Artificial intelligence-based assessments of colonoscopic withdrawal technique: a new method for measuring and enhancing the quality of fold examination

Artificial intelligence alert system based on intraluminal view for colonoscopy intubation

Mucosal contact of the tip of colonoscopy causes red-out views, and more pressure may result in perforation. There is still a lack of quantitative analysis methods for red-out views. We aimed to develop an artificial intelligence (AI)-based system to assess red-out views during intubation in colonoscopy. Altogether, 479 colonoscopies performed by 34 colonoscopists were analysed using the proposed semi-supervised AI-based system. We compared the AI-based red-out avoiding scores among novice, intermediate, and experienced colonoscopists. The mean AI-based red-out avoiding scores were compared among groups stratified by expert-rated direct observation of procedure or skill (DOPS)-based tip control assessment results. Both the percentage of actual red-out views (p < 0.001) and AI-based red-out avoiding scores (p < 0.001) were significantly different among the novice, intermediate, and experienced groups. Colonoscopists who scored better on the DOPS-based tip control assessment also performed better on the AI-based red-out avoiding skill assessment. AI-based red-out avoiding score was negatively correlated with actual caecal intubation time and actual red-out percentage. Feedback of red-out avoiding score may help remind endoscopists to perform colonoscopy in an effective and safe manner. This system can be used as an auxiliary tool for colonoscopy training.

Artificial Intelligence in Endoscopy Quality Measures

Quality of gastrointestinal endoscopy is a major determinant of its effectiveness. Artificial intelligence (AI) has the potential to enhance quality monitoring and improve endoscopy outcomes. This article reviews the current literature on AI algorithms that have been developed for endoscopy quality assessment.

Deep Learning-Based Semantic Segmentation for Objective Colonoscopy Quality Assessment

Background: This study aims to objectively evaluate the overall quality of colonoscopies using a specially trained deep learning-based semantic segmentation neural network. This represents a modern and valuable approach for the analysis of colonoscopy frames. Methods: We collected thousands of colonoscopy frames extracted from a set of video colonoscopy files. A color-based image processing method was used to extract color features from specific regions of each colonoscopy frame, namely, the intestinal mucosa, residues, artifacts, and lumen. With these features, we automatically annotated all the colonoscopy frames and then selected the best of them to train a semantic segmentation network. This trained network was used to classify the four region types in a different set of test colonoscopy frames and extract pixel statistics that are relevant to quality evaluation. The test colonoscopies were also evaluated by colonoscopy experts using the Boston scale. Results: The deep learning semantic segmentation method obtained good results, in terms of classifying the four key regions in colonoscopy frames, and produced pixel statistics that are efficient in terms of objective quality assessment. The Spearman correlation results were as follows: BBPS vs. pixel scores: 0.69; BBPS vs. mucosa pixel percentage: 0.63; BBPS vs. residue pixel percentage: -0.47; BBPS vs. Artifact Pixel Percentage: -0.65. The agreement analysis using Cohen's Kappa yielded a value of 0.28. The colonoscopy evaluation based on the extracted pixel statistics showed a fair level of compatibility with the experts' evaluations. Conclusions: Our proposed deep learning semantic segmentation approach is shown to be a promising tool for evaluating the overall quality of colonoscopies and goes beyond the Boston Bowel Preparation Scale in terms of assessing colonoscopy quality. In particular, while the Boston scale focuses solely on the amount of residual content, our method can identify and quantify the percentage of colonic mucosa, residues, and artifacts, providing a more comprehensive and objective evaluation.

Explaining the Correlates of the Multi-Theory Model (MTM) of Health Behavior Change in Visual (Structural) Colorectal Cancer Screening Examinations

Colorectal cancer (CRC) ranks third in terms of global cancer prevalence and is the second most common cause of cancer-related mortality. Although CRC rates are decreasing in the United States, inequalities still exist despite the effectiveness of invasive screening methods, such as colonoscopy, flexible sigmoidoscopy, and computed tomography (CT) colonography in detecting colorectal cancer. Many current interventions promoting CRC screening do not utilize a modern theory-based approach, which has led to the low utilization of these screening methods. This cross-sectional study aims to address the lack of theory-based treatments for promoting visual CRC screening examinations by applying the multi-theory model (MTM) of health behavior change to explicate the health-related factors for individuals to seek visual colorectal cancer screening examinations for CRC screening. A 57-item validated questionnaire assessing MTM constructs and CRC screening was administered online. The survey questionnaire was administered to a sample of 640 adults from the United States. The participants were between the ages of 45 and 75 years. Hierarchical multiple regression was used to assess the relationship between MTM constructs with the initiation and sustenance of CRC screening behaviors. Out of the total participants in this nationwide sample, 71.4% (n = 457) reported that they had undergone a visual CRC screening examination. MTM subscales, specifically participatory dialogue, changes in the physical environment along with age, recommendation for CRC screening from a healthcare provider, and previous experience with colonoscopy, were found to be significant factors in predicting the initiation of visual CRC screening behavior. These factors accounted for 22% of the variation in initiation among this group (R2 = 0.222, F = 3.521, p < 0.001). The MTM can be a valuable framework for designing educational media, information media, social media platforms, and clinical interventions to promote visual colorectal cancer screening examinations.

