Characterization Of Polyps Research Articles

Optical Diagnosis in the Era or Artificial Intelligence.

The development of new image enhancement modalities and improved endoscopic imaging quality have not led to increased adoption of resect-and-discard in routine practice. Studies have shown that endoscopists have the capacity to achieve quality thresholds to perform optical diagnosis, however, this has not led to acceptance of optical diagnosis as a replacement for pathology for diminutive (1-5mm) polyps. In recent years, Artificial Intelligence (AI)-based Computer Assisted Characterisation (CADx) of diminutive polyps has recently emerged as a strategy that could potentially represent a breakthrough technology to enable widespread adoption of resect-and-discard. Recent evidence suggests that pathology-based diagnosis is suboptimal, as polyp non-retrieval, fragmentation, sectioning errors, incorrect diagnosis as 'normal mucosa', and inter-pathologist variability limit the efficacy of pathology for the diagnosis of 1-5mm polyps. New paradigms in performing polyp diagnosis with or without AI have emerged to compete with pathology in terms of efficacy. Strategies, such as Autonomous AI, AI-assisted human diagnosis, AI-unassisted human diagnosis, and combined strategies have been proposed as potential paradigms for resect-and-discard, although further research is still required to determine the optimal strategy. Implementation studies with high patient acceptance, where polyps are truly being discarded without histologic diagnosis are paving the way towards normalizing resect-and-discard in routine clinical practice. Ultimately the largest challenges for CADx remain liability perceptions from endoscopists. The potential benefits of AI-based resect-and-discard are many, with very little potential harm. Real world implementation studies are therefore required to pave the way for the acceptability of such strategies in routine practice.

The use of artificial intelligence in colonoscopic evaluations.

This review aims to highlight the transformative impact of artificial intelligence in the field of gastrointestinal endoscopy, particularly in the detection and characterization of colorectal polyps. Over the past decade, artificial intelligence has significantly advanced the medical industry, including gastrointestinal endoscopy. Computer aided diagnosis - detection (CADe) systems have shown notable success in increasing ADR. Recent meta-analyses of RCTs have demonstrated that patients undergoing colonoscopy with CADe assistance had a higher ADR compared with conventional methods. Similarly, computer aided diagnosis - characterization (CADx) systems have proven effective in distinguishing between adenomatous and nonadenomatous polyps, enhancing diagnostic confidence and supporting cost-saving measures like the resect-and-discard strategy. Despite the high performance of these systems, the variability in real-world adoption highlights the importance of integrating artificial intelligence as an assistive tool rather than a replacement for human expertise. Artificial intelligence integration in colonoscopy, through CADe and CADx systems, marks a significant advancement in gastroenterology. These systems enhance lesion detection and characterization, leading to improved diagnostic accuracy, training outcomes, and clinical workflow efficiency. While artificial intelligence offers substantial benefits, the optimal approach involves using artificial intelligence to augment the expertise of endoscopists, ensuring that clinical decisions remain under human oversight.

Imaging colonic polyps in 2024.

Screening colonoscopy and polypectomy are the cornerstone in decreasing the incidence and mortality of colorectal cancer. Despite the low incidence of colorectal cancer in India, there has been a rising trend in the incidence of colonic polyps and cancer over the last decade. It is, hence, imperative that we are well equipped in themanagement of colonic polyps. Adequate training in thedetection and characterization of polyps to aid in their management is necessary. Detection of polyps can be increased by adhering to the standards of colonoscopy, including good bowel preparation, cecal intubation rate, adequate withdrawal time and use of distal attachment devices. A detected polyp needs optimal characterization to predict histology in real time and decide on the management strategies. Characterization of the polyps requires high-definition-white light endoscopy and/or image-enhanced endoscopy (dye based or digital). Various factors that helpin predicting histology include size, location and morphology of the polyp and the pit pattern, vascular and surface pattern of the polyp. Polyps can be differentiated as neoplastic or non-neoplastic with reasonable accuracy with the above features. Prediction of advanced pathology including high-grade dysplasia and deep sub-mucosal invasion is essential, as it helps indeciding if the lesion is amenable to endotherapy and the technique of endoscopic resection. Adequate training in image-enhanced endoscopy is necessary to assess advanced pathology in polyps. Technology pertaining to image-enhanced endoscopy includes narrow banding imaging and blue laser imaging; newer variations are being introduced every few years making it necessary to be abreast with growing information. The recent advances in gastrointestinal (GI) endoscopy with the advent of endocytoscopy and artificial intelligence seem promising and are predicted to be the future of GI endoscopy.

