Accurate measurement of the size of lesions or distances between any two points during endoscopic examination of the gastrointestinal tract is difficult owing to the fisheye lens used in endoscopy. To overcome this issue, we developed a phase-shift method to measure three-dimensional (3D) data on a curved surface, which we present herein. Our system allows the creation of 3D shapes on a curved surface by the phase-shift method using a stripe pattern projected from a small projecting device to an object. For evaluation, 88 measurement points were inserted in porcine stomach tissue, attached to a half-pipe jig, with an inner radius of 21 mm. The accuracy and precision of the measurement data for our shape measurement system were compared with the data obtained using an Olympus STM6 measurement microscope. The accuracy of the path length of a simulated protruded lesion was evaluated using a plaster model of the curved stomach and graph paper. The difference in height measures between the measurement microscope and measurement system data was 0.24 mm for the 88 measurement points on the curved surface of the porcine stomach. The error in the path length measurement for a lesion on an underlying curved surface was <1% for a 10-mm lesion. The software was developed for the automated calculation of the major and minor diameters of each lesion. The accuracy of our measurement system could improve the accuracy of determining the size of lesions, whether protruded or depressed, regardless of the curvature of the underlying surface.

Colonoscopy is a crucial tool for evaluating lower gastrointestinal disease, monitoring high-risk patients for colorectal neoplasia, and screening for colorectal cancer. In the United States, over 14 million colonoscopies are performed annually, with a significant portion dedicated to post-polypectomy follow-up. Accurate measurement of colorectal polyp size during colonoscopy is essential, as it influences patient management, including the determination of surveillance intervals, resection strategies, and the assessment of malignancy risk. Despite its importance, many endoscopists typically rely on visual estimation alone, which is often imprecise due to technological and human biases, frequently leading to overestimations of polyp size and unnecessarily shortened surveillance intervals. To address these challenges, multiple tools and technologies have been developed to enhance the accuracy of polyp size estimation. The review examines the evolution of polyp measurement techniques, ranging from through-the-scope tools to computer-based and artificial intelligence-assisted technologies.

Accurate measurement of polyp size during colonoscopy is crucial for informing clinical decisions such as resection technique and surveillance scheduling. This study aimed to compare the accuracy of polyp size measurement when using a virtual scale endoscope (VSE) or snare-based polyp size measurement.This randomized controlled trial enrolled 221 patients undergoing screening, surveillance, or diagnostic outpatient colonoscopies. Study subjects were randomized to have polyps detected during the colonoscopy measured for size either using the VSE or a snare of known size to estimate the size of each polyp. All polyps were measured for reference size directly after their removal from the colon using a digital caliper and before formalin fixation.93 polyps were included in the VSE group and 102 in the snare group. The VSE demonstrated significantly higher relative accuracy (80.0% [95%CI 77.0%-82.9%]) compared with snare-based size estimation (66.4% [95%CI 62.4%-70.5%]; P < 0.001). Misclassification rates were lower with the VSE for polyps >2 mm (13.1% vs. 39.3%) and >3 mm (22.6% vs. 55.4%). For diminutive polyps, the VSE better prevented misclassification of >5 mm polyps as 1-5 mm (21.4% vs. 73.0%). The VSE also outperformed snare-based estimation in measuring within 10% of the reference standard size (30.1% vs. 18.6%) and had lower rates of size underestimation (36.5% vs. 65.7%).Using the VSE improves the accuracy of polyp size measurement during colonoscopy in comparison with snare-based size estimation. In clinical scenarios, the VSE reduced misclassifications at clinically relevant size thresholds 2, 3, and 5 mm, which is relevant for the correct choice of polypectomy technique or when implementing resect-and-discard strategies.

