Deep Learning Computer-aided Polyp Detection Reduces Adenoma Miss Rate: A United States Multi-center Randomized Tandem Colonoscopy Study (CADeT-CS Trial)

Artificial intelligence for reducing missed detection of adenomas and polyps in colonoscopy: A systematic review and meta-analysis

BACKGROUND

Colorectal cancer has a high incidence and mortality rate, and the effectiveness of routine colonoscopy largely depends on the endoscopist’s expertise. In recent years, computer-aided detection (CADe) systems have been increasingly integrated into colonoscopy to improve detection accuracy. However, while most studies have focused on adenoma detection rate (ADR) as the primary outcome, the more sensitive adenoma miss rate (AMR) has been less frequently analyzed.

AIM

To evaluate the effectiveness of CADe in colonoscopy and assess the advantages of AMR over ADR.

METHODS

A comprehensive literature search was conducted in PubMed, Embase, and the Cochrane Central Register of Controlled Trials using predefined search strategies to identify relevant studies published up to August 2, 2024. Statistical analyses were performed to compare outcomes between groups, and potential publication bias was assessed using funnel plots. The quality of the included studies was evaluated using the Cochrane Risk of Bias tool and the Grading of Recommendations, Assessment, Development, and Evaluation approach.

RESULTS

Five studies comprising 1624 patients met the inclusion criteria. AMR was significantly lower in the CADe-assisted group than in the routine colonoscopy group (147/927, 15.9% vs 345/960, 35.9%; P < 0.01). However, CADe did not provide a significant advantage in detecting advanced adenomas or lesions measuring 6-9 mm or ≥ 10 mm. The polyp miss rate (PMR) was also lower in the CADe-assisted group [odds ratio (OR), 0.35; 95% confidence interval (CI): 0.23-0.52; P < 0.01]. While the overall ADR did not differ significantly between groups, the ADR during the first-pass examination was higher in the CADe-assisted group (OR, 1.37; 95%CI: 1.10-1.69; P = 0.004). The level of evidence for the included randomized controlled trials was graded as moderate.

CONCLUSION

CADe can significantly reduce AMR and PMR while improving ADR during initial detection, demonstrating its potential to enhance colonoscopy performance. These findings highlight the value of CADe in improving the detection of colorectal neoplasms, particularly small and histologically distinct adenomas.

Artificial intelligence system improves the quality of digestive endoscopy: A prospective pretest and post-test single-center clinical trial

BACKGROUND

With the assistance of ENDOANGEL, a study was conducted at Hainan General Hospital to evaluate the effect of artificial intelligence (AI) system on the detection of gastrointestinal precancerous lesions.

METHODS

The prospective, randomized, pretest and post-test, single-center clinical trial compared the detection rates of gastric precancerous lesions and intestinal adenomas between baseline and post-intervention phase among traditional digestive endoscopy (control groups i and ii, and experimental group i) and AI-assisted endoscopy (experimental group ii). Additionally, the effect of AI on the detection rate of different seniority physicians was analyzed.

RESULTS

AI assistance significantly increased the detection rates of intestinal metaplasia (experimental group ii vs control group ii: 14.23 % vs 9.15 %, P = 0.013), atrophy (experimental group ii vs control group ii: 22.76 % vs 17.28 %, P = 0.031) and intestinal adenomas (experimental group ii vs control group ii: 48.52 % vs 24.58 %, P < 0.001). The improvement was particularly notable among junior doctors, with significant enhancements in the detection rates of intestinal metaplasia (experimental group ii vs control group ii: 14.39 % vs 9.09 %, P = 0.008), atrophy (experimental group ii vs control group ii: 22.04 % vs 15.31 %, P = 0.004), and intestinal adenomas (experimental group ii vs control group ii: 45.18 % vs 29.27 %, P = 0.002).

CONCLUSIONS

AI systems have the potential to significantly improve the detection rates of precancerous conditions, particularly among less experienced endoscopists. This advancement can lead to more accurate and appropriate follow-up and review strategies for patients, ultimately reducing the risk of missed early cancer diagnoses.

Mucin 5AC as a Biomarker for Sessile Serrated Lesions: Results From a Systematic Review and Meta-Analysis

INTRODUCTION

Sessile serrated lesions (SSLs) are a class of colon polyps challenging to detect through current screening methods but highly associated with colon cancer. To improve detection, we sought a biomarker sensitive for SSLs. Recent endoscopic and histopathologic studies suggest that SSLs are associated with alterations in intestinal mucin expression, but the frequency with which this occurs is not known.

METHODS

We performed a meta-analysis of available pathologic studies comparing mucin expression on SSLs to normal colonic mucosa, tubular adenomas, villous adenomas, traditional serrated adenomas (TSAs), and hyperplastic polyps (HPs). We searched Medline, Pubmed, and Embase and found 440 publications in this topic, and 18 total studies met inclusion.

RESULTS

We found that MUC5AC expression was more common in SSLs compared to normal colonic mucosa (OR = 82.9, P < 0.01), tubular adenoma (OR = 11, P < 0.01), and TSAs (OR = 3.6, P = 0.04). We found no difference in MUC5AC expression between SSLs versus HPs (OR = 2.1, P = 0.09) and no difference in MUC5AC expression between left colon and right colon HPs, with an OR = 1.8, P = 0.23.

DISCUSSION

We found that MUC5AC expression was found commonly on villous adenoma, SSLs, and TSAs while the frequency on colon cancers declined. MUC5AC is also upregulated in inflammatory bowel disease and in response to intestinal infections. MUC5AC expression highlights the potential of mucins as useful biomarkers, though not specific to SSLs. Further research into the clinical utility of MUC5AC as a pathologic or fecal biomarker could enhance SSL detection.

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.

One in three adenomas could be missed by white-light colonoscopy - findings from a systematic review and meta-analysis

BACKGROUND

White light (conventional) colonoscopy (WLC) is widely used for colorectal cancer screening, diagnosis and surveillance but endoscopists may fail to detect adenomas. Our goal was to assess and synthesize overall and subgroup-specific adenoma miss rates (AMR) of WLC in daily practice.

METHODS

We conducted a systematic review in MEDLINE, EMBASE, Cochrane Library, and grey literature on studies evaluating diagnostic WLC accuracy in tandem studies with novel-colonoscopic technologies (NCT) in subjects undergoing screening, diagnostic or surveillance colonoscopy. Information on study design, AMR overall and specific for adenoma size, histology, location, morphology and further outcomes were extracted and reported in standardized evidence tables. Study quality was assessed using the QUADAS-2 tool. Random-effects meta-analyses and meta-regression were performed to estimate pooled estimates for AMR with 95% confidence intervals (95% CI) and to explain heterogeneity.

RESULTS

Out of 5,963 identified studies, we included sixteen studies with 4,101 individuals in our meta-analysis. One in three adenomas (34%; 95% CI: 30-38%) was missed by WLC in daily practice individuals. Subgroup analyses showed significant AMR differences by size (36%, adenomas 1-5 mm; 27%, adenomas 6-9 mm; 12%, adenomas ≥ 10 mm), histology (non-advanced: 42%, advanced: 21%), morphology (flat: 50%, polypoid: 27%), but not by location (distal: 36%, proximal: 36%).