A complete benchmark for polyp detection, segmentation and classification in colonoscopy images.

Colorectal cancer (CRC) is one of the main causes of deaths worldwide. Early detection and diagnosis of its precursor lesion, the polyp, is key to reduce its mortality and to improve procedure efficiency. During the last two decades, several computational methods have been proposed to assist clinicians in detection, segmentation and classification tasks but the lack of a common public validation framework makes it difficult to determine which of them is ready to be deployed in the exploration room. This study presents a complete validation framework and we compare several methodologies for each of the polyp characterization tasks. Results show that the majority of the approaches are able to provide good performance for the detection and segmentation task, but that there is room for improvement regarding polyp classification. While studied show promising results in the assistance of polyp detection and segmentation tasks, further research should be done in classification task to obtain reliable results to assist the clinicians during the procedure. The presented framework provides a standarized method for evaluating and comparing different approaches, which could facilitate the identification of clinically prepared assisting methods.

Reliability of Kudo’s Glandular Pit Pattern in Predicting Colorectal Lesion Histology at Routine Colonoscopy with Digital Chromoendoscopy

Background. The large number of lesions detected via high-definition (HD) imaging during colonoscopy calls for the reliable real-time histological characterization of polyps, especially diminutive and small ones, to permit tailored management based on the neoplastic risk, such as a “resect-and-discard” or a “diagnose-and-leave” strategy for low-risk adenomas and hyperplastic polyps (HPs). The Kudo classification of glandular pit pattern is currently used for predicting polyp histology. Aim. The aim in this study was to assess whether Kudo’s glandular pit pattern, assessed via HD digital chromoendoscopy (i-Scan) without magnification and optical enhancement, reliably predicts polyp histology and differentiates neoplastic lesions (NLs) from non-neoplastic lesions (non-NLs) during routine colonoscopy. Methods. Consecutive colorectal lesions recorded in a database over 12 months, with Kudo’s glandular pit pattern classification, were retrospectively compared with histology. The diagnostic accuracy and negative predictive value (NPV) for adenomatous histology of Kudo’s pit patterns were assessed separately for diminutive (≤5 mm) and small (6–9 mm) polyps, accordingly to the American Society for Gastrointestinal Endoscopy (ASGE) Preservation and Incorporation of Valuable Endoscopic Innovations (PIVI), and in large (≥10 mm) lesions. Results. A total of 2230 lesions were recorded: 898 diminutive, 704 small, and 628 large. Kudo’s type II pit pattern was prevalent in diminutive polyps and recognized mostly in HPs (83.27%); it was also found in 38.8% of adenomas. In the right colon, Kudo’s type II pit pattern was prevalent in adenomas (70.04% vs. 20.74% in HPs); among the serrated lesions, it was evenly distributed between HPs and adenomas. Kudo’s type IIIL/IIIs/IV pit pattern was prevalent in NLs (61% vs. 8.37% of non-NLs) in diminutive polyps, evenly distributed between non-NLs and NLs in small polyps, and found only in NLs in large polyps. Kudo’s type Vi/Vn pit pattern correctly identified all but one adenocarcinoma. The NPV for adenomatous histology did not reach the recommended 90% PIVI threshold for differentiation between NLs and non-NLs in diminutive polyps showing Kudo’s type II pit pattern and in small polyps showing type IIIL/IIIs/IV pit pattern. Conclusions. Kudo’s pit pattern classification carried out with digital chromoendoscopy (i-Scan) during routine colonoscopy does not allow the reliable differentiation between non-NLs and NLs in diminutive and small polyps, so a “diagnose-and-leave” strategy for diminutive polyps may leave undetected adenomas, while a “resect-and-discard” strategy could miss lesions requiring closer follow-up.