Accurate size measurement of colorectal polyps is critical for clinical decision making and patient management. This systematic review aimed to evaluate the current techniques used for colonic polyp measurement to improve the reliability of size estimations in routine practice.A comprehensive literature search was conducted across PubMed, EMBASE, and MEDLINE to identify studies relevant to size measurement techniques published between 1980 and March 2024. The primary outcome was the accuracy of polyp sizing techniques used during colonoscopy.61 studies were included with 34 focusing on unassisted and assisted endoscopic visual estimation and 27 on computer-based tools. There was significant variability in visual size estimation among endoscopists. The most accurate techniques identified were computer-based systems, such as virtual scale endoscopes (VSE) and artificial intelligence (AI)-based systems. The least accurate techniques were visual or snare-based polyp size estimation. VSE assists endoscopists by providing an adaptive scale for real-time, direct, in vivo polyp measurements, while AI systems offer size measurements independent of the endoscopist's subjective judgment.This review highlights the need for standardized, accurate, and accessible techniques to optimize sizing accuracy during endoscopic procedures. There is no consensus on a gold standard for measuring polyps during colonoscopy. While biopsy forceps, snare, and graduated devices can improve the accuracy of visual size estimation, their clinical implementation is limited by practical, time, and cost challenges. Computer-based techniques will likely offer improved accuracy of polyp sizing in the near future.

Background: Colon capsule endoscopy (CCE) is becoming more widely available across Europe, but its uptake is slow due to the need for follow-up colonoscopy for therapeutic procedures and biopsies, which impacts its cost-effectiveness. One of the major factors driving the conversion to colonoscopy is the detection of excess polyps in CCE that cannot be matched during subsequent colonoscopy. The capsule's rocking motion, which can lead to duplicate reporting of the same polyp when viewed from different angles, is likely a key contributor. Objectives: This review aims to explore the types of polyp matching reported in the literature, assess matching techniques and matching accuracy, and evaluate the development of machine learning models to improve polyp matching in CCE and subsequent colonoscopy. Methods: A systematic literature search was conducted in EMBASE, MEDLINE, and PubMed. Due to the scarcity of research in this area, the search encompassed clinical trials, observational studies, reviews, case series, and editorial letters. Three directly related studies were included, and ten indirectly related studies were included for review. Results: Polyp matching in colon capsule endoscopy still needs to be developed, with only one study focused on creating criteria to match polyps within the same CCE video. Another study established that experienced CCE readers have greater accuracy, reducing interobserver variability. A machine learning algorithm was developed in one study to match polyps between initial CCE and subsequent colonoscopy. Only around 50% of polyps were successfully matched, requiring further optimisation. As Artificial Intelligence (AI) algorithms advance in CCE polyp detection, the risk of duplicate reporting may increase when clinicians are presented with polyp images or timestamps, potentially complicating the transition to AI-assisted CCE reading in the future. Conclusions: Polyp matching in CCE is a developing field with considerable challenges, especially in matching polyps within the same video. Although AI shows potential for decent accuracy, more research is needed to refine these techniques and make CCE a more reliable, non-invasive alternative to complement conventional colonoscopy for lower GI investigations.

The development of new image enhancement modalities and improved endoscopic imaging quality has 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 (1-5 mm) polyps. In recent years, artificial intelligence (AI)-based computer-assisted characterization 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 nonretrieval, fragmentation, sectioning errors, incorrect diagnosis as "normal mucosa," and interpathologist variability limit the efficacy of pathology for the diagnosis of 1-5 mm 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 toward normalizing resect-and-discard in routine clinical practice. Ultimately the largest challenges for computer-assisted characterization 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.

Standardized assessment of the gastroesophageal valve during endoscopy, attainable via the Hill classification, is important for clinical assessment and therapeutic decision making. The Hill classification is associated with the presence of hiatal hernia (HH), a common endoscopic finding connected to gastro-esophageal reflux disease. A novel efficient medical artificial intelligence (AI) training pipeline using active learning (AL) is designed. We identified 21,970 gastroscopic images as training data and used our AL to train a model for predicting the Hill classification and detecting HH. Performance of the AL and traditionally trained models were evaluated on an external expert-annotated image collection. The AL model achieved accuracy of 76%. A traditionally trained model with 125% more training data achieved 77% accuracy. Furthermore, the AL model achieved higher precision than the traditional one for rare classes, with 0.54 versus 0.39 (p < 0.05) for grade 3 and 0.72 versus 0.61 (p < 0.05) for grade 4. In detecting HH, the AL model achieved 94% accuracy, 0.72 precision and 0.74 recall. Our AL pipeline is more efficient than traditional methods in training AI for endoscopy.