CONCLUSIONS

Based on our meta-analysis, one in three adenomas could be missed by WLC. This may significantly contribute to interval cancers. Our results should be considered in health technology assessment when interpreting sensitivity of fecal occult blood or other screening tests derived from studies using WLC as "gold standard".

Artificial intelligence in endoscopic diagnosis of esophageal squamous cell carcinoma and precancerous lesions

Esophageal squamous cell carcinoma (ESCC) poses a significant global health challenge, necessitating early detection, timely diagnosis, and prompt treatment to improve patient outcomes. Endoscopic examination plays a pivotal role in this regard. However, despite the availability of various endoscopic techniques, certain limitations can result in missed or misdiagnosed ESCCs. Currently, artificial intelligence (AI)-assisted endoscopic diagnosis has made significant strides in addressing these limitations and improving the diagnosis of ESCC and precancerous lesions. In this review, we provide an overview of the current state of AI applications for endoscopic diagnosis of ESCC and precancerous lesions in aspects including lesion characterization, margin delineation, invasion depth estimation, and microvascular subtype classification. Furthermore, we offer insights into the future direction of this field, highlighting potential advancements that can lead to more accurate diagnoses and ultimately better prognoses for patients.

Colorectal Neoplasia in Inflammatory Bowel Disease

Patients with inflammatory bowel disease (IBD), including ulcerative colitis and colonic Crohn's disease, are at an increased risk of developing colonic dysplasia and neoplasia. Multiple risk factors have been identified that increase the risk of colonic neoplasia in IBD, including but not limited to underlying disease extent, severity, duration, and concomitant primary sclerosing cholangitis. The overall risk of colonic neoplasia in IBD is decreasing but surveillance is still warranted in patients with high-risk features. In this review, we will discuss the epidemiology, pathogenesis, risk factors, approach to surveillance, and management of colonic neoplasia in IBD.

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.

A Narrative Review on the Role of Artificial Intelligence (AI) in Colorectal Cancer Management

The role of artificial intelligence (AI) tools and deep learning in medical practice in the management of colorectal cancer has gathered significant attention in recent years. Colorectal cancer, being the third most common type of malignancy, requires an innovative approach to augment early detection and advanced surgical techniques to reduce morbidity and mortality. With its emerging potential, AI improves colorectal cancer management by assisting with accuracy in screening, pathology evaluation, precision, and postoperative care. Evidence suggests that AI minimizes missed cases during colorectal cancer screening, plays a promising role in pathology and imaging diagnoses, and facilitates accurate staging. In surgical management, AI demonstrates comparable or superior outcomes to laparoscopic approaches, with reduced hospital stays and conversion rates. However, these outcomes are influenced by clinical expertise and other dependable factors, including expertise in implementing AI-based software and detecting possible errors. Despite these advancements, limited multicenter studies and randomized trials restrict the comprehensive evaluation of AI's true potential and integration into standard practice. We used Pubmed, Google Scholar, Cochrane Library, and Scopus databases for this review. The final number of articles selected, depending on inclusion and exclusion criteria, is 122. We included papers published in the English language, literature published in the last 10 years, and adult patient populations above 35 years with colorectal cancer. We thoroughly included randomized controlled trials, cohort studies, meta-analyses, systematic reviews, narrative reviews, and case-control studies. The use of AI paves the way for the adoption of more personalized medicine. This review highlights the advantages of AI at various disease stages for colorectal cancer patients and evaluates its potential for cost-effective implementation in clinical practice.

Artificial Intelligence and Early Detection of Breast, Lung, and Colon Cancer: A Narrative Review

Artificial intelligence (AI) is revolutionizing early cancer detection by enhancing the sensitivity, efficiency, and precision of screening programs for breast, colorectal, and lung cancers. Deep learning algorithms, such as convolutional neural networks, are pivotal in improving diagnostic accuracy by identifying patterns in imaging data that may elude human radiologists. AI has shown remarkable advancements in breast cancer detection, including risk stratification and treatment planning, with models achieving high specificity and precision in identifying invasive ductal carcinoma. In colorectal cancer screening, AI-powered systems significantly enhance polyp detection rates during colonoscopies, optimizing the adenoma detection rate and improving diagnostic workflows. Similarly, low-dose CT scans integrated with AI algorithms are transforming lung cancer screening by increasing the sensitivity and specificity of early-stage cancer detection, while aiding in accurate lesion segmentation and classification. This review highlights the potential of AI to streamline cancer diagnosis and treatment by analyzing vast datasets and reducing diagnostic variability. Despite these advancements, challenges such as data standardization, model generalization, and integration into clinical workflows remain. Addressing these issues through collaborative research, enhanced dataset diversity, and improved explainability of AI models will be critical for widespread adoption. The findings underscore AI's potential to significantly impact patient outcomes and reduce cancer-related mortality, emphasizing the need for further validation and optimization in diverse healthcare settings.

Use of Artificial Intelligence in Lower Gastrointestinal and Small Bowel Disorders: An Update Beyond Polyp Detection

Machine learning and its specialized forms, such as Artificial Neural Networks and Convolutional Neural Networks, are increasingly being used for detecting and managing gastrointestinal conditions. Recent advancements involve using Artificial Neural Network models to enhance predictive accuracy for severe lower gastrointestinal (LGI) bleeding outcomes, including the need for surgery. To this end, artificial intelligence (AI)-guided predictive models have shown promise in improving management outcomes. While much literature focuses on AI in early neoplasia detection, this review highlights AI's role in managing LGI and small bowel disorders, including risk stratification for LGI bleeding, quality control, evaluation of inflammatory bowel disease, and video capsule endoscopy reading. Overall, the integration of AI into routine clinical practice is still developing, with ongoing research aimed at addressing current limitations and gaps in patient care.

Innovative approaches in colorectal cancer screening: advances in detection methods and the role of artificial intelligence

Colorectal cancer (CRC) is the third most prevalent cancer globally and poses a significant health threat, making early detection crucial. This review paper explored emerging detection methods for early screening of CRC, including gut microbiota, metabolites, genetic markers, and artificial intelligence (AI)-based technologies. Current screening methods have their respective advantages and limitations, particularly in detecting precursors. First, the importance of the gut microbiome in CRC progression is discussed, highlighting how specific microbial alterations can serve as biomarkers for early detection, potentially enhancing diagnostic accuracy when combined with traditional screening methods. Next, research on metabolic reprogramming illustrates the relationship between metabolic changes and CRC, with studies developing metabolite-based detection models that show good sensitivity for early diagnosis. In terms of genetic markers, methylated DNA markers like SEPTIN9 have demonstrated high sensitivity, although further validation across diverse populations is necessary. Lastly, AI technology has shown immense potential in improving adenoma detection rates, significantly enhancing the quality of colonoscopic examinations through image recognition techniques. This review aims to provide a comprehensive perspective on new strategies for CRC screening, emphasizing the potential of noninvasive detection technologies and the prospects of AI and genomics in clinical applications. Despite several challenges, this review advocates for future large-scale prospective studies to validate the effectiveness and cost-effectiveness of these new screening methods while promoting the implementation of screening protocols tailored to individual characteristics.

Efficiency of Real-time Computer-aided Polyp Detection during Surveillance Colonoscopy: A Pilot Study

Objectives

Studies have suggested that computer-aided polyp detection using artificial intelligence improves adenoma identification during colonoscopy. However, its real-world effectiveness remains unclear. Therefore, this study evaluated the usefulness of computer-aided detection during regular surveillance colonoscopy.