Comparative analysis of machine learning frameworks for automatic polyp characterization

Early detection and characterization of adenomatous polyps, the precursors to colorectal cancer is crucial for effective diagnosis and treatment of colorectal cancer. A suitably trained machine learning algorithm can perform automatic real-time optical characterization of polyps during colonoscopy procedures with virtual chromoendoscopy systems, such as Narrow Band Imaging (NBI). This study proposes to perform a detailed comparative analysis between the performances of two popular machine learning frameworks, namely training from scratch and transfer learning, using three state-of-the-art architectures ResNet50, EfficientNetB2, and ViTb_32 with one indigenous dataset and one publicly available dataset. The indigenous dataset was created using 1739 NBI frames extracted from colonoscopy videos of 30 polyp masses from 24 patients [870 hyperplastic polyp frames and 869 adenomatous polyp frames]. The publicly available dataset also consisted of NBI frames. Blurry frames or frames with specular reflections were not excluded to closely resemble real-life polyp characterization in a clinical setting. Among the three models used in two different frameworks, EfficientNetB2 in the training from scratch framework yielded the best accuracy, AUC and NPV of 91.91%, 0.99 and 89.06%. The results show that the training from scratch framework outperformed the transfer learning framework in terms of the evaluation metrics. To the best of our knowledge, this is the first indigenous study to focus on polyp classification using solely Narrow Band Imaging (NBI) polyp images. Our results using only NBI polyp frames outperformed those obtained with a combination of white light and NBI frames, as observed in similar existing studies.

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 EYETM (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.

Risk Perception, Acceptance, and Trust of Using AI in Gastroenterology Practice in the Asia-Pacific Region: Web-Based Survey Study

Background The use of artificial intelligence (AI) can revolutionize health care, but this raises risk concerns. It is therefore crucial to understand how clinicians trust and accept AI technology. Gastroenterology, by its nature of being an image-based and intervention-heavy specialty, is an area where AI-assisted diagnosis and management can be applied extensively. Objective This study aimed to study how gastroenterologists or gastrointestinal surgeons accept and trust the use of AI in computer-aided detection (CADe), computer-aided characterization (CADx), and computer-aided intervention (CADi) of colorectal polyps in colonoscopy. Methods We conducted a web-based questionnaire from November 2022 to January 2023, involving 5 countries or areas in the Asia-Pacific region. The questionnaire included variables such as background and demography of users; intention to use AI, perceived risk; acceptance; and trust in AI-assisted detection, characterization, and intervention. We presented participants with 3 AI scenarios related to colonoscopy and the management of colorectal polyps. These scenarios reflect existing AI applications in colonoscopy, namely the detection of polyps (CADe), characterization of polyps (CADx), and AI-assisted polypectomy (CADi). Results In total, 165 gastroenterologists and gastrointestinal surgeons responded to a web-based survey using the structured questionnaire designed by experts in medical communications. Participants had a mean age of 44 (SD 9.65) years, were mostly male (n=116, 70.3%), and mostly worked in publicly funded hospitals (n=110, 66.67%). Participants reported relatively high exposure to AI, with 111 (67.27%) reporting having used AI for clinical diagnosis or treatment of digestive diseases. Gastroenterologists are highly interested to use AI in diagnosis but show different levels of reservations in risk prediction and acceptance of AI. Most participants (n=112, 72.72%) also expressed interest to use AI in their future practice. CADe was accepted by 83.03% (n=137) of respondents, CADx was accepted by 78.79% (n=130), and CADi was accepted by 72.12% (n=119). CADe and CADx were trusted by 85.45% (n=141) of respondents and CADi was trusted by 72.12% (n=119). There were no application-specific differences in risk perceptions, but more experienced clinicians gave lesser risk ratings. Conclusions Gastroenterologists reported overall high acceptance and trust levels of using AI-assisted colonoscopy in the management of colorectal polyps. However, this level of trust depends on the application scenario. Moreover, the relationship among risk perception, acceptance, and trust in using AI in gastroenterology practice is not straightforward.

Artificial intelligence for characterization of colorectal polyps: Prospective multicenter study.

