Evaluation of a Virtual Reality-Based Open Educational Resource Software.

OBJECTIVES: Virtual reality (VR) teaching methods have potential to support medical students acquire increasing amounts of knowledge. EVENT (Easy VR EducatioN Tool) is an open educational resource software for immersive VR environments, which is designed for use without programming skills. In this work, EVENT was used in a medical student VR course on pancreatic cancer. METHODS: Medical students were invited to participate in the course. Before and after VR simulation, participants completed a multiple-choice knowledge assessment, with a maximum score of 10, and a VR experience questionnaire. The primary endpoint compared pre- and post-VR simulation test scores. Secondary endpoints included usability and factors that could affect learning growth and test results. RESULTS: Data from 117 of the 135 participating students was available for analysis. Student test scores improved by an average of 3.4 points (95% CI 3.1-3.7, P < 0.001) after VR course. The secondary endpoints of gender, age, prior knowledge regarding the medical subject, professional training completed in the medical field, video game play, three-dimensional imagination skills, or cyber-sickness had no major impact on test scores or final ranking (top or bottom 25%). The 27 students whose post-VR simulation test scores ranked in the top 25% had no prior experience with VR. The average System Usability Scale score was 86.1, which corresponds to an excellent outcome for user-friendliness. Questionnaire responses post-VR simulation show students (81.2% [95/117]) interest in more VR options in medical school. CONCLUSIONS: We present a freely available software that allows for the development of VR teaching lessons without programming skills.

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.

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.

Direct comparison of multiple computer-aided polyp detection systems.

BACKGROUND AND STUDY AIMS: Artificial intelligence (AI)-based systems for computer-aided detection (CADe) of polyps receive regular updates and occasionally offer customizable detection thresholds, both of which impact their performance, but little is known about these effects. This study aimed to compare the performance of different CADe systems on the same benchmark dataset. METHODS: 101 colonoscopy videos were used as benchmark. Each video frame with a visible polyp was manually annotated with bounding boxes, resulting in 129 705 polyp images. The videos were then analyzed by three different CADe systems, representing five conditions: two versions of GI Genius, Endo-AID with detection Types A and B, and EndoMind, a freely available system. Evaluation included an analysis of sensitivity and false-positive rate, among other metrics. RESULTS: Endo-AID detection Type A, the earlier version of GI Genius, and EndoMind detected all 93 polyps. Both the later version of GI Genius and Endo-AID Type B missed 1 polyp. The mean per-frame sensitivities were 50.63 % and 67.85 %, respectively, for the earlier and later versions of GI Genius, 65.60 % and 52.95 %, respectively, for Endo-AID Types A and B, and 60.22 % for EndoMind. CONCLUSIONS: This study compares the performance of different CADe systems, different updates, and different configuration modes. This might help clinicians to select the most appropriate system for their specific needs.

Reducing scope 3 carbon emissions in gastrointestinal endoscopy: results of the prospective study of the 'Green Endoscopy Project Würzburg'.

OBJECTIVE: Carbon emissions generated by gastrointestinal endoscopy have been recognised as a critical issue. Scope 3 emissions are mainly caused by the manufacturing, packaging and transportation of purchased goods. However, to our knowledge, there are no prospective data on the efficacy of measurements aimed to reduce scope 3 emissions. DESIGN: The study was performed in a medium-sized academic endoscopy unit. Manufacturers of endoscopic consumables were requested to answer a questionnaire on fabrication, origin, packaging and transport. Based on these data, alternative products were purchased whenever possible. In addition, staff was instructed on how to avoid waste. Thereafter, the carbon footprint of each item purchased was calculated from February to May 2023 (intervention period), and scope 3 emissions were compared with the same period of the previous year (control period). RESULTS: 26 of 40 companies answered the questionnaire. 229 of 322 products were classified as unfavourable. A switch to alternative items was possible for 47/229 items (20.5%). 1666 endoscopies were performed during the intervention period compared with 1751 examinations during the control period (-4.1%). The number of instruments used decreased by 10.0% (3111 vs 3457). Using fewer and alternative products resulted in 11.5% less carbon emissions (7.09 vs 8.01 tons of carbon equivalent=tCO2 e). Separation of waste led to a reduction of 20.1% (26.55 vs 33.24 tCO2e). In total, carbon emissions could be reduced by 18.4%. CONCLUSION: Use of fewer instruments per procedure, recycling packaging material and switching to alternative products can reduce carbon emissions without impairing the endoscopic workflow.