Methods

Consecutive patients who underwent surveillance colonoscopy with computer-aided detection between January and March 2023 and had undergone colonoscopy at least twice during the past 3 years were recruited. The clinicopathological findings of lesions identified using computer-aided detection were evaluated. The detection ability was sub-analyzed based on the expertise of the endoscopist and the presence of diminutive adenomas (size ≤5 mm).

Results

A total of 78 patients were included. Computer-aided detection identified 46 adenomas in 28 patients; however, no carcinomas were identified. The mean withdrawal time was 824 ± 353 s, and the mean tumor diameter was 3.3 mm (range, 2-8 mm). The most common gross type was 0-Is (70%), followed by 0-Isp (17%) and 0-IIa (13%). The most common tumor locations were the ascending colon and sigmoid colon (28%), followed by the transverse colon (26%), cecum (7%), descending colon (7%), and rectum (4%). Overall, 34.1% and 38.2% of patients with untreated diminutive adenomas and those with no adenomas, respectively, had newly detected adenomas. Endoscopist expertise did not affect the results.

Conclusions

Computer-aided detection may help identify adenomas during surveillance colonoscopy for patients with untreated diminutive adenomas and those with a history of endoscopic resection.

Automatic Quality Control System and Adenoma Detection Rates During Routine Colonoscopy: A Randomized Clinical Trial

Importance

High-quality colonoscopy reduces the risks of colorectal cancer by increasing the adenoma detection rate. Routine use of an automatic quality control system (AQCS) to assist in colorectal adenoma detection should be considered.

Objective

To evaluate the effect of an AQCS on the adenoma detection rate among colonoscopists who were moderate- and low-level detectors during routine colonoscopy.

Design, Setting, and Participants

This multicenter, single-blind, randomized clinical trial was conducted at 6 centers in China from August 1, 2021, to September 30, 2022. Data were analyzed from March 1 to June 30, 2023. Individuals aged 18 to 80 years were enrolled. Exclusion criteria were a history of inflammatory bowel disease, advanced colorectal cancer, and polyposis syndromes; known colorectal polyps without complete removal previously; a history of colorectal surgery; known stenosis or obstruction with contraindication for biopsy or prior failed colonoscopy; pregnancy or lactation; and refusal to participate. Intention-to-treat and per-protocol analysis was used.

Interventions

Standard colonoscopy or AQCS-assisted colonoscopy.

Main Outcomes and Measures

Adenoma detection rate.

Results

A total of 1254 participants (mean [SD] age, 51.21 [12.10] years; 674 [53.7%] male) were randomized (627 standard colonoscopy, 627 AQCS-assisted colonoscopy). Intention-to-treat analysis showed a significantly higher adenoma detection rate in the AQCS-assisted group vs standard colonoscopy group (32.7% vs 22.6%; relative risk [RR], 1.60; 95% CI, 1.23-2.09; P < .001). The adenoma detection rates were significantly higher in the AQCS group when considering pathology (nonadvanced adenomas, 30.1% vs 21.2%; RR, 1.52; 95% CI, 1.16-1.99; P = .002), and morphology (flat or sessile, 29.3% vs 20.4%, RR, 1.52; 95% CI, 1.16-2.00; P = .003). Use of AQCS significantly increased the adenoma detection rate of both the lower-level detectors (30.0% vs 20.0%; RR, 1.71; 95% CI, 1.24-2.35; P = .001) and the medium-level detectors (38.1% vs 27.7%; RR, 1.61; 95% CI, 1.07-2.43; P = .02). Similar increases were found for adenoma detection rates in the academic and nonacademic centers (academic: 29.3% vs 20.8%; RR, 1.58; 95% CI, 1.10-2.29; P = .01; nonacademic: 36.1% vs 24.5%; RR, 1.74; 95% CI, 1.23-2.46; P = .002). The number of adenomas per colonoscopy was significantly higher in the AQCS-assisted group (0.86 vs 0.48; RR, 1.50; 95% CI, 1.17-1.91; P = .001). The mean withdrawal time without intervention was slightly increased with AQCS assistance (6.78 vs 6.46 minutes; RR, 1.38; 95% CI, 1.26-1.52; P < .001). No serious adverse events were reported.

Conclusions and Relevance

In this randomized clinical trial, AQCS assistance during routine colonoscopy increased adenoma detection rates and several related polyp parameters compared with standard colonoscopy in the lower- and medium-level detectors in academic and nonacademic settings. Routine use of AQCS to assist in colorectal adenoma detection and quality improvement should be considered.

Trial Registration

ClinicalTrials.gov Identifier: NCT04901130.

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.

Use of artificial intelligence improves colonoscopy performance in adenoma detection: a systematic review and meta-analysis

BACKGROUND AND AIMS

Artificial intelligence (AI) is increasingly used to improve adenoma detection during colonoscopy. This meta-analysis aimed to provide an updated evaluation of computer-aided detection (CADe) systems and their impact on key colonoscopy quality indicators.

METHODS

We searched the EMBASE, PubMed, and MEDLINE databases from inception until February 15, 2024, for randomized control trials (RCTs) comparing the performance of CADe systems with routine unassisted colonoscopy in the detection of colorectal adenomas.

RESULTS

Twenty-eight RCTs were selected for inclusion involving 23,861 participants. Random-effects meta-analysis demonstrated a 20% increase in adenoma detection rate (risk ratio [RR], 1.20; 95% confidence interval [CI], 1.14-1.27; P < .01) and 55% decrease in adenoma miss rate (RR, 0.45; 95% CI, 0.37-0.54; P < .01) with AI-assisted colonoscopy. Subgroup analyses involving only expert endoscopists demonstrated a similar effect size (RR, 1.19; 95% CI, 1.11-1.27; P < .001), with similar findings seen in analysis of differing CADe systems and healthcare settings. CADe use also significantly increased adenomas per colonoscopy (weighted mean difference, 0.21; 95% CI, 0.14-0.29; P < .01), primarily because of increased diminutive lesion detection, with no significant difference seen in detection of advanced adenomas. Sessile serrated lesion detection (RR, 1.10; 95% CI, 0.93-1.30; P = .27) and miss rates (RR, 0.44; 95% CI, 0.16-1.19; P = .11) were similar. There was an average 0.15-minute prolongation of withdrawal time with AI-assisted colonoscopy (weighted mean difference, 0.15; 95% CI, 0.04-0.25; P = .01) and a 39% increase in the rate of non-neoplastic resection (RR, 1.39; 95% CI, 1.23-1.57; P < .001).

CONCLUSIONS

AI-assisted colonoscopy significantly improved adenoma detection but not sessile serrated lesion detection irrespective of endoscopist experience, system type, or healthcare setting.

Consensus statements on the current landscape of artificial intelligence applications in endoscopy, addressing roadblocks, and advancing artificial intelligence in gastroenterology

BACKGROUND AND AIMS

The American Society for Gastrointestinal Endoscopy (ASGE) AI Task Force along with experts in endoscopy, technology space, regulatory authorities, and other medical subspecialties initiated a consensus process that analyzed the current literature, highlighted potential areas, and outlined the necessary research in artificial intelligence (AI) to allow a clearer understanding of AI as it pertains to endoscopy currently.

METHODS

A modified Delphi process was used to develop these consensus statements.

