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

Eye movement patterns associated with colorectal adenoma detection: Post hoc analysis of randomized controlled trial

Background and study aims

The adenoma detection rate is higher among endoscopists who spend more time observing screen edges during colonoscopies. Nonetheless, eye movement parameters related to lesion detection remain unknown. This study aimed to determine the specific eye movement parameters related to colorectal adenoma detection, including the gaze rate in a particular area and eye movement speed.

Patients and methods

This study was a post hoc analysis of a randomized controlled trial investigating the effect of modifying eye movements of endoscopists on colorectal adenoma detection. Gaze rate at a specific area and eye movement speed were calculated based on endoscopist gaze coordinates in each examination. Time required for observation and treatment of polyps was excluded. The lower peripheral area was defined as the bottom row when the screen was divided into 6×6 sections. These parameters were compared between patients with and without adenomas.

Results

Five physicians performed 158 colonoscopies. The adenoma detection group exhibited a lower peripheral gaze rate (13.7% vs. 9.5%, P = 0.004) and smaller average eye movement distance (29.9 pixels/30 ms vs. 33.3 pixels/30 ms, P = 0.022). Logistic regression analysis showed that a lower peripheral gaze rate > 13.0% and an average eye movement distance <30 pixels/30 ms were increased independent predictors of adenoma detection ( P = 0.024, odds ratio [OR] 2.53, 95% confidence interval [CI] 1.71-3.28; P = 0.045, OR 4.57, 95% CI 1.03-20.2), whereas age, sex, and withdrawal time were not.

Conclusions

Lower peripheral gaze rate and slow eye movement are associated with colorectal adenoma detection.

Cost-Effectiveness for Artificial Intelligence in Colonoscopy

Artificial intelligence (AI) is set to transform the field of colonoscopy through the implementation of computer-assisted detection and diagnosis. While over 20 randomized controlled trials have demonstrated the efficacy of AI in increasing adenoma detection rate, the broader implementation of these technologies faces hurdles due to economic considerations and reimbursement challenges. Cost-effectiveness analysis plays a crucial role in determining the viability of integrating AI into clinical practice, highlighting both the potential long-term benefits and the initial economic burdens. Comprehensive evaluation and large-scale studies are essential to realize AI's potential in optimizing colonoscopy procedures and reducing health care costs.

Past, Present, and Future: A History Lesson in Artificial Intelligence

Over the past 5 decades, artificial intelligence (AI) has evolved rapidly. Moving from basic models to advanced machine learning and deep learning systems, the impact of AI on various fields, including medicine, has been profound. In gastroenterology, AI-driven computer-aided detection and computer-aided diagnosis systems have revolutionized endoscopy, imaging, and pathology detection. The future promises further advancements in diagnostic precision, personalized treatment, and clinical research. However, challenges such as transparency, liability, and ethical concerns must be addressed. By fostering collaboration, robust governance and development of quality metrics, AI can be leveraged to enhance patient care and advance scientific knowledge.

Building Machine Learning Models in Gastrointestinal Endoscopy

The current landscape of machine learning models in GI endoscopy is fraught with considerable variability in methodologies and quality, posing challenges for validation and generalization. To ensure the effective integration of AI in clinical practice, it is crucial to develop and validate models rigorously across diverse and representative datasets. This involves standardizing reference standards, ensuring thorough external validation, using representative patient populations, and incorporating a range of image qualities. Addressing these methodological discrepancies will enhance the reliability and robustness of AI models, thereby facilitating their adoption and improving patient care in GI endoscopy.

Reimbursement and Regulatory Landscape for Artificial Intelligence in Medical Technology

Integration of artificial intelligence (AI) into medical devices and services promises significant improvements in the diagnosis and treatment of disease. This article reviews current payment pathways for AI medical technology and the regulatory issues affecting both technology development and practical use by physicians; it discusses the need for data privacy, security, and transparency in AI. The Food and Drug Administration's regulations aim to balance safety, efficacy, and innovation encouraging research and collaboration with stakeholders. Effective regulation and reimbursement strategies are essential for the successful adoption of AI in health care, ensuring improved patient care and trust in these technologies.

Role of Artificial Intelligence for Colon Polyp Detection and Diagnosis and Colon Cancer

The broad use of artificial intelligence (AI) and its various applications have already shown significant impact in medicine and in everyday life. In gastroenterology, the most studied AI tools at present are computer-aided detection (CADe) and computer-aided diagnosis (CADx). These tools have been mainly assessed during colonoscopy for the detection of polyps and for the prediction of their histology based on their appearance. Their use aims to improve colonoscopy quality, standardize procedures, and potentially reduce costs. Data on CADe demonstrate clear benefits that are applicable to clinical practice. While CADx shows good diagnostic performance, its additional benefits in assisting endoscopists remain unclear.

AGA Living Clinical Practice Guideline on Computer-Aided Detection-Assisted Colonoscopy

BACKGROUND & AIMS

This American Gastroenterological Association (AGA) guideline is intended to provide an overview of the evidence and support endoscopists and patients on the use of computer-aided detection (CADe) systems for the detection of colorectal polyps during colonoscopy.

METHODS

A multidisciplinary panel of content experts and guideline methodologists used the Grading of Recommendations Assessment, Development and Evaluation framework and relied on the following sources of evidence: (1) a systematic review examining the desirable and undesirable effects (ie, benefits and harms) of CADe-assisted colonoscopy, (2) a microsimulation study estimating the effects of CADe on longer-term patient-important outcomes, (3) a systematic search of evidence evaluating the values and preferences of patients undergoing colonoscopy, and (4) a systematic review of studies evaluating health care providers' trust in artificial intelligence technology in gastroenterology.

RESULTS

The panel reached the conclusion that no recommendation could be made for or against the use of CADe-assisted colonoscopy in light of very low certainty of evidence for the critical outcomes, desirable and undesirable (11 fewer colorectal cancers per 10,000 individuals and 2 fewer colorectal cancer deaths per 10,000 individuals), increased burden of more intensive surveillance colonoscopies (635 more per 10,000 individuals), and cost and resource implications. The panel acknowledged the 8% (95% CI, 6%-10%) increase in adenoma detection rate and 2% (95% CI, 0%-4%) increase in advanced adenoma and/or sessile serrated lesion detection rate.

CONCLUSIONS

This guideline highlights the close tradeoff between desirable and undesirable effects and the limitations in the current evidence to support a recommendation. The panel acknowledged the potential for CADe to continually improve as an iterative artificial intelligence application. Ongoing publications providing evidence for critical outcomes will help inform a future recommendation.

Computer aided detection and diagnosis of polyps in adult patients undergoing colonoscopy: a living clinical practice guideline

CLINICAL QUESTION

In adult patients undergoing colonoscopy for any indication (screening, surveillance, follow-up of positive faecal immunochemical testing, or gastrointestinal symptoms such as blood in the stools) what are the benefits and harms of computer-aided detection (CADe)?

CONTEXT AND CURRENT PRACTICE

Colorectal cancer (CRC), the third most common cancer and the second leading cause of cancer-related death globally, typically arises from adenomatous polyps. Detection and removal of polyps during colonoscopy can reduce the risk of cancer. CADe systems use artificial intelligence (AI) to assist endoscopists by analysing real-time colonoscopy images to detect potential polyps. Despite their increasing use in clinical practice, guideline recommendations that carefully balance all patient-important outcomes remain unavailable. In this first iteration of a living guideline, we address the use of CADe at the level of an individual patient.

