, while also considering some of the challenges and limitations that exist around the use of AI for colonoscopy.

RECENT FINDINGS

Multiple randomized controlled trials have now been published which show a statistically significant improvement when using AI to improve adenoma detection and reduce adenoma miss rates during colonoscopy. There is also a growing pool of literature showing that AI can be helpful for characterizing/diagnosing colorectal polyps in real time. AI has also shown promise in other areas of colonoscopy, including polyp sizing and automated measurement and monitoring of quality metrics during colonoscopy. AI is a promising tool that has the ability to shape the future of gastrointestinal endoscopy, with much of the early data showing significant benefits to use of AI during colonoscopy. However, there remain several challenges that may delay or hamper the widespread use of AI in the field.

Effect of real-time computer-aided detection of colorectal adenoma in routine colonoscopy (COLO-GENIUS): a single-centre randomised controlled trial

BACKGROUND

Artificial intelligence systems have been developed to improve polyp detection. We aimed to evaluate the effect of real-time computer-aided detection (CADe) on the adenoma detection rate (ADR) in routine colonoscopy.

METHODS

This single-centre randomised controlled trial (COLO-GENIUS) was done at the Digestive Endoscopy Unit, Pôle Digestif Paris-Bercy, Clinique Paris-Bercy, Charenton-le-Pont, France. All consecutive individuals aged 18 years or older who were scheduled for a total colonoscopy and had an American Society of Anesthesiologists score of 1-3 were screened for inclusion. After the caecum was reached and the colonic preparation was appropriate, eligible participants were randomly assigned (1:1; computer-generated random numbers list) to either standard colonoscopy or CADe-assisted colonoscopy (GI Genius 2.0.2; Medtronic). Participants and cytopathologists were masked to study assignment, whereas endoscopists were not. The primary outcome was ADR, which was assessed in the modified intention-to-treat population (all randomly assigned participants except those with misplaced consent forms). Safety was analysed in all included patients. According to statistical calculations, 20 endoscopists from the Clinique Paris-Bercy had to include approximately 2100 participants with 1:1 randomisation. The trial is complete and registered with ClinicalTrials.gov, NCT04440865.

FINDINGS

Between May 1, 2021, and May 1, 2022, 2592 participants were assessed for eligibility, of whom 2039 were randomly assigned to standard colonoscopy (n=1026) or CADe-assisted colonoscopy (n=1013). 14 participants in the standard group and ten participants in the CADe group were then excluded due to misplaced consent forms, leaving 2015 participants (979 [48·6%] men and 1036 [51·4%] women) in the modified intention-to-treat analysis. ADR was 33·7% (341 of 1012 colonoscopies) in the standard group and 37·5% (376 of 1003 colonoscopies) in the CADe group (estimated mean absolute difference 4·1 percentage points [95% CI 0·0-8·1]; p=0·051). One bleeding event without deglobulisation occurred in the CADe group after a large (>2 cm) polyp resection and resolved after a haemostasis clip was placed during a second colonoscopy.

INTERPRETATION

Our findings support the benefits of CADe, even in a non-academic centre. Systematic use of CADe in routine colonoscopy should be considered.

FUNDING

None.

Post-colonoscopy cancer rate at a tertiary referral hospital in Australia: A data linkage analysis

BACKGROUND AND AIM

Colorectal cancer (CRC) diagnosed following a cancer-negative colonoscopy is termed as post-colonoscopy CRC (PCCRC). The World Endoscopy Organization has recently standardized the definition of PCCRC-3Y (CRC developing within 3 years of a cancer-negative colonoscopy). In the present study, we sought to assess PCCRC-3Y rate, perform root-cause analyses, and identify factors associated with development of PCCRC at a tertiary referral hospital in Australia.

METHODS

All patients undergoing colonoscopy from 2011 to 2018 were matched to a population-based cancer register. PCCRC-3Y rate was assessed for years 2011-2015. All PCCRC cases that developed within 6-48 months after a cancer-negative colonoscopy underwent root-cause analyses. Descriptive statistics were used to summarize data.

RESULTS

Among 17 828 patients undergoing colonoscopy, 367 CRC cases were diagnosed during the study period. This included nine PCCRC cases, which developed at a median of 14 months (range 7-34 months) after cancer-negative colonoscopy. The PCCRC-3Y rate for years 2011-2015 was 2.16% (95% CI 0.91-5.15). All nine PCCRC cases were moderately or poorly differentiated adenocarcinomas; seven of nine were early-stage CRC (stages I and II) and six of nine probably represented missed lesions at index colonoscopy despite an apparently adequate examination. History of inflammatory bowel disease (IBD) (odds ratio [OR] 21.9, 95% confidence interval [CI] 4.6-103.7, P < 0.001) and diverticulosis (OR 5.4, 95% CI 1.4-20.5, P = 0.01) were significantly associated with development of missed CRC.

CONCLUSIONS

In our tertiary referral colonoscopy cohort, PCCRC-3Y rate was 2.16% (95% CI 0.91-5.15). IBD and diverticulosis were significantly associated with risk of PCCRC. The majority of PCCRC lesions were likely missed at index colonoscopy, despite an apparently adequate examination.

