Uptake and barriers for implementation of the resect and discard strategy: an international survey

High risk features in colorectal adenomatous polyps: A multi-institutional study

High risk features in colorectal adenomatous polyps include size >1 cm and advanced histology: high-grade dysplasia and villous architecture. We investigated whether the diagnostic rates of advanced histology in colorectal adenomatous polyps were similar among institutions across the United States, and if not, could differences be explained by patient age, polyp size, and/or CRC rate. Nine academic institutions contributed data from three pathologists who had signed out at least 100 colorectal adenomatous polyps each from 2018 to 2019 taken from patients undergoing screening colonoscopy. For each case, we recorded patient age and sex, polyp size and location, concurrent CRC, and presence or absence of HGD and villous features. A total of 2700 polyps from 1886 patients (mean age: 61 years) were collected. One hundred twenty-four (5 %) of the 2700 polyps had advanced histology, including 35 (1 %) with HGD and 101 (4 %) with villous features. The diagnostic rate of advanced histology varied by institution from 1.7 % to 9.3 % (median: 4.3 %, standard deviation [SD]: 2.5 %). The rate of HGD ranged from 0 % to 3.3 % (median: 1 %, SD: 1.2 %), while the rate of villous architecture varied from 1 % to 8 % (median: 3.7 %, SD: 2.5 %). In a multivariate analysis, the factor most strongly associated with advanced histology was polyp size >1 cm with an odds ratio (OR) of 31.82 (95 % confidence interval [CI]: 20.52-50.25, p < 0.05). Inter-institutional differences in the rate of polyps >1 cm likely explain some of the diagnostic variance, but pathologic subjectivity may be another contributing factor.

Autonomous Artificial Intelligence vs Artificial Intelligence-Assisted Human Optical Diagnosis of Colorectal Polyps: A Randomized Controlled Trial

BACKGROUND & AIMS

Artificial intelligence (AI)-based optical diagnosis systems (CADx) have been developed to allow pathology prediction of colorectal polyps during colonoscopies. However, CADx systems have not yet been validated for autonomous performance. Therefore, we conducted a trial comparing autonomous AI to AI-assisted human (AI-H) optical diagnosis.

METHODS

We performed a randomized noninferiority trial of patients undergoing elective colonoscopies at 1 academic institution. Patients were randomized into (1) autonomous AI-based CADx optical diagnosis of diminutive polyps without human input or (2) diagnosis by endoscopists who performed optical diagnosis of diminutive polyps after seeing the real-time CADx diagnosis. The primary outcome was accuracy in optical diagnosis in both arms using pathology as the gold standard. Secondary outcomes included agreement with pathology for surveillance intervals.

RESULTS

A total of 467 patients were randomized (238 patients/158 polyps in the autonomous AI group and 229 patients/179 polyps in the AI-H group). Accuracy for optical diagnosis was 77.2% (95% confidence interval [CI], 69.7-84.7) in the autonomous AI group and 72.1% (95% CI, 65.5-78.6) in the AI-H group (P = .86). For high-confidence diagnoses, accuracy for optical diagnosis was 77.2% (95% CI, 69.7-84.7) in the autonomous AI group and 75.5% (95% CI, 67.9-82.0) in the AI-H group. Autonomous AI had statistically significantly higher agreement with pathology-based surveillance intervals compared to AI-H (91.5% [95% CI, 86.9-96.1] vs 82.1% [95% CI, 76.5-87.7]; P = .016).

CONCLUSIONS

Autonomous AI-based optical diagnosis exhibits noninferior accuracy to endoscopist-based diagnosis. Both autonomous AI and AI-H exhibited relatively low accuracy for optical diagnosis; however, autonomous AI achieved higher agreement with pathology-based surveillance intervals. (ClinicalTrials.gov, Number NCT05236790).