EVIDENCE

Evidence for this recommendation is drawn from a living systematic review of 44 randomised controlled trials (RCTs) involving more than 30 000 participants and a companion microsimulation study simulating 10 year follow-up for 100 000 individuals aged 60-69 years to assess the impact of CADe on patient-important outcomes. While no direct evidence was found for critical outcomes of colorectal cancer incidence and post-colonoscopy cancer incidence, low certainty data from the trials indicate that CADe may increase positive endoscopy findings. The microsimulation modelling, however, suggests little to no effect on CRC incidence, CRC-related mortality, or colonoscopy-related complications (perforation and bleeding) over the 10 year follow-up period, although low certainty evidence indicates CADe may increase the number of colonoscopies performed per patient. A review of values and preferences identified that patients value mortality reduction and quality of care but worry about increased anxiety, overdiagnosis, and more frequent surveillance.

RECOMMENDATION

For adults who have agreed to undergo colonoscopy, we suggest against the routine use of CADe (weak recommendation).

HOW THIS GUIDELINE WAS CREATED

An international panel, including three patient partners, 11 healthcare providers, and seven methodologists, deemed by MAGIC and The BMJ to have no relevant competing interests, developed this recommendation. For this guideline the panel took an individual patient approach. The panel started by defining the clinical question in PICO format, and prioritised outcomes including CRC incidence and mortality. Based on the linked systematic review and microsimulation study, the panel sought to balance the benefits, harms, and burdens of CADe and assumed patient preferences when making this recommendation UNDERSTANDING THE RECOMMENDATION: The guideline panel found the benefits of CADe on critical outcomes, such as CRC incidence and post-colonoscopy cancer incidence, over a 10 year follow up period to be highly uncertain. Low certainty evidence suggests little to no impact on CRC-related mortality, while the potential burdens-including more frequent surveillance colonoscopies-are likely to affect many patients. Given the small and uncertain benefits and the likelihood of burdens, the panel issued a weak recommendation against routine CADe use.The panel acknowledges the anticipated variability in values and preferences among patients and clinicians when considering these uncertain benefits and potential burdens. In healthcare settings where CADe is available, individual decision making may be appropriate.

UPDATES

This is the first iteration of a living practice guideline. The panel will update this living guideline if ongoing evidence surveillance identifies new CADe trial data that substantially alters our conclusions about CRC incidence, mortality, or burdens, or studies that increase our certainty in values and preferences of individual patients. Updates will provide recommendations on the use of CADe from a healthcare systems perspective (including resource use, acceptability, feasibility, and equity), as well as the combined use of CADe and computer aided diagnosis (CADx). Users can access the latest guideline version and supporting evidence on MAGICapp, with updates periodically published in The BMJ.

Clinical Implementation of Artificial Intelligence in Gastroenterology: Current Landscape, Regulatory Challenges, and Ethical Issues

Artificial intelligence (AI) is set to rapidly transform gastroenterology, particularly in the field of endoscopy, where algorithms have demonstrated efficacy in addressing human operator variability. However, implementing AI in clinical practice presents significant challenges. The regulatory landscape for AI as a medical device continues to evolve with areas of uncertainty. More robust studies generating real-world evidence are required to ultimately demonstrate impacts on patient outcomes. Cost-effectiveness data and reimbursement models will be pivotal for widespread adoption. Novel challenges are posed by emerging technologies, such as generative AI. Ethical and medicolegal concerns exist relating to data governance, patient harm, liability, and bias. This review provides an overview for clinical implementation of AI in gastroenterology and offers potential solutions to current barriers.

Artificial Intelligence-Driven Personalized Medicine: Transforming Clinical Practice in Inflammatory Bowel Disease

Inflammatory bowel disease is marked by significant clinical heterogeneity, posing challenges for accurate diagnosis and personalized treatment strategies. Conventional approaches, such as endoscopy and histology, often fail to adequately and accurately predict medium- and long-term outcomes, leading to suboptimal patient management. Artificial intelligence is emerging as a transformative force enabling standardized, accurate, and timely disease assessment and outcome prediction, including therapeutic response. Artificial intelligence-driven intestinal barrier healing assessment provides novel insights into deep healing, facilitating the discovery of novel therapeutic targets. In addition, the automated integration of multi-omics data can enhance patient profiling and personalized management strategies. The future of inflammatory bowel disease care lies in the artificial intelligence-enabled "endo-histo-omics" integrative real-time approach, harmoniously fusing endoscopic, histologic, and molecular data. Despite challenges in its adoption, this paradigm shift has the potential to refine risk stratification, improve therapeutic precision, and enable personalized interventions, ultimately advancing the implementation of precision medicine in routine clinical practice.

Colon Cancer Screening, Surveillance, and Treatment: Novel Artificial Intelligence Driving Strategies in the Management of Colon Lesions

Colonoscopy, a crucial procedure for detecting and removing colorectal polyps, has seen transformative advancements through the integration of artificial intelligence, specifically in computer-aided detection (CADe) and diagnosis (CADx). These tools enhance real-time detection and characterization of lesions, potentially reducing human error, and standardizing the quality of colonoscopy across endoscopists. CADe has proven effective in increasing adenoma detection rate, potentially reducing long-term colorectal cancer incidence. However, CADe's benefits are accompanied by challenges, such as potentially longer procedure times, increased non-neoplastic polyp resections, and a higher surveillance burden. CADx, although promising in differentiating neoplastic and non-neoplastic diminutive polyps, encounters limitations in accuracy, particularly in the proximal colon. Real-world data also revealed gaps between trial efficacy and practical outcomes, emphasizing the need for further research in uncontrolled settings. Moreover, CADx limited specificity and binary output underscore the necessity for explainable artificial intelligence to gain endoscopists' trust. This review aimed to explore the benefits, harms, and limitations of artificial intelligence for colon cancer screening, surveillance, and treatment focusing on CADe and CADx systems for lesion detection and characterization, respectively, while addressing challenges in integrating these technologies into clinical practice.

"Artificial histology" in colonic Neoplasia: A critical approach

BACKGROUND

The histological assessment of colorectal precancer and cancer lesions is challenging and primarily impacts the clinical strategies of secondary colon cancer prevention. Artificial intelligence (AI) models may potentially assist in the histological diagnosis of this spectrum of phenotypical changes.

OBJECTIVES

To provide a current overview of the evidence on AI-based methods for histologically assessing colonic precancer and cancer lesions.

METHODS

Based on the available studies, this review focuses on the reliability of AI-driven models in ranking the histological phenotypes included in colonic oncogenesis.

RESULTS

This review acknowledges the efforts to shift from subjective pathologists-based to more objective AI-based histological phenotyping. However, it also points out significant limitations and areas that require improvement.

CONCLUSIONS

Current AI-driven methods have not yet achieved the expected level of clinical effectiveness, and there are still significant ethical concerns that need careful consideration. The integration of "artificial histology" into diagnostic practice requires further efforts to combine advancements in engineering techniques with the expertise of pathologists.