Background and study aims Optical diagnosis poses challenges to implementation of "resect and discard" strategies. This study aimed to assess the feasibility and performance of a new commercially available system for colorectal polyps. Patients and methods Nine expert endoscopists in three centers performed colonoscopies using artificial intelligence-equipped colonoscopes (CAD EYE, Fujifilm). Histology and predictions were compared, with hyperplastic polyps and sessile serrated lesions grouped for analysis. Results Overall, 253 polyps in 119 patients were documented (n=152 adenomas, n=78 hyperplastic polyps, n=23 sessile serrated lesions). CAD EYE detected polyps before endoscopists in 81 of 253 cases (32%). The mean polyp size was 5.5 mm (SD 0.6 mm). Polyp morphology was Paris Ip (4 %), Is (28 %), IIa (60 %), and IIb (8 %). CAD EYE achieved a sensitivity of 80%, specificity of 83%, positive predictive value (PPV) of 96%, and negative predictive value (NPV) of 72%. Expert endoscopists had a sensitivity of 88%, specificity of 83%, PPV of 96%, and NPV of 72%. Diagnostic accuracy was similar between CAD EYE (81%) and endoscopists (86%). However, sensitivity was greater with endoscopists as compared with CAD EYE ( P <0.05). CAD EYE classified sessile serrated lesions as hyperplasia in 22 of 23 cases, and endoscopists correctly classified 16 of 23 cases. Conclusions The CAD EYE system shows promise for detecting and characterizing colorectal polyps. Larger studies are needed, however, to confirm these findings.

A31 DYSPLASIA IN COLONIC POLYPS: PREPARING FOR A RESECT AND DISCARD STRATEGY IN CANADA

Abstract Background While diminutive colorectal polyps have a negligible cancer risk, current management involves resecting and submitting all polyps for histological assessment. This is a substantial burden and cost to the healthcare system. The European Society of Gastrointestinal Endoscopy (ESGE) has recommended a “resect-and-discard strategy” without histological evaluation as an acceptable strategy when high-confidence endoscopic characterization of colorectal polyps is achieved. However, a resect and discard strategy has not been adopted yet in Canada. Aims The objective of this study was to determine the prevalence and characteristics of polyps removed at our center based on size. Methods We retrospectively reviewed colonoscopies and pathology reports at Kingston Health Sciences Center from January to December 2020. Based on the pathology report, polyps were classified as diminutive (1-5mm), small (6-9mm) or large (ampersand:003E or = 10mm). We recorded histology and the presence of high-grade dysplasia (HGD) or cancer. Results Out of 2218 colonoscopies, 2945 polyps were removed. 1703 (57.8%) polyps were diminutive with only two (0.1%) having focal HGD and none having cancer. 699 (23.7%) polyps were classified as small with three (0.4%) having HGD and none having cancer. The large polyp group had 543 (18.4%) polyps, of which 87 (16%) showed HGD and 15 (2.8%) exhibited cancer. The specific histologic findings are shown in Table 1. Endoscopy reports specifically mentioned concern of dysplasia in one out of the five polyps with HGD in the diminutive and small groups. Conclusions As expected, most polyps were either diminutive or small (81.5%). Neither of these groups had cancer and only 5 had HGD. Adopting a resect and discard strategy at our center for diminutive polyps has the potential for significant cost savings with negligible risk of missing a high-risk polyp. The next steps would involve assessing optical diagnosis sensitivity and specificity for diminutive and small polyps. Histological characterization of polyps within each size range Funding Agencies None

Imaging of chronic rhinosinusitis with nasal polyps in the era of biological therapies.

Chronic rhinosinusitis (CRS) is a chronic inflammatory disorder of the sinonasal cavities classified into two major phenotypes: CRS with nasal polyps (CRSwNP) and without nasal polyps (CRSsNP). The diagnosis of CRS is based on clinical symptoms associated with imaging and/or nasal endoscopy findings of mucosal inflammation. Recently, novel biological therapies have emerged as therapeutic options for CRSwNP. Imaging is helpful in deciding whether surgery is likely to be beneficial and in guiding surgery. It can also help demonstrate a clinical response to medical therapy. However, specific guidelines concerning the role of imaging in CRwNP are lacking. This article provides a comprehensive and critical multidisciplinary review of the role of conventional radiology, computed tomography (CT), and magnetic resonance imaging (MRI) in the diagnosis and characterization of CRSwNP. Since the complete characterization of nasal polyps on CT or MR images is very challenging, we provide a critical review of the best imaging methods and essential reporting elements used to assess nasal polyps.

Endoscopic characterization of colonic polyps: where do we stand?

Endoscopic characterization of colonic polyps: where do we stand?

Endoscopic characterization of colonic polyps: a literature review

Endoscopic characterization of colonic polyps: a literature review

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.

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.

From Staining Techniques to Artificial Intelligence: A Review of Colorectal Polyps Characterization.