RESULTS

Statement 1: Current advances in AI allow for the development of AI-based algorithms that can be applied to endoscopy to augment endoscopist performance in detection and characterization of endoscopic lesions. Statement 2: Computer vision-based algorithms provide opportunities to redefine quality metrics in endoscopy using AI, which can be standardized and can reduce subjectivity in reporting quality metrics. Natural language processing-based algorithms can help with the data abstraction needed for reporting current quality metrics in GI endoscopy effortlessly. Statement 3: AI technologies can support smart endoscopy suites, which may help optimize workflows in the endoscopy suite, including automated documentation. Statement 4: Using AI and machine learning helps in predictive modeling, diagnosis, and prognostication. High-quality data with multidimensionality are needed for risk prediction, prognostication of specific clinical conditions, and their outcomes when using machine learning methods. Statement 5: Big data and cloud-based tools can help advance clinical research in gastroenterology. Multimodal data are key to understanding the maximal extent of the disease state and unlocking treatment options. Statement 6: Understanding how to evaluate AI algorithms in the gastroenterology literature and clinical trials is important for gastroenterologists, trainees, and researchers, and hence education efforts by GI societies are needed. Statement 7: Several challenges regarding integrating AI solutions into the clinical practice of endoscopy exist, including understanding the role of human-AI interaction. Transparency, interpretability, and explainability of AI algorithms play a key role in their clinical adoption in GI endoscopy. Developing appropriate AI governance, data procurement, and tools needed for the AI lifecycle are critical for the successful implementation of AI into clinical practice. Statement 8: For payment of AI in endoscopy, a thorough evaluation of the potential value proposition for AI systems may help guide purchasing decisions in endoscopy. Reliable cost-effectiveness studies to guide reimbursement are needed. Statement 9: Relevant clinical outcomes and performance metrics for AI in gastroenterology are currently not well defined. To improve the quality and interpretability of research in the field, steps need to be taken to define these evidence standards. Statement 10: A balanced view of AI technologies and active collaboration between the medical technology industry, computer scientists, gastroenterologists, and researchers are critical for the meaningful advancement of AI in gastroenterology.

CONCLUSIONS

The consensus process led by the ASGE AI Task Force and experts from various disciplines has shed light on the potential of AI in endoscopy and gastroenterology. AI-based algorithms have shown promise in augmenting endoscopist performance, redefining quality metrics, optimizing workflows, and aiding in predictive modeling and diagnosis. However, challenges remain in evaluating AI algorithms, ensuring transparency and interpretability, addressing governance and data procurement, determining payment models, defining relevant clinical outcomes, and fostering collaboration between stakeholders. Addressing these challenges while maintaining a balanced perspective is crucial for the meaningful advancement of AI in gastroenterology.

Validation of Artificial Intelligence Computer-Aided Detection of Colonic Neoplasm in Colonoscopy

BACKGROUND/OBJECTIVES

Controlling colonoscopic quality is important in the detection of colon polyps during colonoscopy as it reduces the overall long-term colorectal cancer risk. Artificial intelligence has recently been introduced in various medical fields. In this study, we aimed to validate a previously developed artificial intelligence (AI) computer-aided detection (CADe) algorithm called ALPHAON® and compare outcomes with previous studies that showed that AI outperformed and assisted endoscopists of diverse levels of expertise in detecting colon polyps.

METHODS

We used the retrospective data of 500 still images, including 100 polyp images and 400 healthy colon images. In addition, we validated the CADe algorithm and compared its diagnostic performance with that of two expert endoscopists and six trainees from Gachon University Gil Medical Center. After a washing-out period of over 2 weeks, endoscopists performed polyp detection on the same dataset with the assistance of ALPHAON®.

RESULTS

The CADe algorithm presented a high capability in detecting colon polyps, with an accuracy of 0.97 (95% CI: 0.96 to 0.99), sensitivity of 0.91 (95% CI: 0.85 to 0.97), specificity of 0.99 (95% CI: 0.97 to 0.99), and AUC of 0.967. When evaluating and comparing the polyp detection ability of ALPHAON® with that of endoscopists with different levels of expertise (regarding years of endoscopic experience), it was found that ALPHAON® outperformed the experts in accuracy (0.97, 95% CI: 0.96 to 0.99), sensitivity (0.91, 95% CI: 0.85 to 0.97), and specificity (0.99, 95% CI: 0.97 to 0.99). After a washing-out period of over 2 weeks, the overall capability significantly improved for both experts and trainees with the assistance of ALPHAON®.

CONCLUSIONS

The high performance of the CADe algorithm system in colon polyp detection during colonoscopy was verified. The sensitivity of ALPHAON® led to it outperforming the experts, and it demonstrated the ability to enhance the polyp detection ability of both experts and trainees, which suggests a significant possibility of ALPHAON® being able to provide endoscopic assistance.

Gastric neoplasm detection of computer-aided detection-assisted esophagogastroduodenoscopy changes with implement scenarios: a real-world study

BACKGROUND AND AIM

The implementation of computer-aided detection (CAD) devices in esophagogastroduodenoscopy (EGD) could autonomously identify gastric precancerous lesions and neoplasms and reduce the miss rate of gastric neoplasms in prospective trials. However, there is still insufficient evidence of their use in real-life clinical practice.

METHODS

A real-world, two-center study was conducted at Wenzhou Central Hospital (WCH) and Renmin Hospital of Wuhan University (RHWU). High biopsy rate and low biopsy rate strategies were adopted, and CAD devices were applied in 2019 and 2021 at WCH and RHWU, respectively. We compared differences in gastric precancerous and neoplasm detection of EGD before and after the use of CAD devices in the first half of the year.

RESULTS

A total of 33 885 patients were included and 32 886 patients were ultimately analyzed. In WCH of which biopsy rate >95%, with the implementation of CAD, more the number of early gastric cancer divided by all gastric neoplasm (EGC/GN) (0.35% vs 0.59%, P = 0.028, OR [95% CI] = 1.65 [1.0-2.60]) was found, while gastric neoplasm detection rate (1.39% vs 1.36%, P = 0.897, OR [95% CI] = 0.98 [0.76-1.26]) remained stable. In RHWU of which biopsy rate <20%, the gastric neoplasm detection rate (1.78% vs 3.23%, P < 0.001, OR [95% CI] = 1.84 [1.33-2.54]) nearly doubled after the implementation of CAD, while there was no significant change in the EGC/GN.

CONCLUSION

The application of CAD devices devoted to distinct increases in gastric neoplasm detection according to different biopsy strategies, which implied that CAD devices demonstrated assistance on gastric neoplasm detection while varied effectiveness according to different implementation scenarios.

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-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).

Clinical consequences of computer-aided colorectal polyp detection

BACKGROUND AND AIM

Randomised trials show improved polyp detection with computer-aided detection (CADe), mostly of small lesions. However, operator and selection bias may affect CADe's true benefit. Clinical outcomes of increased detection have not yet been fully elucidated.

METHODS

In this multicentre trial, CADe combining convolutional and recurrent neural networks was used for polyp detection. Blinded endoscopists were monitored in real time by a second observer with CADe access. CADe detections prompted reinspection. Adenoma detection rates (ADR) and polyp detection rates were measured prestudy and poststudy. Histological assessments were done by independent histopathologists. The primary outcome compared polyp detection between endoscopists and CADe.