Optimal visual gaze pattern of endoscopists for improving adenoma detection during colonoscopy (with video)

BACKGROUND AND AIMS

Visual gaze pattern (VGP) analysis quantifies endoscopists' specific eye movements. VGP during colonoscopy may be associated with polyp detection. However, the optimal VGP to maximize detection performance remains unclear. This study evaluated the optimal endoscopic VGP that enabled the highest colorectal adenoma detection rate.

METHODS

This randomized controlled trial was conducted between July and December 2023. We developed an eye-tracking and feedback (ETF) system that instructed endoscopists to correct their gaze toward the periphery of an endoscope screen with an audible alert. Patients who underwent colonoscopy were randomly assigned to 4 groups: 3 intervention groups, in which the endoscopist's gaze was instructed to a different level of the peripheral screen area using the ETF system (the periphery of 4 × 4, 5 × 5, and 6 × 6 divisions of the screen), and a control group in which the endoscopist did not receive instructions. The primary outcome was the number of adenomas detected per colonoscopy (APC).

RESULTS

In total, 189 patients were enrolled. The APC and adenoma detection rate were significantly higher in the 6 × 6 group than in the control group (1.82 ± 2.41 vs 0.59 ± 1.17, P = .002; 68.9% vs 30.8%, P = .002). The APC and the number of screen divisions were positively correlated (R = 0.985, P = .0152). The rate at which the endoscopist gazed at the periphery of the screen was positively correlated with the number of divisions (R = 0.964, P = .0363).

CONCLUSIONS

Colorectal adenoma detection was improved by correcting the endoscopist's gaze to the periphery of the screen, especially by dividing the screen into 6 × 6 segments.

AI and Polyp Detection During Colonoscopy

Colorectal cancer (CRC) prevention depends on effective colonoscopy; yet variability in adenoma detection rates (ADRs) and missed lesions remain significant hurdles. Artificial intelligence-powered computer-aided detection (CADe) systems offer promising advancements in enhancing polyp detection. This review examines the role of CADe in improving ADR and reducing adenoma miss rates (AMRs) while addressing its broader clinical implications. CADe has demonstrated consistent improvements in ADRs and AMRs; largely by detecting diminutive polyps, but shows limited efficacy in identifying advanced adenomas or sessile serrated lesions. Challenges such as operator deskilling and the need for enhanced algorithms persist. Combining CADe with adjunctive techniques has shown potential for further optimizing performance. While CADe has standardized detection quality; its long-term impact on CRC incidence and mortality remains inconclusive. Future research should focus on refining CADe technology and assessing its effectiveness in reducing the global burden of CRC.

Variability in computer-aided detection effect on adenoma detection rate in randomized controlled trials: A meta-regression analysis

BACKGROUND

Computer-aided detection (CADe) systems may increase adenoma detection rate (ADR) during colonoscopy. However, the variable results of CADe effects in different RCTs warrant investigation into factors influencing these results.

AIMS

Investigate the different variables possibly affecting the impact of CADe-assisted colonoscopy and its effect on ADR.

METHODS

We searched MEDLINE, EMBASE, and Scopus databases until July 2023 for RCTs reporting performance of CADe systems in the detection of colorectal neoplasia. The main outcome was pooled ADR. A random-effects meta-analysis was performed to obtain the pooled risk ratios (RR) with 95 % confidence intervals (CI)). To explore sources of heterogeneity, we conducted a meta-regression analysis using both univariable and multivariable mixed-effects models. Potential explanatory variables included factors influencing adenoma prevalence, such as patient gender, age, and colonoscopy indication. We also included both key (ADR), and minor (Withdrawal time) performance measures considered as quality indicators for colonoscopy.

RESULTS

Twenty-three randomized controlled trials (RCTs) on 19,077 patients were include. ADR was higher in the CADe group (46 % [95 % CI 39-52]) than in the standard colonoscopy group (38 % [95 % CI 31-46]) with a risk ratio of 1.22 [95 % CI 1.14-1.29]); and a substantial level of heterogeneity (I2 = 67.69 %). In the univariable meta-regression analysis, patient age, ADR in control arms, and withdrawal time were the strongest predictors of CADe effect on ADR (P < .001). In multivariable meta-regression, ADR in control arms, and withdrawal time were simultaneous significant predictors of the proportion of the CADe effect on ADR.

CONCLUSION

The substantial level of heterogeneity found appeared to be associated with variability in colonoscopy quality performances across the studies, namely ADR in control arm, and withdrawal time.

Practical utility of linked color imaging in colonoscopy: Updated literature review

The remarkable recent developments in image-enhanced endoscopy (IEE) have significantly contributed to the advancement of diagnostic techniques. Linked color imaging (LCI) is an IEE technique in which color differences are expanded by processing image data to enhance short-wavelength narrow-band light. This feature of LCI causes reddish areas to appear redder and whitish areas to appear whiter. Because most colorectal lesions, such as neoplastic and inflammatory lesions, have a reddish tone, LCI is an effective tool for identifying colorectal lesions by clarifying the redder areas and distinguishing them from the surrounding normal mucosa. To date, eight randomized controlled trials have been conducted to evaluate the effectiveness of LCI in identifying colorectal adenomatous lesions. The results of a meta-analysis integrating these studies demonstrated that LCI was superior to white-light endoscopy for detecting colorectal adenomatous lesions. LCI also improves the detection of serrated lesions by enhancing their whiteness. Furthermore, accumulating evidence suggests that LCI is superior to white-light endoscopy for the diagnosis of the colonic mucosa in patients with ulcerative colitis. In this review, based on a comprehensive search of the current literature since the implementation of LCI, the utility of LCI in the detection and diagnosis of colorectal lesions is discussed. Additionally, the latest data, including attempts to combine artificial intelligence and LCI, are presented.

Artificial intelligence and early cancer of the digestive tract: New challenges and new futures

Early gastrointestinal tumors have a good prognosis, but they have insidious onset and no specific manifestations, making their diagnosis difficult. With the rapid development of artificial intelligence technology in the medical field, it has shown great potential in clinical work such as diagnosis and prognosis prediction of early gastrointestinal cancer. In this paper, we systematically review the relevant studies on AI in early esophageal cancer, early gastric cancer, early colon cancer, and hepatobiliary pancreatic cancer, and discuss the challenges and futures of AI application in early gastrointestinal cancer.

Impact of Artificial Intelligence on Pancreaticobiliary Endoscopy

Pancreaticobiliary diseases can lead to significant morbidity and their diagnoses rely on imaging and endoscopy which are dependent on operator expertise. Artificial intelligence (AI) has seen a rapid uptake in the field of luminal endoscopy, such as polyp detection during colonoscopy. However, its use for pancreaticobiliary endoscopic modalities such as endoscopic ultrasound (EUS) and cholangioscopy remains scarce, with only few studies available. In this review, we delve into the current evidence, benefits, limitations, and future scope of AI technologies in pancreaticobiliary endoscopy.