White light computer-aided optical diagnosis of diminutive colorectal polyps in routine clinical practice

Background and study aims Artificial Intelligence (AI) systems could make the optical diagnosis (OD) of diminutive colorectal polyps (DCPs) more reliable and objective. This study was aimed at prospectively evaluating feasibility and diagnostic performance of AI-standalone and AI-assisted OD of DCPs in a real-life setting by using a white light-based system (GI Genius, Medtronic Co, Minneapolis, Minnesota, United States). Patients and methods Consecutive colonoscopy outpatients with at least one DCP were evaluated by 11 endoscopists (5 experts and 6 non-experts in OD). DCPs were classified in real time by AI (AI-standalone OD) and by the endoscopist with the assistance of AI (AI-assisted OD), with histopathology as the reference standard. Results Of the 480 DCPs, AI provided the outcome "adenoma" or "non-adenoma" in 81.4% (95% confidence interval [CI]: 77.5-84.6). Sensitivity, specificity, positive and negative predictive value, and accuracy of AI-standalone OD were 97.0% (95% CI 94.0-98.6), 38.1% (95% CI 28.9-48.1), 80.1% (95% CI 75.2-84.2), 83.3% (95% CI 69.2-92.0), and 80.5% (95% CI 68.7-82.8%), respectively. Compared with AI-standalone, the specificity of AI-assisted OD was significantly higher (58.9%, 95% CI 49.7-67.5) and a trend toward an increase was observed for other diagnostic performance measures. Overall accuracy and negative predictive value of AI-assisted OD for experts and non-experts were 85.8% (95% CI 80.0-90.4) vs. 80.1% (95% CI 73.6-85.6) and 89.1% (95% CI 75.6-95.9) vs. 80.0% (95% CI 63.9-90.4), respectively. Conclusions Standalone AI is able to provide an OD of adenoma/non-adenoma in more than 80% of DCPs, with a high sensitivity but low specificity. The human-machine interaction improved diagnostic performance, especially when experts were involved.

Evaluating the Efficacy of Resect-and-Discard and Resect-and-Retrieve Strategies for Diminutive Colonic Polyps

BACKGROUND AND OBJECTIVES

Diminutive polyps present a unique challenge in colorectal cancer (CRC) prevention strategies. This study aims to assess the characteristics and variables of diminutive polyps in a Romanian cohort, intending to develop a combined resect-and-retrieve or resect-and-discard strategy that reduces the need for an optical diagnosis.

MATERIALS AND METHODS

A prospective cohort study was conducted at two endoscopy centers in Romania from July to December 2021. Adult patients undergoing colonoscopies where polyps were identified and resected were included. Endoscopic procedures employed advanced diagnostic features, including blue-light imaging (BLI) and narrow-band imaging (NBI). Logistic regression analysis was utilized to determine factors impacting the probability of adenomatous polyps with high-grade dysplasia (HGD).

RESULTS

A total of 427 patients were included, with a mean age of 59.42 years (±11.19), predominantly male (60.2%). The most common indication for a colonoscopy was lower gastrointestinal symptoms (42.6%), followed by screening (28.8%). Adequate bowel preparation was achieved in 87.8% of cases. The logistic regression analysis revealed significant predictors of HGD in adenomatous polyps: age (OR = 1.05, 95% CI: 1.01-1.08, p = 0.01) and polyp size (>5 mm vs. ≤5 mm, OR = 4.4, 95% CI: 1.94-10.06, p < 0.001). Polyps classified as Paris IIa, Ip, and Isp were significantly more likely to harbor HGD compared to the reference group (Is), with odds ratios of 6.05, 3.68, and 2.7, respectively.

CONCLUSIONS

The study elucidates significant associations between the presence of HGD in adenomatous polyps and factors such as age, polyp size, and Paris classification. These findings support the feasibility of a tailored approach in the resect-and-discard and resect-and-retrieve strategies for diminutive polyps, potentially optimizing CRC prevention and intervention practices. Further research is warranted to validate these strategies in broader clinical settings.