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.

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.

Current Status of Artificial Intelligence Use in Colonoscopy

BACKGROUND

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

SUMMARY

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

KEY MESSAGES

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

Artificial Intelligence in Gastrointestinal Endoscopy

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

Artificial Intelligence 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.

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.

Computer-aided quality assessment of endoscopist competence during colonoscopy: a systematic review

BACKGROUND AND AIMS

Endoscopists' competence can vary widely, as shown in the variation in the adenoma detection rate (ADR). Computer-aided quality assessment (CAQ) can automatically assess performance during individual procedures. In this review we identified and described different CAQ systems for colonoscopy.

METHODS

A systematic review of the literature was done using MEDLINE, EMBASE, and Scopus based on 3 blocks of terms according to the inclusion criteria: colonoscopy, competence assessment, and automatic evaluation. Articles were systematically reviewed by 2 reviewers, first by abstract and then in full text. The methodological quality was assessed using the Medical Education Research Study Quality Instrument (MERSQI).

RESULTS

Of 12,575 identified studies, 6831 remained after removal of duplicates and 6806 did not pass the eligibility criteria and were excluded, leaving 25 studies, of which 13 studies were included in the final analysis. Five categories of CAQ systems were identified: withdrawal speedometer (7 studies), endoscope movement analysis (3 studies), effective withdrawal time (1 study), fold examination quality (1 study), and visual gaze pattern (1 study). The withdrawal speedometer was the only CAQ system that tested its feedback by examining changes in ADR. Three studies observed an improvement in ADR, and 2 studies did not. The methodological quality of the studies was high (mean MERSQI, 15.2 points; maximum, 18 points).

CONCLUSIONS

Thirteen studies developed or tested CAQ systems, most frequently by correlating it to the ADR. Only 5 studies tested feedback by implementing the CAQ system. A meta-analysis was impossible because of the heterogeneous study designs, and more studies are warranted.

G-EYE Improves Polyp, Adenoma, and Serrated Polyp Detection Rates in Colonoscopy: A Systematic Review and Meta-analysis

BACKGROUND

Colonoscopy is the gold-standard test to decrease mortality from colorectal cancer (CRC). G-EYE is an inflated balloon on the bending section of the scope with the ability to flatten the folds to improve the adenoma detection rate (ADR). We performed this meta-analysis to evaluate the efficacy of G-EYE in improving ADR and other quality indicators of colonoscopy.

METHODS

A literature search was performed through March 21, 2023, on databases including Embase, Medline, Cochrane Central Register of Controlled Trials, Web of Science Core Collection, KCI-Korean Journal Index, and Global Index Medicus. Core concepts of G-EYE, colonoscopy, ADR, polyp detection rate (PDR), serrated polyp detection rate (SPDR), and withdrawal time were searched. Statistical analysis was performed with OpenMeta[Analyst]. The odds ratio (OR) for the proportional variable and mean difference for the continuous variable along with 95% CI was used with a P-value <0.05 considered statistically significant. We used the DerSimonian-Laird method and random effects model for pooling data.

RESULTS

The search strategy yielded a total of 143 articles. Three studies with 3868 total colonoscopies were finalized. The pooled ADR, PDR, and SPDR showed statistical improvement with G-EYE (OR: 1.744, 95% CI: 1.534-1.984, P<0.001; OR: 1.766, 95% CI: 1.547-2.015, P<0.001; and OR: 1.603, 95% CI: 1.176-2.185, P=0.003). The withdrawal time was also noted to be higher in the G-EYE group (mean difference: 0.114, CI: 0.041-0.186, P=0.002).

CONCLUSIONS

This meta-analysis suggests that G-EYE can improve ADR, PDR, and SPDR. Further studies are needed to evaluate the effect of G-EYE on interval CRC and mortality rate.

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.

Artificial intelligence-assisted real-time monitoring of effective withdrawal time during colonoscopy: a novel quality marker of colonoscopy

BACKGROUND AND AIMS

The importance of withdrawal time during colonoscopy cannot be overstated in mitigating the risk of missed lesions and postcolonoscopy colorectal cancer. We evaluated a novel colonoscopy quality metric called the effective withdrawal time (EWT), which is an artificial intelligence (AI)-derived quantitative measure of quality withdrawal time, and its association with various colonic lesion detection rates as compared with standard withdrawal time (SWT).