Artificial Intelligence for Adenoma and Polyp Detection During Screening and Surveillance Colonoscopy: A Randomized-Controlled Trial

Background: Colorectal cancer (CRC) is the second leading cause of cancer death in Kuwait. The effectiveness of colonoscopy in preventing CRC is dependent on a high adenoma detection rate (ADR). Computer-aided detection can identify (CADe) and characterize polyps in real time and differentiate benign from neoplastic polyps, but its role remains unclear in screening colonoscopy. Methods: This was a randomized-controlled trial (RCT) enrolling patients 45 years of age or older presenting for outpatient screening or surveillance colonoscopy (Kuwait clinical trial registration number 2047/2022). Patients with a history of inflammatory bowel disease, alarm symptoms, familial polyposis syndrome, colon resection, or poor bowel preparation were excluded. Patients were randomly assigned to either high-definition white-light (HD-WL) colonoscopy (standard of care) or HD-WL colonoscopy with the CADe system. The primary outcome was ADR. The secondary outcomes included polyp detection rate (PDR), adenoma per colonoscopy (APC), polyp per colonoscopy (PPC), and accuracy of polyp characterization. Results: From 1 September 2022 to 1 March 2023, 102 patients were included and allocated to either the HD-WL colonoscopy group (n = 51) or CADe group (n = 51). The mean age was 52.8 years (SD 8.2), and males represented 50% of the cohort. Screening for CRC accounted for 94.1% of all examinations, while the remaining patients underwent surveillance colonoscopy. A total of 121 polyps were detected with an average size of 4.18 mm (SD 5.1), the majority being tubular adenomas with low-grade dysplasia (47.1%) and hyperplastic polyps (46.3%). There was no difference in the overall bowel preparation, insertion and withdrawal times, and adverse events between the two arms. ADR (primary outcome) was non-significantly higher in the CADe group compared to the HD colonoscopy group (47.1% vs. 37.3%, p = 0.3). Among the secondary outcomes, PDR (78.4% vs. 56.8%, p = 0.02) and PPC (1.35 vs. 0.96, p = 0.04) were significantly higher in the CADe group, but APC was not (0.75 vs. 0.51, p = 0.09). Accuracy in characterizing polyp histology was similar in both groups. Conclusions: In this RCT, the artificial intelligence system showed a non-significant trend towards improving ADR among Kuwaiti patients undergoing screening or surveillance colonoscopy compared to HD-WL colonoscopy alone, while it significantly improved the detection of diminutive polyps. A larger multicenter study is required to detect the true effect of CADe on the detection of adenomas.

Benefits, burden, and harms of computer aided polyp detection with artificial intelligence in colorectal cancer screening: microsimulation modelling study

ABSTRACT

Objective

To estimate the benefits, burden, and harms of implementing computer aided detection (CADe) of polyps in colonoscopy of population based screening programmes for colorectal cancer.

Design

Microsimulation modelling study.

Setting

Cost effectiveness working package in the OperA (optimising colorectal cancer prevention through personalised treatment with artificial intelligence) project. A parallel guideline committee panel (BMJ Rapid recommendation) was consulted in defining the screening interventions and selection of outcome measures.

Population

Four cohorts of 100 000 European individuals aged 60-69 years.

Intervention

The intervention was one screening of colonoscopy and a screening of colonoscopy after faecal immunochemical test every other year with CADe. The comparison group had the same screening every other year without CADe.

Main outcome measures

Benefits (colorectal cancer incidence and death), burden (surveillance colonoscopies), and harms (colonoscopy related adverse events) over 10 years were measured. The certainty in each outcome was assessed by use of the GRADE (Grading of Recommendations Assessment, Development, and Evaluation) approach.

Results

For 100 000 individuals participating in colonoscopy screening, 824 (0.82%) were diagnosed with colorectal cancer within 10 years without CADe versus 713 (0.71%) with CADe (risk difference -0.11% (95% CI -0.43% to 0.21%)). For faecal immunochemical test screening colonoscopy, the risk was 5.82% (n=5820) without CADe versus 5.77% (n=5770) with CADe (difference -0.05% (-0.33% to 0.15%)). The risk of surveillance colonoscopy increased from 26.45% (n=26 453) to 32.82% (n=32 819) (difference 6.37% (5.8% to 6.9%)) for colonoscopy screening and from 52.26% (n=52 263) to 53.08% (n=53 082) (difference 0.82% (0.38% to 1.26%)) for faecal immunochemical test screening colonoscopy. No significant differences were noted in adverse events related to the colonoscopy between CADe and no CADe. The model estimates were sensitive to the assumed effects of screening on colorectal cancer risk and of CADe on adenoma detection rates. All outcomes were graded as low certainty.

Conclusion

With low certainty of evidence, adoption of CADe in population based screening provides small and uncertain clinical meaningful benefit, no incremental harms, and increased surveillance burden after screening.

Effectiveness of artificial intelligence assisted colonoscopy on adenoma and polyp miss rate: A meta-analysis of tandem RCTs

BACKGROUND AND AIMS

One-fourth of colorectal neoplasia is missed at screening colonoscopy, representing the leading cause of interval colorectal cancer (I-CRC). This systematic review and meta-analysis summarizes the efficacy of computer-aided colonoscopy (CAC) compared to white-light colonoscopy (WLC) in reducing lesion miss rates.

METHODS

Major databases were systematically searched through May 2024 for tandem-design RCTs comparing lesion miss rates in CAC-first followed by WLC vs WLC-first followed by CAC. The primary outcomes were adenoma miss rate (AMR) and polyp miss rate (PMR). The secondary outcomes were advanced AMR (aAMR) and sessile serrated lesion miss rate (SMR).

RESULTS

Six RCTs (1718 patients) were included. AMR was significantly lower for CAC compared to WLC (RR = 0.46; 95 %CI [0.38-0.55]; P < 0.001). PMR was also lower for CAC compared to WLC (RR = 0.44; 95 %CI [0.33-0.60]; P < 0.001). No significant difference in aAMR (RR = 1.28; 95 %CI [0.34-4.83]; P = 0.71) and SMR (RR = 0.44; 95 %CI [0.15-1.28]; P = 0.13) were observed. Sensitivity analysis including only RCTs performed in CRC screening and surveillance setting confirmed lower AMR (RR = 0.48; 95 %CI [0.39-0.58]; P < 0.001) and PMR (RR = 0.50; 95 %CI [0.37-0.66]; P < 0.001), also showing significantly lower SMR (RR = 0.28; 95 %CI [0.11-0.70]; P = 0.007) for CAC compared to WLC.

CONCLUSIONS

CAC results in significantly lower AMR and PMR compared to WLC overall, and significantly lower AMR, PMR and SMR in the screening/surveillance setting, potentially reducing the incidence of I-CRC.

Effectiveness of a novel artificial intelligence-assisted colonoscopy system for adenoma detection: a prospective, propensity score-matched, non-randomized controlled study in Korea

BACKGROUND/AIMS

The real-world effectiveness of computer-aided detection (CADe) systems during colonoscopies remains uncertain. We assessed the effectiveness of the novel CADe system, ENdoscopy as AI-powered Device (ENAD), in enhancing the adenoma detection rate (ADR) and other quality indicators in real-world clinical practice.

METHODS

We enrolled patients who underwent elective colonoscopies between May 2022 and October 2022 at a tertiary healthcare center. Standard colonoscopy (SC) was compared to ENAD-assisted colonoscopy. Eight experienced endoscopists performed the procedures in randomly assigned CADe- and non-CADe-assisted rooms. The primary outcome was a comparison of ADR between the ENAD and SC groups.