Colorectal cancer is a significant global health concern, necessitating effective screening strategies to reduce its incidence and mortality rates. Colonoscopy plays a crucial role in the detection and removal of colorectal neoplastic precursors. However, there are limitations and variations in the performance of endoscopists, leading to missed lesions and suboptimal outcomes. The emergence of artificial intelligence (AI) in endoscopy offers promising opportunities to improve the quality and efficacy of screening colonoscopies. In particular, AI applications, including computer-aided detection (CADe) and computer-aided characterization (CADx), have demonstrated the potential to enhance adenoma detection and optical diagnosis accuracy. Additionally, AI-assisted quality control systems aim to standardize the endoscopic examination process. This narrative review provides an overview of AI principles and discusses the current knowledge on AI-assisted endoscopy in the context of screening colonoscopies. It highlights the significant role of AI in improving lesion detection, characterization, and quality assurance during colonoscopy. However, further well-designed studies are needed to validate the clinical impact and cost-effectiveness of AI-assisted colonoscopy before its widespread implementation.

Comprehensive and standardized colonoscopy reports are crucial in colorectal cancer prevention, monitoring, and research. This study investigates adherence to national and international guidelines by analyzing reporting practices among 21 endoscopists in 7 German centers, with a focus on polyp reporting.

We identified and assessed German, European, American, and World Health Organization-provided statements to identify key elements in colonoscopy reporting. Board-certified gastroenterologists rated the relevance of each element and estimated their reporting frequency. Adherence to the identified report elements was evaluated for 874 polyps from 351 colonoscopy reports ranging from March 2021 to March 2022.

We identified numerous recommendations for colonoscopy reporting. We categorized the reasoning behind those recommendations into clinical relevance, justification, and quality control and research. Although all elements were considered relevant by the surveyed gastroenterologists, discrepancies were observed in the evaluated reports. Particularly diminutive polyps or attributes which are rarely abnormal (e.g., surface integrity) respectively rarely performed (e.g., injection) were sparsely documented. Furthermore, the white light morphology of polyps was inconsistently documented using either the Paris classification or free text. In summary, the analysis of 874 reported polyps revealed heterogeneous adherence to the recommendations, with reporting frequencies ranging from 3% to 89%.

The inhomogeneous report practices may result from implicit reporting practices and recommendations with varying clinical relevance. Future recommendations should clearly differentiate between clinical relevance and research and quality control or explanatory purposes. Additionally, the role of computer-assisted documentation should be further evaluated to increase report frequencies of non-pathological findings and diminutive polyps.

To avoid acute variceal bleeding in cirrhosis, current guidelines recommend screening for high-risk esophageal varices (EVs) by determining variceal size and identifying red wale markings. However, visual measurements of EV during routine endoscopy are often inaccurate.

To determine whether biopsy forceps (BF) could be used as a reference to improve the accuracy of binary classification of variceal size.

An in vitro self-made EV model with sizes ranging from 2 to 12 mm in diameter was constructed. An online image-based survey comprising 11 endoscopic images of simulated EV without BF and 11 endoscopic images of EV with BF was assembled and sent to 84 endoscopists. The endoscopists were blinded to the actual EV size and evaluated the 22 images in random order.

The respondents included 48 academic and four private endoscopists. The accuracy of EV size estimation was low in both the visual (13.81%) and BF-based (20.28%) groups. The use of open forceps improved the ability of the endoscopists to correctly classify the varices by size (small ≤ 5 mm, large > 5 mm) from 71.85% to 82.17% (P < 0.001).

BF may improve the accuracy of EV size assessment, and its use in clinical practice should be investigated.