RESULTS

In 946 patients (51.9% male, mean age 64), a total of 2141 polyps were identified, including 989 adenomas. CADe was not superior to human polyp detection (sensitivity 94.6% vs 96.0%) but outperformed them when restricted to adenomas. Unblinding led to an additional yield of 86 true positive polyp detections (1.1% ADR increase per patient; 73.8% were <5 mm). CADe also increased non-neoplastic polyp detection by an absolute value of 4.9% of the cases (1.8% increase of entire polyp load). Procedure time increased with 6.6±6.5 min (+42.6%). In 22/946 patients, the additional detection of adenomas changed surveillance intervals (2.3%), mostly by increasing the number of small adenomas beyond the cut-off.

CONCLUSION

Even if CADe appears to be slightly more sensitive than human endoscopists, the additional gain in ADR was minimal and follow-up intervals rarely changed. Additional inspection of non-neoplastic lesions was increased, adding to the inspection and/or polypectomy workload.

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).

Comparing Right-Sided Colon Adenoma and Serrated Polyp Miss Rates With Water Exchange and CO 2 Insufflation: A Randomized Controlled Trial

INTRODUCTION

Postcolonoscopy colorectal cancers primarily occur in the right-sided colon because of missed adenomas and serrated polyps (SPs). Water exchange (WE) improves cleanliness and visibility of the right-sided colon. We hypothesized that WE could reduce the right-sided colon adenoma (rAMR) and SP miss rate (rSPMR) compared with standard colonoscopy.

METHODS

We randomly assigned 386 colonoscopy patients to insertion with either WE or CO 2 insufflation. During the first withdrawal, polypectomies were performed up to the hepatic flexure. A second endoscopist, blinded to the insertion technique, re-examined the right-sided colon. The miss rate was determined by dividing the number of additional adenomas or SPs by the total number detected in both examinations. The primary outcome was the combined rAMR and rSPMR.

RESULTS

WE significantly decreased the combined rAMR and rSPMR (22.2% vs 32.2%, P < 0.001) and rSPMR alone (22.5% vs 37.1%, P = 0.002) compared with CO 2 insufflation, but not rAMR (21.8% vs 29.8%, P = 0.079). In addition, WE significantly increased the detection of SP per colonoscopy (SP per colonoscopy) in the right-sided colon (0.95 ± 1.56 vs 0.50 ± 0.79, P < 0.001). Multivariate logistic regression analysis showed that ≥2 SPs in the right-sided colon were an independent predictor of rSPMR (odds ratio, 3.47; 95% confidence interval, 1.89─6.38), along with a higher right-sided colon Boston Bowel Preparation Scale score (odds ratio, 0.55; 95% confidence interval, 0.32─0.94).

DISCUSSION

The significant reduction in rSPMR and increase in right-sided colon SP per colonoscopy suggest that colonoscopy insertion using WE is a valid alternative to CO 2 insufflation (clinical trial registration number: NCT04124393).

Artificial Intelligence in Coloproctology: A Review of Emerging Technologies and Clinical Applications

Artificial intelligence (AI) has emerged as a transformative tool across several specialties, namely gastroenterology, where it has the potential to optimize both diagnosis and treatment as well as enhance patient care. Coloproctology, due to its highly prevalent pathologies and tremendous potential to cause significant mortality and morbidity, has drawn a lot of attention regarding AI applications. In fact, its application has yielded impressive outcomes in various domains, colonoscopy being one prominent example, where it aids in the detection of polyps and early signs of colorectal cancer with high accuracy and efficiency. With a less explored path but equivalent promise, AI-powered capsule endoscopy ensures accurate and time-efficient video readings, already detecting a wide spectrum of anomalies. High-resolution anoscopy is an area that has been growing in interest in recent years, with efforts being made to integrate AI. There are other areas, such as functional studies, that are currently in the early stages, but evidence is expected to emerge soon. According to the current state of research, AI is anticipated to empower gastroenterologists in the decision-making process, paving the way for a more precise approach to diagnosing and treating patients. This review aims to provide the state-of-the-art use of AI in coloproctology while also reflecting on future directions and perspectives.

Influence of artificial intelligence on the diagnostic performance of endoscopists in the assessment of Barrett's esophagus: a tandem randomized and video trial

BACKGROUND

This study evaluated the effect of an artificial intelligence (AI)-based clinical decision support system on the performance and diagnostic confidence of endoscopists in their assessment of Barrett's esophagus (BE).

METHODS

96 standardized endoscopy videos were assessed by 22 endoscopists with varying degrees of BE experience from 12 centers. Assessment was randomized into two video sets: group A (review first without AI and second with AI) and group B (review first with AI and second without AI). Endoscopists were required to evaluate each video for the presence of Barrett's esophagus-related neoplasia (BERN) and then decide on a spot for a targeted biopsy. After the second assessment, they were allowed to change their clinical decision and confidence level.

RESULTS

AI had a stand-alone sensitivity, specificity, and accuracy of 92.2%, 68.9%, and 81.3%, respectively. Without AI, BE experts had an overall sensitivity, specificity, and accuracy of 83.3%, 58.1%, and 71.5%, respectively. With AI, BE nonexperts showed a significant improvement in sensitivity and specificity when videos were assessed a second time with AI (sensitivity 69.8% [95%CI 65.2%-74.2%] to 78.0% [95%CI 74.0%-82.0%]; specificity 67.3% [95%CI 62.5%-72.2%] to 72.7% [95%CI 68.2%-77.3%]). In addition, the diagnostic confidence of BE nonexperts improved significantly with AI.

CONCLUSION

BE nonexperts benefitted significantly from additional AI. BE experts and nonexperts remained significantly below the stand-alone performance of AI, suggesting that there may be other factors influencing endoscopists' decisions to follow or discard AI advice.

Will artificial intelligence reach any limit in gastroenterology?

Endoscopy is the cornerstone in the management of digestive diseases. Over the last few decades, technology has played an important role in the development of this field, helping endoscopists in better detecting and characterizing luminal lesions. However, despite ongoing advancements in endoscopic technology, the incidence of missed pre-neoplastic and neoplastic lesions remains high due to the operator-dependent nature of endoscopy and the challenging learning curve associated with new technologies. Artificial intelligence (AI), an operator-independent field, could be an invaluable solution. AI can serve as a “second observer”, enhancing the performance of endoscopists in detecting and characterizing luminal lesions. By utilizing deep learning (DL), an innovation within machine learning, AI automatically extracts input features from targeted endoscopic images. DL encompasses both computer-aided detection and computer-aided diagnosis, assisting endoscopists in reducing missed detection rates and predicting the histology of luminal digestive lesions. AI applications in clinical gastrointestinal diseases are continuously expanding and evolving the entire digestive tract. In all published studies, real-time AI assists endoscopists in improving the performance of non-expert gastroenterologists, bringing it to a level comparable to that of experts. The development of DL may be affected by selection biases. Studies have utilized different AI-assisted models, which are heterogeneous. In the future, algorithms need validation through large, randomized trials. Theoretically, AI has no limit to assist endoscopists in increasing the accuracy and the quality of endoscopic exams. However, practically, we still have a long way to go before standardizing our AI models to be accepted and applied by all gastroenterologists.

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.

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.

Measuring Size of Colorectal Polyps Using a Virtual Scale Endoscope or Visual Assessment: A Randomized Controlled Trial

INTRODUCTION

This study aimed to compare the accuracy of polyp size measurements using a virtual scale endoscope (VSE) with an integrated laser-based adaptive scale function and visual assessment (VA) during colonoscopies.