RESULTS

A total of 1,758 sex- and age-matched patients were included and evenly distributed into two groups. The ENAD group had a significantly higher ADR (45.1% vs. 38.8%, p=0.010), higher sessile serrated lesion detection rate (SSLDR) (5.7% vs. 2.5%, p=0.001), higher mean number of adenomas per colonoscopy (APC) (0.78±1.17 vs. 0.61±0.99; incidence risk ratio, 1.27; 95% confidence interval, 1.13-1.42), and longer withdrawal time (9.0±3.4 vs. 8.3±3.1, p<0.001) than the SC group. However, the mean withdrawal times were not significantly different between the two groups in cases where no polyps were detected (6.9±1.7 vs. 6.7±1.7, p=0.058).

CONCLUSIONS

ENAD-assisted colonoscopy significantly improved the ADR, APC, and SSLDR in real-world clinical practice, particularly for smaller and nonpolypoid adenomas.

The use of artificial intelligence in colonoscopic evaluations

PURPOSE OF REVIEW

This review aims to highlight the transformative impact of artificial intelligence in the field of gastrointestinal endoscopy, particularly in the detection and characterization of colorectal polyps.

RECENT FINDINGS

Over the past decade, artificial intelligence has significantly advanced the medical industry, including gastrointestinal endoscopy. Computer aided diagnosis - detection (CADe) systems have shown notable success in increasing ADR. Recent meta-analyses of RCTs have demonstrated that patients undergoing colonoscopy with CADe assistance had a higher ADR compared with conventional methods. Similarly, computer aided diagnosis - characterization (CADx) systems have proven effective in distinguishing between adenomatous and nonadenomatous polyps, enhancing diagnostic confidence and supporting cost-saving measures like the resect-and-discard strategy. Despite the high performance of these systems, the variability in real-world adoption highlights the importance of integrating artificial intelligence as an assistive tool rather than a replacement for human expertise.

SUMMARY

Artificial intelligence integration in colonoscopy, through CADe and CADx systems, marks a significant advancement in gastroenterology. These systems enhance lesion detection and characterization, leading to improved diagnostic accuracy, training outcomes, and clinical workflow efficiency. While artificial intelligence offers substantial benefits, the optimal approach involves using artificial intelligence to augment the expertise of endoscopists, ensuring that clinical decisions remain under human oversight.

Current Status of Artificial Intelligence Use in Colonoscopy

BACKGROUND

Artificial intelligence (AI) has significantly impacted medical imaging, particularly in gastrointestinal endoscopy. Computer-aided detection and diagnosis systems (CADe and CADx) are thought to enhance the quality of colonoscopy procedures.

SUMMARY

Colonoscopy is essential for colorectal cancer screening but often misses a significant percentage of adenomas. AI-assisted systems employing deep learning offer improved detection and differentiation of colorectal polyps, potentially increasing adenoma detection rates by 8%-10%. The main benefit of CADe is in detecting small adenomas, whereas it has a limited impact on advanced neoplasm detection. Recent advancements include real-time CADe systems and CADx for histopathological predictions, aiding in the differentiation of neoplastic and nonneoplastic lesions. Biases such as the Hawthorne effect and potential overdiagnosis necessitate large-scale clinical trials to validate the long-term benefits of AI. Additionally, novel concepts such as computer-aided quality improvement systems are emerging to address limitations facing current CADe systems.

KEY MESSAGES

Despite the potential of AI for enhancing colonoscopy outcomes, its effectiveness in reducing colorectal cancer incidence and mortality remains unproven. Further prospective studies are essential to establish the overall utility and clinical benefits of AI in colonoscopy.

QUAIDE - Quality assessment of AI preclinical studies in diagnostic endoscopy

Artificial intelligence (AI) holds significant potential for enhancing quality of gastrointestinal (GI) endoscopy, but the adoption of AI in clinical practice is hampered by the lack of rigorous standardisation and development methodology ensuring generalisability. The aim of the Quality Assessment of pre-clinical AI studies in Diagnostic Endoscopy (QUAIDE) Explanation and Checklist was to develop recommendations for standardised design and reporting of preclinical AI studies in GI endoscopy.The recommendations were developed based on a formal consensus approach with an international multidisciplinary panel of 32 experts among endoscopists and computer scientists. The Delphi methodology was employed to achieve consensus on statements, with a predetermined threshold of 80% agreement. A maximum three rounds of voting were permitted.Consensus was reached on 18 key recommendations, covering 6 key domains: data acquisition and annotation (6 statements), outcome reporting (3 statements), experimental setup and algorithm architecture (4 statements) and result presentation and interpretation (5 statements). QUAIDE provides recommendations on how to properly design (1. Methods, statements 1-14), present results (2. Results, statements 15-16) and integrate and interpret the obtained results (3. Discussion, statements 17-18).The QUAIDE framework offers practical guidance for authors, readers, editors and reviewers involved in AI preclinical studies in GI endoscopy, aiming at improving design and reporting, thereby promoting research standardisation and accelerating the translation of AI innovations into clinical practice.

Development of an oral cancer detection system through deep learning

OBJECTIVE

We aimed to develop an AI-based model that uses a portable electronic oral endoscope to capture intraoral images of patients for the detection of oral cancer.

SUBJECTS AND METHODS

From September 2019 to October 2023, 205 high-quality annotated images of oral cancer were collected using a portable oral electronic endoscope at the Chinese PLA General Hospital for this study. The U-Net and ResNet-34 deep learning models were employed for oral cancer detection. The performance of these models was evaluated using several metrics: Dice coefficient, Intersection over Union (IoU), Loss, Precision, Recall, and F1 Score.

RESULTS

During the algorithm model training phase, the Dice values were approximately 0.8, the Loss values were close to 0, and the IoU values were around 0.7. In the validation phase, the highest Dice values ranged between 0.4 and 0.5, while the Loss values increased, and the training loss began to decrease gradually. In the test phase, the model achieved a maximum Precision of 0.96 with a confidence threshold of 0.990. Additionally, with a confidence threshold of 0.010, the highest F1 score reached was 0.58.

CONCLUSION

This study provides an initial demonstration of the potential of deep learning models in identifying oral cancer.

The application of the combination between artificial intelligence and endoscopy in gastrointestinal tumors

Gastrointestinal (GI) tumors have always been a major type of malignant tumor and a leading cause of tumor‐related deaths worldwide. The main principles of modern medicine for GI tumors are early prevention, early diagnosis, and early treatment, with early diagnosis being the most effective measure. Endoscopy, due to its ability to visualize lesions, has been one of the primary modalities for screening, diagnosing, and treating GI tumors. However, a qualified endoscopist often requires long training and extensive experience, which to some extent limits the wider use of endoscopy. With advances in data science, artificial intelligence (AI) has brought a new development direction for the endoscopy of GI tumors. AI can quickly process large quantities of data and images and improve diagnostic accuracy with some training, greatly reducing the workload of endoscopists and assisting them in early diagnosis. Therefore, this review focuses on the combined application of endoscopy and AI in GI tumors in recent years, describing the latest research progress on the main types of tumors and their performance in clinical trials, the application of multimodal AI in endoscopy, the development of endoscopy, and the potential applications of AI within it, with the aim of providing a reference for subsequent research.