Diagnostic performance of endocytoscopy with normal pit-like structure sign for colorectal low-grade adenoma compared with conventional modalities

Objectives

A "resect-and-discard" strategy has been proposed for diminutive adenomas in the colorectum. However, this strategy is sometimes difficult to implement because of the lack of confidence in differentiating low-grade adenoma (LGA) from advanced lesions such as high-grade adenoma or carcinoma. To perform real-time precise diagnosis of LGA with high confidence, we assessed whether endocytoscopy (EC) diagnosis, considering normal pit-like structure (NP-sign), an excellent indicator of LGA, could have additional diagnostic potential compared with conventional modalities.

Methods

All the neoplastic lesions that were observed by non-magnifying narrow-band imaging (NBI), magnifying NBI (M-NBI), magnifying pit pattern, and EC prior to pathological examination between 2005 and 2018 were retrospectively investigated. The neoplastic lesions were classified into two categories: LGA and other neoplastic lesions. We assessed the differential diagnostic ability of EC with NP-sign between LGA and other neoplastic lesions compared with that of NBI, M-NBI, pit pattern, and conventional EC in terms of sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve (AUC).

Results

A total of 1376 lesions from 1097 patients were eligible. The specificity (94.9%), accuracy (91.5%), and area under the receiver operating characteristic curve (0.95) of EC with NP-sign were significantly higher than those of NBI, M-NBI, pit pattern, and conventional EC.

Conclusions

EC diagnosis with NP-sign has significantly higher diagnostic performance for predicting colorectal LGA compared with the conventional modalities and enables stratification of neoplastic lesions for "resect-and-discard" with higher confidence.

Artificial intelligence for characterization of diminutive colorectal polyps: A feasibility study comparing two computer-aided diagnosis systems

BACKGROUND

Artificial intelligence (AI) has potential in the optical diagnosis of colorectal polyps.

AIM

To evaluate the feasibility of the real-time use of the computer-aided diagnosis system (CADx) AI for ColoRectal Polyps (AI4CRP) for the optical diagnosis of diminutive colorectal polyps and to compare the performance with CAD EYE^TM (Fujifilm, Tokyo, Japan). CADx influence on the optical diagnosis of an expert endoscopist was also investigated.

METHODS

AI4CRP was developed in-house and CAD EYE was proprietary software provided by Fujifilm. Both CADx-systems exploit convolutional neural networks. Colorectal polyps were characterized as benign or premalignant and histopathology was used as gold standard. AI4CRP provided an objective assessment of its characterization by presenting a calibrated confidence characterization value (range 0.0-1.0). A predefined cut-off value of 0.6 was set with values < 0.6 indicating benign and values ≥ 0.6 indicating premalignant colorectal polyps. Low confidence characterizations were defined as values 40% around the cut-off value of 0.6 (< 0.36 and > 0.76). Self-critical AI4CRP’s diagnostic performances excluded low confidence characterizations.

RESULTS

AI4CRP use was feasible and performed on 30 patients with 51 colorectal polyps. Self-critical AI4CRP, excluding 14 low confidence characterizations [27.5% (14/51)], had a diagnostic accuracy of 89.2%, sensitivity of 89.7%, and specificity of 87.5%, which was higher compared to AI4CRP. CAD EYE had a 83.7% diagnostic accuracy, 74.2% sensitivity, and 100.0% specificity. Diagnostic performances of the endoscopist alone (before AI) increased non-significantly after reviewing the CADx characterizations of both AI4CRP and CAD EYE (AI-assisted endoscopist). Diagnostic performances of the AI-assisted endoscopist were higher compared to both CADx-systems, except for specificity for which CAD EYE performed best.

CONCLUSION

Real-time use of AI4CRP was feasible. Objective confidence values provided by a CADx is novel and self-critical AI4CRP showed higher diagnostic performances compared to AI4CRP.

Accuracy in Polyp Size Measurement Among Surgeons, Gastroenterologists, Trainees, and Experts: A Prospective Video-Based Study

INTRODUCTION

Polyp size determination plays an important role in endoscopic decision making and follow-up determination. However, there is a lack of knowledge of endoscopist accuracy for polyp sizing and efficacy of available tools for size measurement. Our aim was to compare the accuracy of visual assessment, snare, forceps, and virtual scale endoscope (VSE) in estimating polyp size among a diverse group of endoscopists.