METHODS

We conducted a single-blinded, prospective randomized controlled trial. Eligible patients (aged 45-80 years) undergoing screening, surveillance, or diagnostic colonoscopies were randomly assigned (1:1) into 2 groups. In the intervention group, all detected polyps were measured for size using VSE; in the control group, all polyps were measured using VA. Size measurements were compared with a reference standard of digital caliper measurement immediately post polypectomy. The primary outcome was the relative accuracy of real-time VSE measurement compared with VA. Secondary outcomes included the mean differences and the correlations between VSE or VA sizes and the reference standard of measurement.

RESULTS

Overall, 230 patients were enrolled and randomized. The relative size measurement accuracy of VSE was 84% in 118 polyps, which was significantly higher than that of VA (105 polyps; 68.4%, P < 0.001). VSE resulted in a significantly higher percentage of size measurements within 25% of true size compared with VA (81.4% vs 41%, P < 0.001). VSE had a significantly lower percentage for >5-mm polyps incorrectly sized as 1-5 mm compared with VA (13.5% vs 57.1%; P < 0.001) and a significantly lower percentage for >3-mm polyps incorrectly sized as 1-3 mm compared with VA (11.3% vs 56.5%; P < 0.001).

DISCUSSION

VSE significantly improves the size measurement accuracy of colorectal polyps during colonoscopies compared with VA and results in fewer misclassifications at relevant decision-making size thresholds.

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.

Assessment of colonoscopy skill using machine learning to measure quality: Proof-of-concept and initial validation

Background and study aims Low-quality colonoscopy increases cancer risk but measuring quality remains challenging. We developed an automated, interactive assessment of colonoscopy quality (AI-CQ) using machine learning (ML). Methods Based on quality guidelines, metrics selected for AI development included insertion time (IT), withdrawal time (WT), polyp detection rate (PDR), and polyps per colonoscopy (PPC). Two novel metrics were also developed: HQ-WT (time during withdrawal with clear image) and WT-PT (withdrawal time subtracting polypectomy time). The model was pre-trained using a self-supervised vision transformer on unlabeled colonoscopy images and then finetuned for multi-label classification on another mutually exclusive colonoscopy image dataset. A timeline of video predictions and metric calculations were presented to clinicians in addition to the raw video using a web-based application. The model was externally validated using 50 colonoscopies at a second hospital. Results The AI-CQ accuracy to identify cecal intubation was 88%. IT ( P = 0.99) and WT ( P = 0.99) were highly correlated between manual and AI-CQ measurements with a median difference of 1.5 seconds and 4.5 seconds, respectively. AI-CQ PDR did not significantly differ from manual PDR (47.6% versus 45.5%, P = 0.66). Retroflexion was correctly identified in 95.2% and number of right colon evaluations in 100% of colonoscopies. HQ-WT was 45.9% of, and significantly correlated with ( P = 0.85) WT time. Conclusions An interactive AI assessment of colonoscopy skill can automatically assess quality. We propose that this tool can be utilized to rapidly identify and train providers in need of remediation.

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.

Use of a Novel Artificial Intelligence System Leads to the Detection of Significantly Higher Number of Adenomas During Screening and Surveillance Colonoscopy: Results From a Large, Prospective, US Multicenter, Randomized Clinical Trial

INTRODUCTION

Adenoma per colonoscopy (APC) has recently been proposed as a quality measure for colonoscopy. We evaluated the impact of a novel artificial intelligence (AI) system, compared with standard high-definition colonoscopy, for APC measurement.

METHODS

This was a US-based, multicenter, prospective randomized trial examining a novel AI detection system (EW10-EC02) that enables a real-time colorectal polyp detection enabled with the colonoscope (CAD-EYE). Eligible average-risk subjects (45 years or older) undergoing screening or surveillance colonoscopy were randomized to undergo either CAD-EYE-assisted colonoscopy (CAC) or conventional colonoscopy (CC). Modified intention-to-treat analysis was performed for all patients who completed colonoscopy with the primary outcome of APC. Secondary outcomes included positive predictive value (total number of adenomas divided by total polyps removed) and adenoma detection rate.

RESULTS

In modified intention-to-treat analysis, of 1,031 subjects (age: 59.1 ± 9.8 years; 49.9% male), 510 underwent CAC vs 523 underwent CC with no significant differences in age, gender, ethnicity, or colonoscopy indication between the 2 groups. CAC led to a significantly higher APC compared with CC: 0.99 ± 1.6 vs 0.85 ± 1.5, P = 0.02, incidence rate ratio 1.17 (1.03-1.33, P = 0.02) with no significant difference in the withdrawal time: 11.28 ± 4.59 minutes vs 10.8 ± 4.81 minutes; P = 0.11 between the 2 groups. Difference in positive predictive value of a polyp being an adenoma among CAC and CC was less than 10% threshold established: 48.6% vs 54%, 95% CI -9.56% to -1.48%. There were no significant differences in adenoma detection rate (46.9% vs 42.8%), advanced adenoma (6.5% vs 6.3%), sessile serrated lesion detection rate (12.9% vs 10.1%), and polyp detection rate (63.9% vs 59.3%) between the 2 groups. There was a higher polyp per colonoscopy with CAC compared with CC: 1.68 ± 2.1 vs 1.33 ± 1.8 (incidence rate ratio 1.27; 1.15-1.4; P < 0.01).

DISCUSSION

Use of a novel AI detection system showed to a significantly higher number of adenomas per colonoscopy compared with conventional high-definition colonoscopy without any increase in colonoscopy withdrawal time, thus supporting the use of AI-assisted colonoscopy to improve colonoscopy quality ( ClinicalTrials.gov NCT04979962).

Precision endoscopy in colorectal polyps' characterization and planning of endoscopic therapy

Precision endoscopy in the management of colorectal polyps and early colorectal cancer has emerged as the standard of care. It includes optical characterization of polyps and estimation of submucosal invasion depth of large nonpedunculated colorectal polyps to select the appropriate endoscopic resection modality. Over time, several imaging modalities have been implemented in endoscopic practice to improve optical performance. Among these, image-enhanced endoscopy systems and magnification endoscopy represent now well-established tools. New advanced technologies, such as endocytoscopy and confocal laser endomicroscopy, have recently shown promising results in predicting the histology of colorectal polyps. In recent years, artificial intelligence has continued to enhance endoscopic performance in the characterization of colorectal polyps, overcoming the limitations of other imaging modes. In this review we retrace the path of precision endoscopy, analyzing the yield of various endoscopic imaging techniques in personalizing management of colorectal polyps and early colorectal cancer.

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.

Applications of artificial intelligence in urologic oncology

PURPOSE

With the recent rising interest in artificial intelligence (AI) in medicine, many studies have explored the potential and usefulness of AI in urological diseases. This study aimed to comprehensively review recent applications of AI in urologic oncology.

MATERIALS AND METHODS

We searched the PubMed-MEDLINE databases for articles in English on machine learning (ML) and deep learning (DL) models related to general surgery and prostate, bladder, and kidney cancer. The search terms were a combination of keywords, including both "urology" and "artificial intelligence" with one of the following: "machine learning," "deep learning," "neural network," "renal cell carcinoma," "kidney cancer," "urothelial carcinoma," "bladder cancer," "prostate cancer," and "robotic surgery."