The Role of Specialized Instruments for Advanced Endoscopic Resections in Gastrointestinal Disease.

INTRODUCTION: Advanced endoscopic therapy techniques have been developed and have created alternative treatment options to surgical therapy for several gastrointestinal diseases. This work will focus on new endoscopic tools for special indications of advanced endoscopic resections (ER), especially endoscopic submucosal dissection (ESD), which were developed in our institution. This paper aims to analyze these specialized instruments and identify their status. METHODS: Initially, the technical process of ESD was analyzed, and the following limitations of the different endoscopic steps and the necessary manipulations were determined: the problem of traction-countertraction, the grasping force needed to pull on tissue, the instrument tip maneuverability, the limited angulation/triangulation, and the mobility of the scope and instruments. Five instruments developed by our team were used: the Endo-dissector, additional working channel system, external independent next-to-the-scope grasper, 3D overtube working station, and over-the-scope grasper. The instruments were used and applied according to their special functions in dry lab, experimental in vivo, and clinical conditions by the members of our team. RESULTS: The Endo-dissector has a two-fold function: (1) grasping submucosal tissue with enough precision and strength to pull it off the surrounding mucosa and muscle, avoiding damage during energy application and (2) effectively dividing tissue using monopolar energy. The AWC system quickly fulfills the lack of a second working channel as needed to complete the endoscopic task on demand. The EINTS grasper can deliver a serious grasping force, which may be necessary for a traction-countertraction situation during endoscopic resection for lifting a larger specimen. The 3D overtube multifunctional platform provides surgical-like work with bimanual-operated instruments at the tip of the scope, which allows for a coordinated approach during lesion treatment. The OTSG is a grasping tool with very special features for cleaning cavities with debris. CONCLUSIONS: The research and development of instruments with special features can solve unmet needs in advanced endoscopic procedures. The latter may help to increase indications for the endoscopic resections of gut lesions in the future.

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.

Quality of life in benign colorectal disease-a review of the assessment with the Gastrointestinal Quality of Life Index (GIQLI).

BACKGROUND AND PURPOSE: The Gastrointestinal Quality of Life Index (GIQLI) is an instrument for the assessment of quality of life (QOL) in diseases of the upper and lower GI tract, which is validated in several languages around the world. The purpose of this literature review is the assessment of the GIQLI in patients with benign colorectal diseases. Reports on GIQLI data are collected from several institutions, countries, and different cultures which allows for comparisons, which are lacking in literature. METHODS: The GIQL Index uses 36 items around 5 dimensions (gastrointestinal symptoms (19 items), emotional dimension (5 items), physical dimension (7 items), social dimension (4 items), and therapeutic influences (1 item). The literature search was performed on the GIQLI and colorectal disease, using reports in PubMed. Data are presented descriptively as GIQL Index points as well as a reduction from 100% maximum possible index points (max 144 index points = highest quality of life). RESULTS: The GIQLI was found in 122 reports concerning benign colorectal diseases, of which 27 were finally selected for detailed analysis. From these 27 studies, information on 5664 patients (4046 female versus 1178 male) was recorded and summarized. The median age was 52 years (range 29-74.7). The median GIQLI of all studies concerning benign colorectal disease was 88 index points (range 56.2-113). Benign colorectal disease causes a severe reduction in QOL for patients down to 61% of the maximum. CONCLUSIONS: Benign colorectal diseases cause substantial reductions in the patient's QOL, well documented by GIQLI, which allows a comparison QOL with other published cohorts.