METHODS

We conducted a prospective video-based study. One hundred twenty polyps measured and recorded along with all available measurement tools were randomized to visual assessment, snare, forceps, or VSE group. Eleven endoscopists conducted video-based measurement using the randomized measurement tool. Primary outcome was relative accuracy in polyp size measurement compared with caliper measurement immediately postresection.

RESULTS

One thousand three hundred twenty measurements were performed. VSE had statistically significantly higher relative accuracy when compared to forceps (79.3 vs 71.3%; P < 0.0001). Forceps had statistically significantly higher relative accuracy when compared to visual assessment (71.3 vs 63.6%; P = 0.0036). There was no statistically significant difference when comparing visual assessment and snare-based measurements (63.6 vs 62.8%; P = 0.797). Overall, 21.5% of polyps >5 mm were misclassified as ≤5 mm and 17.3% of polyps ≥10 mm were misclassified as <10 mm. VSE had the lowest percentage of polyps >5 mm misclassified as ≤5 mm (2.6%), polyps ≤5 mm misclassified as >5 mm (5.1%), and polyps <10 mm misclassified as ≥10 mm (1.7%).

DISCUSSION

Visual size estimation of polyps is inaccurate independently of training level, sex, and specialty. Size measurement accuracy can be improved using forceps and yields the highest relative accuracy when an adaptive scale technology is used.

Computer-aided polyp characterization in colonoscopy: sufficient performance or not?

Computer-assisted polyp characterization (computer-aided diagnosis, CADx) facilitates optical diagnosis during colonoscopy. Several studies have demonstrated high sensitivity and specificity of CADx tools in identifying neoplastic changes in colorectal polyps. To implement CADx tools in colonoscopy, there is a need to confirm whether these tools satisfy the threshold levels that are required to introduce optical diagnosis strategies such as "diagnose-and-leave," "resect-and-discard" or "DISCARD-lite." In this article, we review the available data from prospective trials regarding the effect of multiple CADx tools and discuss whether they meet these thresholds.

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

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

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.

The effectiveness of real-time computer-aided and quality control systems in colorectal adenoma and polyp detection during colonoscopies: a meta-analysis

This meta-analysis aims to quantify the effectiveness of artificial intelligence (AI)-supported colonoscopy compared to standard colonoscopy in adenoma detection rate (ADR) differences with the use of computer-aided detection and quality control systems. Moreover, the polyp detection rate (PDR) intergroup differences and withdrawal times will be analyzed.

Methods

This study was conducted adhering to PRISMA guidelines. Studies were searched across PubMed, CINAHL, EMBASE, Scopus, Cochrane, and Web of Science. Keywords including the following 'Artificial Intelligence, Polyp, Adenoma, Detection, Rate, Colonoscopy, Colorectal, Colon, Rectal' were used. Odds ratio (OR) applying 95% CI for PDR and ADR were computed. SMD with 95% CI for withdrawal times were computed using RevMan 5.4.1 (Cochrane). The risk of bias was assessed using the RoB 2 tool.

Results

Of 2562 studies identified, 11 trials were included comprising 6856 participants. Of these, 57.4% participants were in the AI group and 42.6% individuals were in in the standard group. ADR was higher in the AI group compared to the standard of care group (OR=1.51, P=0.003). PDR favored the intervened group compared to the standard group (OR=1.89, P<0.0001). A medium measure of effect was found for withdrawal times (SMD=0.25, P<0.0001), therefore with limited practical applications.

Conclusion

AI-supported colonoscopies improve PDR and ADR; however, no noticeable worsening of withdrawal times is noted. Colorectal cancers are highly preventable if diagnosed early-on. With AI-assisted tools in clinical practice, there is a strong potential to reduce the incidence rates of cancers in the near future.