Artificial Intelligence in Gastrointestinal Endoscopy

Recent advancements in artificial intelligence (AI) have significantly impacted the field of gastrointestinal (GI) endoscopy, with applications spanning a wide range of clinical indications. The central goals for AI in GI endoscopy are to improve endoscopic procedural performance and quality assessment, optimize patient outcomes, and reduce administrative burden. Despite early progress, such as Food and Drug Administration approval of the first computer-aided polyp detection system in 2021, there are numerous important challenges to be faced on the path toward broader adoption of AI algorithms in clinical endoscopic practice.

Artificial Intelligence-Assisted Colonoscopy for Polyp Detection : A Systematic Review and Meta-analysis

BACKGROUND

Randomized clinical trials (RCTs) of computer-aided detection (CADe) system-enhanced colonoscopy compared with conventional colonoscopy suggest increased adenoma detection rate (ADR) and decreased adenoma miss rate (AMR), but the effect on detection of advanced colorectal neoplasia (ACN) is unclear.

PURPOSE

To conduct a systematic review to compare performance of CADe-enhanced and conventional colonoscopy.

DATA SOURCES

Cochrane Library, Google Scholar, Ovid EMBASE, Ovid MEDLINE, PubMed, Scopus, and Web of Science Core Collection databases were searched through February 2024.

STUDY SELECTION

Published RCTs comparing CADe-enhanced and conventional colonoscopy.

DATA EXTRACTION

Average adenoma per colonoscopy (APC) and ACN per colonoscopy were primary outcomes. Adenoma detection rate, AMR, and ACN detection rate (ACN DR) were secondary outcomes. Balancing outcomes included withdrawal time and resection of nonneoplastic polyps (NNPs). Subgroup analyses were done by neural network architecture.

DATA SYNTHESIS

Forty-four RCTs with 36 201 cases were included. Computer-aided detection-enhanced colonoscopies have higher average APC (12 090 of 12 279 [0.98] vs. 9690 of 12 292 [0.78], incidence rate difference [IRD] = 0.22 [95% CI, 0.16 to 0.28]) and higher ADR (7098 of 16 253 [44.7%] vs. 5825 of 15 855 [36.7%], rate ratio [RR] = 1.21 [CI, 1.15 to 1.28]). Average ACN per colonoscopy was similar (1512 of 9296 [0.16] vs. 1392 of 9121 [0.15], IRD = 0.01 [CI, -0.01 to 0.02]), but ACN DR was higher with CADe system use (1260 of 9899 [12.7%] vs. 1119 of 9746 [11.5%], RR = 1.16 [CI, 1.02 to 1.32]). Using CADe systems resulted in resection of almost 2 extra NNPs per 10 colonoscopies and longer total withdrawal time (0.53 minutes [CI, 0.30 to 0.77]).

LIMITATION

Statistically significant heterogeneity in quality and sample size and inability to blind endoscopists to the intervention in included studies may affect the performance estimates.

CONCLUSION

Computer-aided detection-enhanced colonoscopies have increased APC and detection rate but no difference in ACN per colonoscopy and a small increase in ACN DR. There is minimal increase in procedure time and no difference in performance across neural network architectures.

PRIMARY FUNDING SOURCE

None. (PROSPERO: CRD42023422835).

Creating a standardized tool for the evaluation and comparison of artificial intelligence-based computer-aided detection programs in colonoscopy: a modified Delphi approach

BACKGROUND AND AIMS

Multiple computer-aided detection (CADe) software programs have now achieved regulatory approval in the United States, Europe, and Asia and are being used in routine clinical practice to support colorectal cancer screening. There is uncertainty regarding how different CADe algorithms may perform. No objective methodology exists for comparing different algorithms. We aimed to identify priority scoring metrics for CADe evaluation and comparison.

METHODS

A modified Delphi approach was used. Twenty-five global leaders in CADe in colonoscopy, including endoscopists, researchers, and industry representatives, participated in an online survey over the course of 8 months. Participants generated 121 scoring criteria, 54 of which were deemed within the study scope and distributed for review and asynchronous e-mail-based open comment. Participants then scored criteria in order of priority on a 5-point Likert scale during ranking round 1. The top 11 highest priority criteria were re-distributed, with another opportunity for open comment, followed by a final round of priority scoring to identify the final 6 criteria.

RESULTS

Mean priority scores for the 54 criteria ranged from 2.25 to 4.38 after the first ranking round. The top 11 criteria after round 1 of ranking yielded mean priority scores ranging from 3.04 to 4.16. The final 6 highest priority criteria, including a tie for first-place ranking, were (1, tied) sensitivity (average, 4.16) and (1, tied) separate and independent validation of the CADe algorithm (average, 4.16); (3) adenoma detection rate (average, 4.08); (4) false-positive rate (average, 4.00); (5) latency (average, 3.84); and (6) adenoma miss rate (average, 3.68).

CONCLUSIONS

This is the first reported international consensus statement of priority scoring metrics for CADe in colonoscopy. These scoring criteria should inform CADe software development and refinement. Future research should validate these metrics on a benchmark video data set to develop a validated scoring instrument.

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.

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.

Assessing the potential of artificial intelligence to enhance colonoscopy adenoma detection in clinical practice: a prospective observational trial

BACKGROUND/AIMS

This study aimed to evaluate the effectiveness of the GI Genius (Medtronic) module in clinical practice, focusing on the adenoma detection rate (ADR) during colonoscopy. Computer-aided polyp detection (CADe) systems using artificial intelligence have been shown to improve adenoma detection in controlled trials. However, the effectiveness of these systems in clinical practice has recently been questioned.

METHODS

This single-center prospective observational study was conducted at the University Hospital of Southern Denmark and included all individuals referred for colonoscopy between November 2020 and January 2021. The primary outcome was ADR, comparing patients examined with CADe to those examined without it. The selection of patients to be examined with the CADe module was completely random.

RESULTS

A total of 502 patients were analyzed (318 in the control group and 184 in the CADe group). The overall ADR was 32.1% with a slight increase in the CADe group (34.7% vs. 30.5%). Multivariable analysis showed a very modest and statistically insignificant increase in ADR (risk ratio, 1.12; 95% confidence interval, 0.88-1.43).

CONCLUSIONS

The use of CADe in clinical practice did not increase ADR with statistical significance when compared to colonoscopy without CADe. These findings suggest that the impact of CADe systems in everyday clinical practice are modest.

Polyp detection with colonoscopy assisted by the GI Genius artificial intelligence endoscopy module compared with standard colonoscopy in routine colonoscopy practice (COLO-DETECT): a multicentre, open-label, parallel-arm, pragmatic randomised controlled trial

BACKGROUND

Increased polyp detection during colonoscopy is associated with decreased post-colonoscopy colorectal cancer incidence and mortality. The COLO-DETECT trial aimed to assess the clinical effectiveness of the GI Genius intelligent endoscopy module for polyp detection, comparing colonoscopy assisted by GI Genius (computer-aided detection [CADe]-assisted colonoscopy) with standard colonoscopy in routine practice.