Artificial Intelligence Applied to Colonoscopy: Is It Time to Take a Step Forward?

Growing evidence indicates that artificial intelligence (AI) applied to medicine is here to stay. In gastroenterology, AI computer vision applications have been stated as a research priority. The two main AI system categories are computer-aided polyp detection (CADe) and computer-assisted diagnosis (CADx). However, other fields of expansion are those related to colonoscopy quality, such as methods to objectively assess colon cleansing during the colonoscopy, as well as devices to automatically predict and improve bowel cleansing before the examination, predict deep submucosal invasion, obtain a reliable measurement of colorectal polyps and accurately locate colorectal lesions in the colon. Although growing evidence indicates that AI systems could improve some of these quality metrics, there are concerns regarding cost-effectiveness, and large and multicentric randomized studies with strong outcomes, such as post-colonoscopy colorectal cancer incidence and mortality, are lacking. The integration of all these tasks into one quality-improvement device could facilitate the incorporation of AI systems in clinical practice. In this manuscript, the current status of the role of AI in colonoscopy is reviewed, as well as its current applications, drawbacks and areas for improvement.

Impact of Artificial Intelligence on Colonoscopy Surveillance After Polyp Removal: A Pooled Analysis of Randomized Trials

BACKGROUND AND AIMS

Artificial intelligence (AI) tools aimed at improving polyp detection have been shown to increase the adenoma detection rate during colonoscopy. However, it is unknown how increased polyp detection rates by AI affect the burden of patient surveillance after polyp removal.

METHODS

We conducted a pooled analysis of 9 randomized controlled trials (5 in China, 2 in Italy, 1 in Japan, and 1 in the United States) comparing colonoscopy with or without AI detection aids. The primary outcome was the proportion of patients recommended to undergo intensive surveillance (ie, 3-year interval). We analyzed intervals for AI and non-AI colonoscopies for the U.S. and European recommendations separately. We estimated proportions by calculating relative risks using the Mantel-Haenszel method.

RESULTS

A total of 5796 patients (51% male, mean 53 years of age) were included; 2894 underwent AI-assisted colonoscopy and 2902 non-AI colonoscopy. When following U.S. guidelines, the proportion of patients recommended intensive surveillance increased from 8.4% (95% CI, 7.4%-9.5%) in the non-AI group to 11.3% (95% CI, 10.2%-12.6%) in the AI group (absolute difference, 2.9% [95% CI, 1.4%-4.4%]; risk ratio, 1.35 [95% CI, 1.16-1.57]). When following European guidelines, it increased from 6.1% (95% CI, 5.3%-7.0%) to 7.4% (95% CI, 6.5%-8.4%) (absolute difference, 1.3% [95% CI, 0.01%-2.6%]; risk ratio, 1.22 [95% CI, 1.01-1.47]).

CONCLUSIONS

The use of AI during colonoscopy increased the proportion of patients requiring intensive colonoscopy surveillance by approximately 35% in the United States and 20% in Europe (absolute increases of 2.9% and 1.3%, respectively). While this may contribute to improved cancer prevention, it significantly adds patient burden and healthcare costs.

Effectiveness of artificial intelligence-assisted colonoscopy in early diagnosis of colorectal cancer: a systematic review

INTRODUCTION

As artificial intelligence (AI)-assisted diagnosis gained immense popularity, it is imperative to consider its utility and efficiency in the early diagnosis of colorectal cancer (CRC), responsible for over 1.8 million cases and 881 000 deaths globally, as reported in 2018. Improved adenoma detection rate, as well as better characterizations of polyps, are significant advantages of AI-assisted colonoscopy (AIC). This systematic review (SR) investigates the effectiveness of AIC in the early diagnosis of CRC as compared to conventional colonoscopy.

MATERIALS AND METHODS

Electronic databases such as PubMed/Medline, SCOPUS, and Web of Science were reviewed for original studies (randomized controlled trials, observational studies), SRs, and meta-analysis between 2017 and 2022 utilizing Medical Subject Headings terminology in a broad search strategy. All searches were performed and analyzed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis methodology and were conducted from November 2022. A data extraction form based on the Cochrane Consumers and Communication Review group's extraction template for quality assessment and evidence synthesis was used for data extraction. All included studies considered for bias and ethical criteria and provided valuable evidence to answer the research question.

RESULTS

The database search identified 218 studies, including 87 from PubMed, 60 from SCOPUS, and 71 from Web of Science databases. The retrieved studies from the databases were imported to Rayyan software and a duplicate article check was performed, all duplicate articles were removed after careful evaluation of the data. The abstract and full-text screening was performed in accordance with the following eligibility criteria: Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) for observational studies; Preferred Reporting Items for Systematic Reviews and Meta-Analysis for review articles, ENTREQ for narrative studies; and modified JADAD for randomized controlled trials. This yielded 15 studies that met the requirements for this SR and were finally included in the review.

CONCLUSION

AIC is a safe, highly effective screening tool that can increase the detection rate of adenomas, and polyps resulting in an early diagnosis of CRC in adults when compared to conventional colonoscopy. The results of this SR prompt further large-scale research to investigate the effectiveness in accordance with sex, race, and socioeconomic status, as well as its influence on prognosis and survival rate.