Artificial intelligence-assisted optical diagnosis for the resect-and-discard strategy in clinical practice: the Artificial intelligence BLI Characterization (ABC) study

BACKGROUND

Optical diagnosis of colonic polyps is poorly reproducible outside of high volume referral centers. The present study aimed to assess whether real-time artificial intelligence (AI)-assisted optical diagnosis is accurate enough to implement the leave-in-situ strategy for diminutive (≤ 5 mm) rectosigmoid polyps (DRSPs).

METHODS

Consecutive colonoscopy outpatients with ≥ 1 DRSP were included. DRSPs were categorized as adenomas or nonadenomas by the endoscopists, who had differing expertise in optical diagnosis, with the assistance of a real-time AI system (CAD-EYE). The primary end point was ≥ 90 % negative predictive value (NPV) for adenomatous histology in high confidence AI-assisted optical diagnosis of DRSPs (Preservation and Incorporation of Valuable endoscopic Innovations [PIVI-1] threshold), with histopathology as the reference standard. The agreement between optical- and histology-based post-polypectomy surveillance intervals (≥ 90 %; PIVI-2 threshold) was also calculated according to European Society of Gastrointestinal Endoscopy (ESGE) and United States Multi-Society Task Force (USMSTF) guidelines.

RESULTS

Overall 596 DRSPs were retrieved for histology in 389 patients; an AI-assisted high confidence optical diagnosis was made in 92.3 %. The NPV of AI-assisted optical diagnosis for DRSPs (PIVI-1) was 91.0 % (95 %CI 87.1 %-93.9 %). The PIVI-2 threshold was met with 97.4 % (95 %CI 95.7 %-98.9 %) and 92.6 % (95 %CI 90.0 %-95.2 %) of patients according to ESGE and USMSTF, respectively. AI-assisted optical diagnosis accuracy was significantly lower for nonexperts (82.3 %, 95 %CI 76.4 %-87.3 %) than for experts (91.9 %, 95 %CI 88.5 %-94.5 %); however, nonexperts quickly approached the performance levels of experts over time.

CONCLUSION

AI-assisted optical diagnosis matches the required PIVI thresholds. This does not however offset the need for endoscopists' high level confidence and expertise. The AI system seems to be useful, especially for nonexperts.

Non-optical polyp-based resect and discard strategy: A prospective clinical study

BACKGROUND

Post-polypectomy surveillance intervals are currently determined based on pathology results.

AIM

To evaluate a polyp-based resect and discard model that assigns surveillance intervals based solely on polyp number and size.

METHODS

Patients undergoing elective colonoscopies at the Montreal University Medical Center were enrolled prospectively. The polyp-based strategy was used to assign the next surveillance interval using polyp size and number. Surveillance intervals were also assigned using optical diagnosis for small polyps (< 10 mm). The primary outcome was surveillance interval agreement between the polyp-based model, optical diagnosis, and the pathology-based reference standard using the 2020 United States Multi-Society Task Force guidelines. Secondary outcomes included the proportion of reduction in required histopathology evaluations and proportion of immediate post-colonoscopy recommendations provided to patients.

RESULTS

Of 944 patients (mean age 62.6 years, 49.3% male, 933 polyps) were enrolled. The surveillance interval agreement for the polyp-based strategy was 98.0% [95% confidence interval (CI): 0.97-0.99] compared with pathology-based assignment. Optical diagnosis-based intervals achieved 95.8% (95%CI: 0.94-0.97) agreement with pathology. When using the polyp-based strategy and optical diagnosis, the need for pathology assessment was reduced by 87.8% and 70.6%, respectively. The polyp-based strategy provided 93.7% of patients with immediate surveillance interval recommendations vs 76.1% for optical diagnosis.

CONCLUSION

The polyp-based strategy achieved almost perfect surveillance interval agreement compared with pathology-based assignments, significantly reduced the number of required pathology evaluations, and provided most patients with immediate surveillance interval recommendations.