METHODS

Three hundred fifty video recordings of colonoscopy withdrawal (from the cecum to the anus) were assessed by the new AI model. The primary outcome was adenoma detection rate (ADR) according to different quintiles of EWT. Multivariate logistic regression, adjusting for baseline covariates, was used to determine the adjusted odd ratios (ORs) for EWT on lesion detection rates, with the lowest quintile as reference. The area under the receiver-operating characteristic curve of EWT was compared with SWT.

RESULTS

The crude ADR in different quintiles of EWT, from lowest to highest, was 10.0%, 31.4%, 33.3%, 53.5%, and 85.7%. The ORs of detecting adenomas and polyps were significantly higher in all top 4 quintiles when compared with the lowest quintile. Each minute increase in EWT was associated with a 49% increase in ADR (aOR, 1.49; 95% confidence interval [CI], 1.36-1.65). The area under the receiver-operating characteristic curve of EWT was also significantly higher than SWT on adenoma detection (.80 [95% CI, .75-.84] vs .70 [95% CI, .64-.74], P < .01).

CONCLUSIONS

AI-derived monitoring of EWT is a promising novel quality indicator for colonoscopy, which is more associated with ADR than SWT.

Assisted documentation as a new focus for artificial intelligence in endoscopy: the precedent of reliable withdrawal time and image reporting

BACKGROUND : Reliable documentation is essential for maintaining quality standards in endoscopy; however, in clinical practice, report quality varies. We developed an artificial intelligence (AI)-based prototype for the measurement of withdrawal and intervention times, and automatic photodocumentation. METHOD:  A multiclass deep learning algorithm distinguishing different endoscopic image content was trained with 10 557 images (1300 examinations, nine centers, four processors). Consecutively, the algorithm was used to calculate withdrawal time (AI prediction) and extract relevant images. Validation was performed on 100 colonoscopy videos (five centers). The reported and AI-predicted withdrawal times were compared with video-based measurement; photodocumentation was compared for documented polypectomies. RESULTS:  Video-based measurement in 100 colonoscopies revealed a median absolute difference of 2.0 minutes between the measured and reported withdrawal times, compared with 0.4 minutes for AI predictions. The original photodocumentation represented the cecum in 88 examinations compared with 98/100 examinations for the AI-generated documentation. For 39/104 polypectomies, the examiners' photographs included the instrument, compared with 68 for the AI images. Lastly, we demonstrated real-time capability (10 colonoscopies). CONCLUSION : Our AI system calculates withdrawal time, provides an image report, and is real-time ready. After further validation, the system may improve standardized reporting, while decreasing the workload created by routine documentation.

AGA Clinical Practice Update on the Role of Artificial Intelligence in Colon Polyp Diagnosis and Management: Commentary

DESCRIPTION

The purpose of this American Gastroenterological Association (AGA) Institute Clinical Practice Update (CPU) is to review the available evidence and provide expert commentary on the current landscape of artificial intelligence in the evaluation and management of colorectal polyps.

METHODS

This CPU was commissioned and approved by the AGA Institute Clinical Practice Updates Committee (CPUC) and the AGA Governing Board to provide timely guidance on a topic of high clinical importance to the AGA membership and underwent internal peer review by the CPUC and external peer review through standard procedures of Gastroenterology. This Expert Commentary incorporates important as well as recently published studies in this field, and it reflects the experiences of the authors who are experienced endoscopists with expertise in the field of artificial intelligence and colorectal polyps.

Artificial intelligence in endoscopy: Overview, applications, and future directions

Since the emergence of artificial intelligence (AI) in medicine, endoscopy applications in gastroenterology have been at the forefront of innovations. The ever-increasing number of studies necessitates the need to organize and classify applications in a useful way. Separating AI capabilities by computer aided detection (CADe), diagnosis (CADx), and quality assessment (CADq) allows for a systematic evaluation of each application. CADe studies have shown promise in accurate detection of esophageal, gastric and colonic neoplasia as well as identifying sources of bleeding and Crohn's disease in the small bowel. While more advanced CADx applications employ optical biopsies to give further information to characterize neoplasia and grade inflammatory disease, diverse CADq applications ensure quality and increase the efficiency of procedures. Future applications show promise in advanced therapeutic modalities and integrated systems that provide multimodal capabilities. AI is set to revolutionize clinical decision making and performance of endoscopy.