METHODS

We did a multicentre, open-label, parallel-arm, pragmatic randomised controlled trial in 12 National Health Service (NHS) hospitals (ten NHS Trusts) in England, among adults (aged ≥18 years) undergoing planned colonoscopy for gastrointestinal symptoms or for surveillance due to personal or family history (ie, symptomatic indications), or colorectal cancer screening. Randomisation (1:1) to CADe-assisted colonoscopy or standard colonoscopy was done with a web-based dynamic adaptive algorithm, immediately before colonoscopy, with stratification by age group, sex, colonoscopy indication (screening or symptomatic), and NHS Trust. Recruiting staff, participants, and colonoscopists were unmasked to trial allocation; histopathologists, co-chief investigators, and trial statisticians were masked. CADe-assisted colonoscopy consisted of standard colonoscopy plus the GI Genius module active for at least the entire inspection phase of colonoscope withdrawal. The primary outcome was mean adenomas per procedure (total number of adenomas detected divided by total number of procedures); the key secondary outcome was adenoma detection rate (proportion of colonoscopies with at least one adenoma). Analysis was by intention to treat (ITT), with outcomes compared between groups by mixed-effects regression modelling, in which effect estimates were adjusted for randomisation stratification variables. Data were imputed for outcome measures with more than 5% missing values. All participants who underwent colonoscopy were assessed for safety. The trial is registered on ISRCTN (ISRCTN10451355) and ClinicalTrials.gov (NCT04723758), and is complete.

FINDINGS

Between March 29, 2021, and April 6, 2023, 2032 participants (1132 [55·7%] male, 900 [44·3%] female; mean age 62·4 years [SD 10·8]) were recruited and randomly assigned: 1015 to CADe-assisted colonoscopy and 1017 to standard colonoscopy. 1231 (60·6%) participants were undergoing screening and 801 (39·4%) had symptomatic indications. Mean adenomas per procedure was 1·56 (SD 2·82; n=1001 participants with available data) in the CADe-assisted colonoscopy group versus 1·21 (1·91; n=1009) in the standard colonoscopy group, representing an adjusted mean difference of 0·36 (95% CI 0·14-0·57; adjusted incidence rate ratio 1·30 [95% CI 1·15-1·47], p<0·0001). Adenomas were detected in 555 (56·6%) of 980 participants in the CADe-assisted colonoscopy group versus 477 (48·4%) of 986 in the standard colonoscopy group, representing a proportion difference of 8·3% (95% CI 3·9-12·7; adjusted odds ratio 1·47 [95% CI 1·21-1·78], p<0·0001). Numbers of adverse events were similar between the CADe-assisted colonoscopy and standard colonoscopy groups (adverse events: 25 vs 19; serious adverse events: four vs six), and no adverse events in the CADe-assisted colonoscopy group were deemed to be related to GI Genius use on independent review.

INTERPRETATION

Results of the COLO-DETECT trial support the use of GI Genius to increase detection of premalignant colorectal polyps in routine colonoscopy practice.

FUNDING

Medtronic.

Comparative bibliometric analysis of artificial intelligence-assisted polyp diagnosis and AI-assisted digestive endoscopy: trends and growth in AI gastroenterology (2003-2023)

Introduction

Artificial intelligence is already widely utilized in gastroenterology. This study aims to comprehensively evaluate the research hotspots and development trends within the field of AI in gastroenterology by employing bibliometric techniques to scrutinize geographical distribution, authorship, affiliated institutions, keyword usage, references, and other pertinent data contained within relevant publications.

Methods

This investigation compiled all pertinent publications related to artificial intelligence in the context of gastrointestinal polyps and digestive endoscopy from 2003 to 2023 within the Web of Science Core Collection database. Furthermore, the study harnessed the tools CiteSpace, VOSviewer, GraphPad Prism and Scimago Graphica for visual data analysis. The study retrieved a total of 2,394 documents in the field of AI in digestive endoscopy and 628 documents specifically related to AI in digestive tract polyps.

Results

The United States and China are the primary contributors to research in both fields. Since 2019, studies on AI for digestive tract polyps have constituted approximately 25% of the total AI digestive endoscopy studies annually. Six of the top 10 most-cited studies in AI digestive endoscopy also rank among the top 10 most-cited studies in AI for gastrointestinal polyps. Additionally, the number of studies on AI-assisted polyp segmentation is growing the fastest, with significant increases in AI-assisted polyp diagnosis and real-time systems beginning after 2020.

Discussion

The application of AI in gastroenterology has garnered increasing attention. As theoretical advancements in AI for gastroenterology have progressed, real-time diagnosis and detection of gastrointestinal diseases have become feasible in recent years, highlighting the promising potential of AI in this field.

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.

Traditional septotomy or Z-POEM for Zenker's diverticulum

Zenker's diverticulum (ZD), also known as a cricopharyngeal pouch, is a pulsion pseudodiverticulum located dorsally at the pharyngoesophageal junction. The pathophysiology of ZD involves cricopharyngeal spasm, incoordination, impaired upper esophageal sphincter opening, and structural changes in the cricopharyngeal muscle, leading to symptoms such as dysphagia, regurgitation of undigested food, foreign body sensation, halitosis, unintentional weight loss, and respiratory issues. Treatment for symptomatic ZD typically involves myotomy of the cricopharyngeal muscle. Endoscopic techniques, particularly flexible endoscopy septotomy (FES) and Zenker peroral endoscopic myotomy (Z-POEM), have become preferred options due to their minimally invasive nature. This review discusses the techniques and clinical outcomes of FES and Z-POEM, focusing on specific clinical scenarios to guide the choice between these methods. Additionally, the variability in FES techniques, the effectiveness of Z-POEM, and the impact of different diverticulum sizes on treatment outcomes are analyzed, providing a comprehensive overview of current therapeutic approaches for ZD.

Screening and Surveillance of Colorectal Cancer: A Review of the Literature

Colorectal cancer (CRC) has the highest mortality rate among men and is the second highest among women under fifty, with incidence and mortality rates rising in younger populations. Studies indicate that up to one-third of patients diagnosed before fifty have a family history or genetic factors, highlighting the need for earlier screening. Contrariwise, diagnosis in healthy subjects through screening strategies enables early-stage detection of the tumor and better clinical outcomes. In recent years, mortality rates of CRC in Western countries have been on a steady decline, which is largely attributed to widespread screening programs and advancements in treatment modalities. Indeed, early detection through screening significantly improves prognosis, with stark differences in survival rates between localized and metastatic disease. This article aims to provide a comprehensive review of the existing literature, delving into the performance and efficacy of various CRC screening strategies. It navigates through available screening tools, evaluating their efficacy and cost-effectiveness. The discussion extends to delineating target populations for screening, emphasizing the importance of tailored approaches for individuals at heightened risk.

Relative Efficacies of Interventions to Improve the Quality of Screening-Related Colonoscopy: A Systematic Review and Network Meta-Analysis of Randomized Controlled Trials

BACKGROUND & AIMS

Significant variability exists in colonoscopy quality indicators, including adenoma detection rate (ADR). We synthesized evidence from randomized trials in a network meta-analysis on interventions to improve colonoscopy quality.