The location-based resect and discard strategy for diminutive colorectal polyps: a prospective clinical study

BACKGROUND

Clinical implementation of the resect-and-discard strategy has been difficult because optical diagnosis is highly operator dependent. This prospective study aimed to evaluate a resect-and-discard strategy that is not operator dependent.

METHODS

The study evaluated a resect-and-discard strategy that uses the anatomical polyp location to classify colonic polyps into non-neoplastic or low risk neoplastic. All rectosigmoid diminutive polyps were considered hyperplastic and all polyps located proximally to the sigmoid colon were considered neoplastic. Surveillance interval assignments based on these a priori assumptions were compared with those based on actual pathology results and on optical diagnosis. The primary outcome was ≥ 90 % agreement with pathology in surveillance interval assignment.

RESULTS

1117 patients undergoing complete colonoscopy were included and 482 (43.1 %) had at least one diminutive polyp. Surveillance interval agreement between the location-based strategy and pathological findings using the 2020 US Multi-Society Task Force guideline was 97.0 % (95 % confidence interval [CI] 0.96-0.98), surpassing the ≥ 90 % benchmark. Optical diagnoses using the NICE and Sano classifications reached 89.1 % and 90.01 % agreement, respectively (P < 0.001), and were inferior to the location-based strategy. The location-based resect-and-discard strategy allowed a 69.7 % (95 %CI 0.67-0.72) reduction in pathology examinations compared with 55.3 % (95 %CI 0.52-0.58; NICE and Sano) and 41.9 % (95 %CI 0.39-0.45; WASP) with optical diagnosis.

CONCLUSION

The location-based resect-and-discard strategy achieved very high surveillance interval agreement with pathology-based surveillance interval assignment, surpassing the ≥ 90 % benchmark and outperforming optical diagnosis in surveillance interval agreement and the number of pathology examinations avoided.

Polypectomy for Diminutive and Small Colorectal Polyps

Diminutive and small colorectal polyps are common findings during colonoscopies, and rarely contain dysplastic elements and progress to colorectal cancer. With improving technology and the advent of artificial intelligence, detection rates of small or diminutive polyps and adenomas are rising, resulting in increasing costs associated with colonoscopy. Incomplete resection rates are an outcome of interest because it correlates with interval colorectal cancer. More effort is warranted to standardize training programs and sensitize endoscopists to the importance of personal performance as a quality metric of colonoscopy. This article reviews indications, methods, and recent developments in polypectomy for small and diminutive polyps.

Definition of competence standards for optical diagnosis of diminutive colorectal polyps: European Society of Gastrointestinal Endoscopy (ESGE) Position Statement

BACKGROUND : The European Society of Gastrointestinal Endoscopy (ESGE) has developed a core curriculum for high quality optical diagnosis training for practice across Europe. The development of easy-to-measure competence standards for optical diagnosis can optimize clinical decision-making in endoscopy. This manuscript represents an official Position Statement of the ESGE aiming to define simple, safe, and easy-to-measure competence standards for endoscopists and artificial intelligence systems performing optical diagnosis of diminutive colorectal polyps (1 - 5 mm). METHODS : A panel of European experts in optical diagnosis participated in a modified Delphi process to reach consensus on Simple Optical Diagnosis Accuracy (SODA) competence standards for implementation of the optical diagnosis strategy for diminutive colorectal polyps. In order to assess the clinical benefits and harms of implementing optical diagnosis with different competence standards, a systematic literature search was performed. This was complemented with the results from a recently performed simulation study that provides guidance for setting alternative competence standards for optical diagnosis. Proposed competence standards were based on literature search and simulation study results. Competence standards were accepted if at least 80 % agreement was reached after a maximum of three voting rounds. RECOMMENDATION 1:  In order to implement the leave-in-situ strategy for diminutive colorectal lesions (1-5 mm), it is clinically acceptable if, during real-time colonoscopy, at least 90 % sensitivity and 80 % specificity is achieved for high confidence endoscopic characterization of colorectal neoplasia of 1-5 mm in the rectosigmoid. Histopathology is used as the gold standard.Level of agreement 95 %. RECOMMENDATION 2:  In order to implement the resect-and-discard strategy for diminutive colorectal lesions (1-5 mm), it is clinically acceptable if, during real-time colonoscopy, at least 80 % sensitivity and 80 % specificity is achieved for high confidence endoscopic characterization of colorectal neoplasia of 1-5 mm. Histopathology is used as the gold standard.Level of agreement 100 %. CONCLUSION : The developed SODA competence standards define diagnostic performance thresholds in relation to clinical consequences, for training and for use when auditing the optical diagnosis of diminutive colorectal polyps.