Artificial intelligence in digestive endoscopy: recent advances

PURPOSE OF REVIEW

With the incessant advances in information technology and its implications in all domains of our life, artificial intelligence (AI) started to emerge as a need for better machine performance. How it can help endoscopists and what are the areas of interest in improving both diagnostic and therapeutic endoscopy in each part of the gastrointestinal (GI) tract. What are the recent benefits and clinical usefulness of this new technology in daily endoscopic practice.

RECENT FINDINGS

The two main AI systems categories are computer-assisted detection 'CADe' for lesion detection and computer-assisted diagnosis 'CADx' for optical biopsy and lesion characterization. Multiple softwares are now implemented in endoscopy practice. Other AI systems offer therapeutic assistance such as lesion delineation for complete endoscopic resection or prediction of possible lymphanode after endoscopic treatment. Quality assurance is the coming step with complete monitoring of high-quality colonoscopy. In all cases it is a computer-aid endoscopy as the overall result rely on the physician. Video capsule endoscopy is the unique example were the computer conduct the device, store multiple images, and perform accurate diagnosis.

SUMMARY

AI is a breakthrough in digestive endoscopy. Screening gastric and colonic cancer detection should be improved especially outside of expert's centers. Prospective and multicenter trials are mandatory before introducing new software in clinical practice.

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

INTRODUCTION

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

AREAS COVERED

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

EXPERT OPINION

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

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

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

Current and future implications of artificial intelligence in colonoscopy

Gastrointestinal endoscopy has proved to be a perfect context for the development of artificial intelligence (AI) systems that can aid endoscopists in many tasks of their daily activities. Lesion detection (computer-aided detection, CADe) and lesion characterization (computer-aided characterization, CADx) during colonoscopy are the clinical applications of AI in gastroenterology for which by far the most evidence has been published. Indeed, they are the only applications for which more than one system has been developed by different companies, is currently available on the market, and may be used in clinical practice. Both CADe and CADx, alongside hopes and hypes, come with potential drawbacks, limitations and dangers that must be known, studied and researched as much as the optimal uses of these machines, aiming to stay one step ahead of the possible misuse of what will always be an aid to the clinician and never a substitute. An AI revolution in colonoscopy is on the way, but the potential uses are infinite and only a fraction of them have currently been studied. Future applications can be designed to ensure all aspects of colonoscopy quality parameters and truly deliver a standardization of practice, regardless of the setting in which the procedure is performed. In this review, we cover the available clinical evidence on AI applications in colonoscopy and offer an overview of future directions.

Impact of withdrawal time on adenoma detection rate: results from a prospective multicenter trial

BACKGROUND AND AIMS

Performing a high-quality colonoscopy is critical for optimizing the adenoma detection rate (ADR). Colonoscopy withdrawal time (a surrogate measure) of ≥6 minutes is recommended; however, a threshold of a high-quality withdrawal and its impact on ADR are not known.

METHODS

We examined withdrawal time (excluding polyp resection and bowel cleaning time) of subjects undergoing screening and/or surveillance colonoscopy in a prospective, multicenter, randomized controlled trial. We examined the relationship of withdrawal time in 1-minute increments on ADR and reported odds ratio (OR) with 95% confidence intervals. Linear regression analysis was performed to assess the maximal inspection time threshold that impacts the ADR.

RESULTS

A total of 1142 subjects (age, 62.3 ± 8.9 years; 80.5% men) underwent screening (45.9%) or surveillance (53.6%) colonoscopy. The screening group had a median withdrawal time of 9.0 minutes (interquartile range [IQR], 3.3) with an ADR of 49.6%, whereas the surveillance group had a median withdrawal time of 9.3 minutes (IQR, 4.3) with an ADR of 63.9%. ADR correspondingly increased for a withdrawal time of 6 minutes to 13 minutes, beyond which ADR did not increase (50.4% vs 76.6%, P < .01). For every 1-minute increase in withdrawal time, there was 6% higher odds of detecting an additional subject with an adenoma (OR, 1.06; 95% confidence interval, 1.02-1.10; P = .004).

CONCLUSIONS

Results from this multicenter, randomized controlled trial underscore the importance of a high-quality examination and efforts required to achieve this with an incremental yield in ADR based on withdrawal time. (Clinical trial registration number: NCT03952611.).

Key quality indicators in colonoscopy

Many quality indicators have been proposed for colonoscopy, but most colonoscopists and endoscopy groups focus on measuring the adenoma detection rate and the cecal intubation rate. Use of proper screening and surveillance intervals is another accepted key indicator but it is seldom evaluated in clinical practice. Bowel preparation efficacy and polyp resection skills are areas that are emerging as potential key or priority indicators. This review summarizes and provides an update on key performance indicators for colonoscopy quality.