RESULTS

A total of 58 articles were included. The studies on prostate cancer were related to grade prediction, improved diagnosis, and predicting outcomes and recurrence. The studies on bladder cancer mainly used radiomics to identify aggressive tumors and predict treatment outcomes, recurrence, and survival rates. Most studies on the application of ML and DL in kidney cancer were focused on the differentiation of benign and malignant tumors as well as prediction of their grade and subtype. Most studies suggested that methods using AI may be better than or similar to existing traditional methods.

CONCLUSIONS

AI technology is actively being investigated in the field of urological cancers as a tool for diagnosis, prediction of prognosis, and decision-making and is expected to be applied in additional clinical areas soon. Despite technological, legal, and ethical concerns, AI will change the landscape of urological cancer management.

Efficacy of artificial intelligence in reducing miss rates of GI adenomas, polyps, and sessile serrated lesions: a meta-analysis of randomized controlled trials

BACKGROUND AND AIMS

The aim of this study was to determine if utilization of artificial intelligence (AI) in the course of endoscopic procedures can significantly diminish both the adenoma miss rate (AMR) and the polyp miss rate (PMR) compared with standard endoscopy.

METHODS

We performed an extensive search of various databases, encompassing PubMed, Embase, Cochrane Library, Web of Science, and Scopus, until June 2023. The search terms used were artificial intelligence, machine learning, deep learning, transfer machine learning, computer-assisted diagnosis, convolutional neural networks, gastrointestinal (GI) endoscopy, endoscopic image analysis, polyp, adenoma, and neoplasms. The main study aim was to explore the impact of AI on the AMR, PMR, and sessile serrated lesion miss rate.

RESULTS

A total of 7 randomized controlled trials were included in this meta-analysis. Pooled AMR was markedly lower in the AI group versus the non-AI group (pooled relative risk [RR], .46; 95% confidence interval [CI], .36-.59; P < .001). PMR was also reduced in the AI group in contrast with the non-AI control (pooled RR, .43; 95% CI, .27-.69; P < .001). The results showed that AI decreased the miss rate of sessile serrated lesions (pooled RR, .43; 95% CI, .20 to .92; P < .05) and diminutive adenomas (pooled RR, .49; 95% CI, .26-.93) during endoscopy, but no significant effect was observed for advanced adenomas (pooled RR, .48; 95% CI, .17-1.37; P = .17). The average number of polyps (Hedges' g = -.486; 95% CI, -.697 to -.274; P = .000) and adenomas (Hedges' g = -.312; 95% CI, -.551 to -.074; P = .01) detected during the second procedure also favored AI. However, AI implementation did not lead to a prolonged withdrawal time (P > .05).

CONCLUSIONS

This meta-analysis suggests that AI technology leads to significant reduction of miss rates for GI adenomas, polyps, and sessile serrated lesions during endoscopic surveillance. These results underscore the potential of AI to improve the accuracy and efficiency of GI endoscopic procedures.

As how artificial intelligence is revolutionizing endoscopy

With incessant advances in information technology and its implications in all domains of our lives, artificial intelligence (AI) has emerged as a requirement for improved machine performance. This brings forth the query of how this can benefit endoscopists and improve both diagnostic and therapeutic endoscopy in each part of the gastrointestinal tract. Additionally, it also raises the question of the recent benefits and clinical usefulness of this new technology in daily endoscopic practice. There are two main categories of AI systems: computer-assisted detection (CADe) for lesion detection and computer-assisted diagnosis (CADx) for optical biopsy and lesion characterization. Quality assurance is the next step in the complete monitoring of high-quality colonoscopies. In all cases, computer-aided endoscopy is used, as the overall results rely on the physician. Video capsule endoscopy is a unique example in which a computer operates a device, stores multiple images, and performs an accurate diagnosis. While there are many expectations, we need to standardize and assess various software packages. It is important for healthcare providers to support this new development and make its use an obligation in daily clinical practice. In summary, AI represents a breakthrough in digestive endoscopy. Screening for gastric and colonic cancer detection should be improved, particularly outside expert centers. Prospective and multicenter trials are mandatory before introducing new software into clinical practice.

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.

Development and external validation of a transfer learning-based system for the pathological diagnosis of colorectal cancer: a large emulated prospective study

Background

The progress in Colorectal cancer (CRC) screening and management has resulted in an unprecedented caseload for histopathological diagnosis. While artificial intelligence (AI) presents a potential solution, the predominant emphasis on slide-level aggregation performance without thorough verification of cancer in each location, impedes both explainability and transparency. Effectively addressing these challenges is crucial to ensuring the reliability and efficacy of AI in histology applications.

Method

In this study, we created an innovative AI algorithm using transfer learning from a polyp segmentation model in endoscopy. The algorithm precisely localized CRC targets within 0.25 mm² grids from whole slide imaging (WSI). We assessed the CRC detection capabilities at this fine granularity and examined the influence of AI on the diagnostic behavior of pathologists. The evaluation utilized an extensive dataset comprising 858 consecutive patient cases with 1418 WSIs obtained from an external center.

Results

Our results underscore a notable sensitivity of 90.25% and specificity of 96.60% at the grid level, accompanied by a commendable area under the curve (AUC) of 0.962. This translates to an impressive 99.39% sensitivity at the slide level, coupled with a negative likelihood ratio of <0.01, signifying the dependability of the AI system to preclude diagnostic considerations. The positive likelihood ratio of 26.54, surpassing 10 at the grid level, underscores the imperative for meticulous scrutiny of any AI-generated highlights. Consequently, all four participating pathologists demonstrated statistically significant diagnostic improvements with AI assistance.

Conclusion

Our transfer learning approach has successfully yielded an algorithm that can be validated for CRC histological localizations in whole slide imaging. The outcome advocates for the integration of the AI system into histopathological diagnosis, serving either as a diagnostic exclusion application or a computer-aided detection (CADe) tool. This integration has the potential to alleviate the workload of pathologists and ultimately benefit patients.

Two-stage deep-learning-based colonoscopy polyp detection incorporating fisheye and reflection correction

BACKGROUND AND AIM

Colonoscopy is a useful method for the diagnosis and management of colorectal diseases. Many computer-aided systems have been developed to assist clinicians in detecting colorectal lesions by analyzing colonoscopy images. However, fisheye-lens distortion and light reflection in colonoscopy images can substantially affect the clarity of these images and their utility in detecting polyps. This study proposed a two-stage deep-learning model to correct distortion and reflections in colonoscopy images and thus facilitate polyp detection.

METHODS

Images were collected from the PolypSet dataset, the Kvasir-SEG dataset, and one medical center's patient archiving and communication system. The training, validation, and testing datasets comprised 808, 202, and 1100 images, respectively. The first stage involved the correction of fisheye-related distortion in colonoscopy images and polyp detection, which was performed using a convolutional neural network. The second stage involved the use of generative and adversarial networks for correcting reflective colonoscopy images before the convolutional neural network was used for polyp detection.

RESULTS

The model had higher accuracy when it was validated using corrected images than when it was validated using uncorrected images (96.8% vs 90.8%, P < 0.001). The model's accuracy in detecting polyps in the Kvasir-SEG dataset reached 96%, and the area under the receiver operating characteristic curve was 0.94.