Artificial intelligence-assisted colonoscopy: A review of current state of practice and research

Colonoscopy is an effective screening procedure in colorectal cancer prevention programs; however, colonoscopy practice can vary in terms of lesion detection, classification, and removal. Artificial intelligence (AI)-assisted decision support systems for endoscopy is an area of rapid research and development. The systems promise improved detection, classification, screening, and surveillance for colorectal polyps and cancer. Several recently developed applications for AI-assisted colonoscopy have shown promising results for the detection and classification of colorectal polyps and adenomas. However, their value for real-time application in clinical practice has yet to be determined owing to limitations in the design, validation, and testing of AI models under real-life clinical conditions. Despite these current limitations, ambitious attempts to expand the technology further by developing more complex systems capable of assisting and supporting the endoscopist throughout the entire colonoscopy examination, including polypectomy procedures, are at the concept stage. However, further work is required to address the barriers and challenges of AI integration into broader colonoscopy practice, to navigate the approval process from regulatory organizations and societies, and to support physicians and patients on their journey to accepting the technology by providing strong evidence of its accuracy and safety. This article takes a closer look at the current state of AI integration into the field of colonoscopy and offers suggestions for future research.

Optical diagnosis of colorectal polyps: a randomized controlled trial comparing endoscopic image-enhancing modalities

BACKGROUND AND AIMS

Optical polyp diagnosis using image-enhanced endoscopy (IEE) allows for real-time histology prediction of colorectal polyps. The aim of this study was to evaluate a recently introduced IEE modality (Optivista [OV]; Pentax Medical, Tokyo, Japan) in a randomized controlled trial.

METHODS

In a prospective cohort of subjects (ages 45-80 years) undergoing elective screening, surveillance, or diagnostic colonoscopy, all colorectal polyps between 1 and 5 mm underwent IEE assessment. Study subjects were randomized before their colonoscopy procedure to undergo optical polyp diagnosis using either OV IEE or iScan (IS) IEE. A validated IEE scale (NBI International Colorectal Endoscopic classification) was used for optical polyp diagnosis. The primary outcome was the agreement of surveillance intervals determined when using OV IEE compared with IS IEE in reference with pathology-based surveillance intervals. Secondary outcomes were the percentage of surveillance intervals that could be given on the same day as the procedure, percentage of pathology tests avoided, diagnostic performance, and negative predictive value (NPV) of optical diagnosis for rectosigmoid adenomas.

RESULTS

Four hundred ten patients were enrolled in the trial. The polyp detection rate was 58.6%, and the adenoma detection rate was 38.8%. The proportion of correct surveillance interval assignment when using OV or IS IEE was 96.5% versus 96.0% (P = .75). A total of 65.1% of patients could be given same-day surveillance intervals when using OV IEE versus 73.1% for IS IEE (P = .07). The NPV for rectosigmoid adenomas (including sessile serrated adenomas) was 97.5% when using OV IEE and 88.2% when using IS IEE. Using high-confidence optical diagnosis instead of pathology would have resulted in a 44.3% elimination of required pathology examinations for OV IEE versus 52.8% for IS IEE (P = .34).

CONCLUSIONS

Optical diagnosis using OV and IS IEE both surpassed the 90% benchmark of surveillance interval assignment, and no significant difference with regard to correct surveillance interval assignment was found. OV IEE surpassed the ≥90% NPV for rectosigmoid adenomas, whereas IS IEE did not. (Clinical trial registration number: NCT03515343.).