METHODS

We included trials from database inceptions to September 25, 2023, of patients undergoing screening-related colonoscopy and presented efficacies of interventions within domains (periprocedural parameters, endoscopist-directed interventions, intraprocedural techniques, endoscopic technologies, distal attachment devices, and additive substances) compared to standard colonoscopy. The primary outcome was ADR. We used a Bayesian random-effects model using Markov-chain Monte Carlo simulation, with 10,000 burn-ins and 100,000 iterations. We calculated odds ratios with 95% credible intervals and present surface under the cumulative ranking (SUCRA) curves.

RESULTS

We included 124 trials evaluating 37 interventions for the primary outcome. Nine interventions resulted in statistically significant improvements in ADR compared to standard colonoscopy (9-minute withdrawal time, dual observation, water exchange, i-SCAN [Pentax Ltd], linked color imaging, computer-aided detection, Endocuff [Olympus Corp], Endocuff Vision [Olympus Corp], and oral methylene blue). Dual observation (SUCRA, 0.84) and water exchange (SUCRA, 0.78) ranked highest among intraprocedural techniques; i-SCAN (SUCRA, 0.95), linked color imaging (SUCRA, 0.85), and computer-aided detection (SUCRA, 0.78) among endoscopic technologies; WingCap (A&A Medical Supply LLC) (SUCRA, 0.87) and Endocuff (SUCRA, 0.85) among distal attachment devices and oral methylene blue (SUCRA, 0.94) among additive substances. No interventions improved detection of advanced adenomas, and only narrow-band imaging improved detection of serrated lesions (odds ratio, 2.94; 95% credible interval, 1.46-6.25).

CONCLUSIONS

Several interventions are effective in improving adenoma detection and overall colonoscopy quality, many of which are cost-free. These results can inform endoscopists, unit managers, and endoscopy societies on relative efficacies.

Non-polypoid Colorectal Lesions Detection and False Positive Detection by Artificial Intelligence under Blue Laser Imaging and Linked Color Imaging

Objectives

Artificial intelligence (AI) with white light imaging (WLI) is not enough for detecting non-polypoid colorectal polyps and it still has high false positive rate (FPR). We developed AIs using blue laser imaging (BLI) and linked color imaging (LCI) to detect them with specific learning sets (LS).

Methods

The contents of LS were as follows, LS (WLI): 1991 WLI images of lesion of 2-10 mm, LS (IEE): 5920 WLI, BLI, and LCI images of non-polypoid and small lesions of 2-20 mm. LS (IEE) was extracted from videos and included both in-focus and out-of-focus images. We designed three AIs as follows: AI (WLI) finetuned by LS (WLI), AI (IEE) finetuned by LS (WLI)+LS (IEE), and AI (HQ) finetuned by LS (WLI)+LS (IEE) only with images in focus. Polyp detection using a test set of WLI, BLI, and LCI videos of 100 non-polypoid or non-reddish lesions of 2-20 mm and FPR using movies of 15 total colonoscopy were analyzed, compared to 2 experts and 2 trainees.

Results

The sensitivity for LCI in AI (IEE) (83%) was compared to that for WLI in AI (IEE) (76%: p=0.02), WLI in AI (WLI) (57%: p<0.01), BLI in AI (IEE) (78%: p=0.14), and LCI in trainees (74%: p<0.01). The sensitivity for LCI in AI (IEE) (83%) was significantly higher than that in AI (HQ) (78%: p<0.01). The FPR for LCI (6.5%) in AI (IEE) were significantly lower than that in AI (HQ) (17.3%: p<0.01).

Conclusions

AI finetuned by appropriate LS detected non-reddish and non-polypoid polyps under LCI.

Endoscopic Artificial Intelligence for Image Analysis in Gastrointestinal Neoplasms

BACKGROUND

Artificial intelligence (AI) using deep learning systems has recently been utilized in various medical fields. In the field of gastroenterology, AI is primarily implemented in image recognition and utilized in the realm of gastrointestinal (GI) endoscopy. In GI endoscopy, computer-aided detection/diagnosis (CAD) systems assist endoscopists in GI neoplasm detection or differentiation of cancerous or noncancerous lesions. Several AI systems for colorectal polyps have already been applied in colonoscopy clinical practices. In esophagogastroduodenoscopy, a few CAD systems for upper GI neoplasms have been launched in Asian countries. The usefulness of these CAD systems in GI endoscopy has been gradually elucidated.

SUMMARY

In this review, we outline recent articles on several studies of endoscopic AI systems for GI neoplasms, focusing on esophageal squamous cell carcinoma (ESCC), esophageal adenocarcinoma (EAC), gastric cancer (GC), and colorectal polyps. In ESCC and EAC, computer-aided detection (CADe) systems were mainly developed, and a recent meta-analysis study showed sensitivities of 91.2% and 93.1% and specificities of 80% and 86.9%, respectively. In GC, a recent meta-analysis study on CADe systems demonstrated that their sensitivity and specificity were as high as 90%. A randomized controlled trial (RCT) also showed that the use of the CADe system reduced the miss rate. Regarding computer-aided diagnosis (CADx) systems for GC, although RCTs have not yet been conducted, most studies have demonstrated expert-level performance. In colorectal polyps, multiple RCTs have shown the usefulness of the CADe system for improving the polyp detection rate, and several CADx systems have been shown to have high accuracy in colorectal polyp differentiation.

KEY MESSAGES

Most analyses of endoscopic AI systems suggested that their performance was better than that of nonexpert endoscopists and equivalent to that of expert endoscopists. Thus, endoscopic AI systems may be useful for reducing the risk of overlooking lesions and improving the diagnostic ability of endoscopists.

Endocuff With or Without Artificial Intelligence-Assisted Colonoscopy in Detection of Colorectal Adenoma: A Randomized Colonoscopy Trial

INTRODUCTION

Both artificial intelligence (AI) and distal attachment devices have been shown to improve adenoma detection rate and reduce miss rate during colonoscopy. We studied the combined effect of Endocuff and AI on enhancing detection rates of various colonic lesions.

METHODS

This was a 3-arm prospective randomized colonoscopy study involving patients aged 40 years or older. Participants were randomly assigned in a 1:1:1 ratio to undergo Endocuff with AI, AI alone, or standard high-definition (HD) colonoscopy. The primary outcome was adenoma detection rate (ADR) between the Endocuff-AI and AI groups while secondary outcomes included detection rates of polyp (PDR), sessile serrated lesion (sessile detection rate [SDR]), and advanced adenoma (advanced adenoma detection rate) between the 2 groups.

RESULTS

A total of 682 patients were included (mean age 65.4 years, 52.3% male), with 53.7% undergoing diagnostic colonoscopy. The ADR for the Endocuff-AI, AI, and HD groups was 58.7%, 53.8%, and 46.3%, respectively, while the corresponding PDR was 77.0%, 74.0%, and 61.2%. A significant increase in ADR, PDR, and SDR was observed between the Endocuff-AI and AI groups (ADR difference: 4.9%, 95% CI: 1.4%-8.2%, P = 0.03; PDR difference: 3.0%, 95% CI: 0.4%-5.8%, P = 0.04; SDR difference: 6.4%, 95% CI: 3.4%-9.7%, P < 0.01). Both Endocuff-AI and AI groups had a higher ADR, PDR, SDR, and advanced adenoma detection rate than the HD group (all P < 0.01).

DISCUSSION

Endocuff in combination with AI further improves various colonic lesion detection rates when compared with AI alone.

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.