CONCLUSION

The proposed model can facilitate the clinical diagnosis of colorectal polyps and improve the quality of colonoscopy.

Improvement of adenoma detection rate by two computer-aided colonic polyp detection systems in high adenoma detectors: a randomized multicenter trial

BACKGROUND

This study aimed to evaluate the benefits of a self-developed computer-aided polyp detection system (SD-CADe) and a commercial system (CM-CADe) for high adenoma detectors compared with white-light endoscopy (WLE) as a control.

METHODS

Average-risk 50-75-year-old individuals who underwent screening colonoscopy at five referral centers were randomized to SD-CADe, CM-CADe, or WLE groups (1:1:1 ratio). Trainees and staff with an adenoma detection rate (ADR) of ≥35% were recruited. The primary outcome was ADR. Secondary outcomes were the proximal adenoma detection rate (pADR), advanced adenoma detection rate (AADR), and the number of adenomas, proximal adenomas, and advanced adenomas per colonoscopy (APC, pAPC, and AAPC, respectively).

RESULTS

The study enrolled 1200 participants. The ADR in the control, CM-CADe, and SD-CADe groups was 38.3%, 50.0%, and 54.8%, respectively. The pADR was 23.0%, 32.3%, and 38.8%, respectively. AADR was 6.0%, 10.3%, and 9.5%, respectively. After adjustment, the ADR and pADR in both intervention groups were significantly higher than in controls (all P<0.05). The APC in the control, CM-CADe, and SD-CADe groups was 0.66, 1.04, and 1.16, respectively. The pAPC was 0.33, 0.53, and 0.64, respectively, and the AAPC was 0.07, 0.12, and 0.10, respectively. Both CADe systems showed significantly higher APC and pAPC than WLE. AADR and AAPC were improved in both CADe groups versus control, although the differences were not statistically significant.

CONCLUSION

Even in high adenoma detectors, CADe significantly improved ADR and APC. The AADR tended to be higher with both systems, and this may enhance colorectal cancer prevention.

Preliminary Evidence of the Use of Generative AI in Health Care Clinical Services: Systematic Narrative Review

BACKGROUND

Generative artificial intelligence tools and applications (GenAI) are being increasingly used in health care. Physicians, specialists, and other providers have started primarily using GenAI as an aid or tool to gather knowledge, provide information, train, or generate suggestive dialogue between physicians and patients or between physicians and patients' families or friends. However, unless the use of GenAI is oriented to be helpful in clinical service encounters that can improve the accuracy of diagnosis, treatment, and patient outcomes, the expected potential will not be achieved. As adoption continues, it is essential to validate the effectiveness of the infusion of GenAI as an intelligent technology in service encounters to understand the gap in actual clinical service use of GenAI.

OBJECTIVE

This study synthesizes preliminary evidence on how GenAI assists, guides, and automates clinical service rendering and encounters in health care The review scope was limited to articles published in peer-reviewed medical journals.

METHODS

We screened and selected 0.38% (161/42,459) of articles published between January 1, 2020, and May 31, 2023, identified from PubMed. We followed the protocols outlined in the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines to select highly relevant studies with at least 1 element on clinical use, evaluation, and validation to provide evidence of GenAI use in clinical services. The articles were classified based on their relevance to clinical service functions or activities using the descriptive and analytical information presented in the articles.

RESULTS

Of 161 articles, 141 (87.6%) reported using GenAI to assist services through knowledge access, collation, and filtering. GenAI was used for disease detection (19/161, 11.8%), diagnosis (14/161, 8.7%), and screening processes (12/161, 7.5%) in the areas of radiology (17/161, 10.6%), cardiology (12/161, 7.5%), gastrointestinal medicine (4/161, 2.5%), and diabetes (6/161, 3.7%). The literature synthesis in this study suggests that GenAI is mainly used for diagnostic processes, improvement of diagnosis accuracy, and screening and diagnostic purposes using knowledge access. Although this solves the problem of knowledge access and may improve diagnostic accuracy, it is oriented toward higher value creation in health care.

CONCLUSIONS

GenAI informs rather than assisting or automating clinical service functions in health care. There is potential in clinical service, but it has yet to be actualized for GenAI. More clinical service-level evidence that GenAI is used to streamline some functions or provides more automated help than only information retrieval is needed. To transform health care as purported, more studies related to GenAI applications must automate and guide human-performed services and keep up with the optimism that forward-thinking health care organizations will take advantage of GenAI.

A colonial serrated polyp classification model using white-light ordinary endoscopy images with an artificial intelligence model and TensorFlow chart

In this study, we implemented a combination of data augmentation and artificial intelligence (AI) model-Convolutional Neural Network (CNN)-to help physicians classify colonic polyps into traditional adenoma (TA), sessile serrated adenoma (SSA), and hyperplastic polyp (HP). We collected ordinary endoscopy images under both white and NBI lights. Under white light, we collected 257 images of HP, 423 images of SSA, and 60 images of TA. Under NBI light, were collected 238 images of HP, 284 images of SSA, and 71 images of TA. We implemented the CNN-based artificial intelligence model, Inception V4, to build a classification model for the types of colon polyps. Our final AI classification model with data augmentation process is constructed only with white light images. Our classification prediction accuracy of colon polyp type is 94%, and the discriminability of the model (area under the curve) was 98%. Thus, we can conclude that our model can help physicians distinguish between TA, SSA, and HPs and correctly identify precancerous lesions such as TA and SSA.

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).

Artificial Intelligence in the Diagnosis of Colorectal Cancer: A Literature Review

BACKGROUND

The aim of this review is to explore the role of artificial intelligence in the diagnosis of colorectal cancer, how it impacts CRC morbidity and mortality, and why its role in clinical medicine is limited.

METHODS

A targeted, non-systematic review of the published literature relating to colorectal cancer diagnosis was performed with PubMed databases that were scouted to help provide a more defined understanding of the recent advances regarding artificial intelligence and their impact on colorectal-related morbidity and mortality. Articles were included if deemed relevant and including information associated with the keywords.

RESULTS

The advancements in artificial intelligence have been significant in facilitating an earlier diagnosis of CRC. In this review, we focused on evaluating genomic biomarkers, the integration of instruments with artificial intelligence, MR and hyperspectral imaging, and the architecture of neural networks. We found that these neural networks seem practical and yield positive results in initial testing. Furthermore, we explored the use of deep-learning-based majority voting methods, such as bag of words and PAHLI, in improving diagnostic accuracy in colorectal cancer detection. Alongside this, the autonomous and expansive learning ability of artificial intelligence, coupled with its ability to extract increasingly complex features from images or videos without human reliance, highlight its impact in the diagnostic sector. Despite this, as most of the research involves a small sample of patients, a diversification of patient data is needed to enhance cohort stratification for a more sensitive and specific neural model. We also examined the successful application of artificial intelligence in predicting microsatellite instability, showcasing its potential in stratifying patients for targeted therapies.

CONCLUSIONS

Since its commencement in colorectal cancer, artificial intelligence has revealed a multitude of functionalities and augmentations in the diagnostic sector of CRC. Given its early implementation, its clinical application remains a fair way away, but with steady research dedicated to improving neural architecture and expanding its applicational range, there is hope that these advanced neural software could directly impact the early diagnosis of CRC. The true promise of artificial intelligence, extending beyond the medical sector, lies in its potential to significantly influence the future landscape of CRC's morbidity and mortality.

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.