This review article provides a comprehensive overview of the evolving techniques in image-enhanced endoscopy (IEE) for the characterization of colorectal polyps, and the potential of artificial intelligence (AI) in revolutionizing the diagnostic accuracy of endoscopy. We discuss the historical use of dye-spray and virtual chromoendoscopy for the characterization of colorectal polyps, which are now being replaced with more advanced technologies. Specifically, we focus on the application of AI to create a "virtual biopsy" for the detection and characterization of colorectal polyps, with potential for replacing histopathological diagnosis. The incorporation of AI has the potential to provide an evolutionary learning system that aids in the diagnosis and management of patients with the best possible outcomes. A detailed analysis of the literature supporting AI-assisted diagnostic techniques for the detection and characterization of colorectal polyps, with a particular emphasis on AI's characterization mechanism, is provided. The benefits of AI over traditional IEE techniques, including the reduction in human error in diagnosis, and its potential to provide an accurate diagnosis with similar accuracy to the gold standard are presented. However, the need for large-scale testing of AI in clinical practice and the importance of integrating patient data into the diagnostic process are acknowledged. In conclusion, the constant evolution of IEE technology and the potential for AI to revolutionize the field of endoscopy in the future are presented.

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

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

Colon polyp characterization (morphology and mucosal patterns): clinical application and techniques

Colon polyp characterization (morphology and mucosal patterns): clinical application and techniques

AI in colonoscopy - detection and characterisation of malignant polyps

The medical technological revolution has transformed the nature with which we deliver care. Adjuncts such as artificial intelligence and machine learning have underpinned this. The applications to the field of endoscopy are numerous. Malignant polyps represent a significant diagnostic dilemma as they lie in an area in which mischaracterisation may mean the difference between an endoscopic procedure and a formal bowel resection. This has implications for patients’ oncological outcomes, morbidity and mortality, especially if post-procedure histopathology upstages disease. We have made significant strides with the applications of artificial intelligence to colonoscopic detection. Deep learning algorithms are able to be created from video and image databases. These have been applied to traditional, human-derived, classification methods, such as Paris or Kudo, with up to 93% accuracy. Furthermore, multimodal characterisation systems have been developed, which also factor in patient demographics and colonic location to provide an estimation of invasion and endoscopic resectability with over 90% accuracy. Although the technology is still evolving, and the lack of high-quality randomised controlled trials limits clinical usability, there is an exciting horizon upon us for artificial intelligence-augmented endoscopy.

Accuracy of polyp characterization by artificial intelligence and endoscopists: a prospective, non-randomized study in a tertiary endoscopy center.

Background and study aims Artificial intelligence (AI) in gastrointestinal endoscopy is developing very fast. Computer-aided detection of polyps and computer-aided diagnosis (CADx) for polyp characterization are available now. This study was performed to evaluate the diagnostic performance of a new commercially available CADx system in clinical practice. Patients and methods This prospective, non-randomized study was performed at a tertiary academic endoscopy center from March to August 2022. We included patients receiving a colonoscopy. Polypectomy had to be performed in all polyps. Every patient was examined concurrently by an endoscopist and AI using two opposing screens. The AI system, overseen by a second observer, was not visible to the endoscopist. The primary outcome was accuracy of the AI classifying the polyps into "neoplastic" and "non-neoplastic." The secondary outcome was accuracy of the classification by the endoscopists. Sessile serrated lesions were classified as neoplastic. Results We included 156 patients (mean age 65; 57 women) with 262 polyps ≤10 mm. Eighty-four were hyperplastic polyps (32.1%), 158 adenomas (60.3%), seven sessile serrated lesions (2.7%) and 13 other entities (normal/inflammatory colonmucosa, lymphoidic polyp) (4.9%) on histological diagnosis. Sensitivity, specificity and accuracy of AI were 89.70% (95% confidence interval [CI]: 84.02%-93.88%), 75.26% (95% CI: 65.46%-83.46%) and 84.35% (95% CI:79.38%-88.53%), respectively. Sensitivity, specificity and accuracy for less experienced endoscopists (2-5 years of endoscopy) were 95.56% (95% CI: 84.85%-99.46%), 61.54% (95% CI: 40.57%-79.77%) and 83.10% (95% CI: 72.34%-90.95%) and for experienced endoscopists 90.83% (95% CI: 84.19%-95.33%), 71.83% (95% CI: 59.90%-81.87%) and 83.77% (95% CI: 77.76%-88.70%), respectively. Conclusion Accuracy for polyp characterization by a new commercially available AI system is high, but does not fulfill the criteria for a "resect-and-discard" strategy.