Artificial intelligence-based polyp size measurement in gastrointestinal endoscopy using the auxiliary waterjet as a reference

Exploratory investigation of virtual lesions in gastrointestinal endoscopy using a novel phase-shift method for three-dimensional shape measurement

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.

Artificial Intelligence-assisted colonoscopy and colorectal cancer screening: Where are we going?

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

Human-artificial intelligence interaction in gastrointestinal endoscopy

The number and variety of applications of artificial intelligence (AI) in gastrointestinal (GI) endoscopy is growing rapidly. New technologies based on machine learning (ML) and convolutional neural networks (CNNs) are at various stages of development and deployment to assist patients and endoscopists in preparing for endoscopic procedures, in detection, diagnosis and classification of pathology during endoscopy and in confirmation of key performance indicators. Platforms based on ML and CNNs require regulatory approval as medical devices. Interactions between humans and the technologies we use are complex and are influenced by design, behavioural and psychological elements. Due to the substantial differences between AI and prior technologies, important differences may be expected in how we interact with advice from AI technologies. Human–AI interaction (HAII) may be optimised by developing AI algorithms to minimise false positives and designing platform interfaces to maximise usability. Human factors influencing HAII may include automation bias, alarm fatigue, algorithm aversion, learning effect and deskilling. Each of these areas merits further study in the specific setting of AI applications in GI endoscopy and professional societies should engage to ensure that sufficient emphasis is placed on human-centred design in development of new AI technologies.

Closing the Gap: A Critical Examination of Adherence, Inconsistency, and Improvements in Colonoscopy Reporting Practices

INTRODUCTION

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.

METHODS

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.

RESULTS

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%.

CONCLUSION

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.

Biopsy forceps are useful for measuring esophageal varices in vitro

BACKGROUND

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.

AIM

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

METHODS

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.

RESULTS

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).

CONCLUSION

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