Brood size in an uncertain world.

Reproduction in an uncertain world is fraught. The consequences of investing in too many offspring in a resource poor season can be disastrous but so too is missing the opportunity of a resource rich year. We consider a simple population and individual growth model and use Lyapunov exponents to find analytical results for the optimum brood size under stochastic environmental conditions. We show that if the environment shows dramatic changes between breeding seasons choosing a smaller brood size is more likely to be successful but the best strategy is to synchronize your reproduction to the food availability. Finally, we show that if the cost of having offspring is high it can be better to live in a highly varying world with a plastic strategy that synchronizes to the environment than to live in a deterministic world with a constant strategy, a finding with implications for invasive species and climate change.

New Concept of External and Independent Next-to-the-Scope Intraluminal Grasping Device (EINTS).

Background: Interventional endoscopic procedures require complex manipulations and precise maneuvering of end-effectors. One focus in research on improved endoscopic instrument function was based on surgical experience to gain additional traction. The idea has emerged using assisting instruments by applying external tools next-to-the endoscope to follow surgical concepts. The aim of this study is the assessment of flexible endoscopic grasping instruments regarding their function and working-radius introducing the concept of an intraluminal "next-to the-scope" endoscopic grasper. Methods: In this study endoscopic graspers are evaluated (1:through-the-scope-grasper, TTSG; 2:additional-working-channel-system AWC-S;3:external-independent-next-to-the-scope-grasper EINTS-G) regarding their working-radius, grasping abilities, maneuverability and the ability to expose tissue with varying angulation. Results: The working radius of the tools attached or within the endoscope (TTS-G and AWC-S) benefit from the steering abilities of the scope reaching 180-210 degrees in retroflexion; EINTS-G is limited to 110-degrees. The robust EINTS-grasper has the advantage of stronger grip for grasping and pulling force, which enables manipulation of larger objects. The independent maneuverability during ESD-dissection provides better tissue-exposure by changing the traction-angulation. Conclusion: The working radius of tools attached to the endoscope benefit from scope- steering. The EINTS-grasper has the advantage of stronger grasping force and pulling within the GI-tract and independent maneuverability enables improved tissue-exposure. WC200.

Automated classification of polyps using deep learning architectures and few-shot learning.

BACKGROUND: Colorectal cancer is a leading cause of cancer-related deaths worldwide. The best method to prevent CRC is a colonoscopy. However, not all colon polyps have the risk of becoming cancerous. Therefore, polyps are classified using different classification systems. After the classification, further treatment and procedures are based on the classification of the polyp. Nevertheless, classification is not easy. Therefore, we suggest two novel automated classifications system assisting gastroenterologists in classifying polyps based on the NICE and Paris classification. METHODS: We build two classification systems. One is classifying polyps based on their shape (Paris). The other classifies polyps based on their texture and surface patterns (NICE). A two-step process for the Paris classification is introduced: First, detecting and cropping the polyp on the image, and secondly, classifying the polyp based on the cropped area with a transformer network. For the NICE classification, we design a few-shot learning algorithm based on the Deep Metric Learning approach. The algorithm creates an embedding space for polyps, which allows classification from a few examples to account for the data scarcity of NICE annotated images in our database. RESULTS: For the Paris classification, we achieve an accuracy of 89.35 %, surpassing all papers in the literature and establishing a new state-of-the-art and baseline accuracy for other publications on a public data set. For the NICE classification, we achieve a competitive accuracy of 81.13 % and demonstrate thereby the viability of the few-shot learning paradigm in polyp classification in data-scarce environments. Additionally, we show different ablations of the algorithms. Finally, we further elaborate on the explainability of the system by showing heat maps of the neural network explaining neural activations. CONCLUSION: Overall we introduce two polyp classification systems to assist gastroenterologists. We achieve state-of-the-art performance in the Paris classification and demonstrate the viability of the few-shot learning paradigm in the NICE classification, addressing the prevalent data scarcity issues faced in medical machine learning.

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

BACKGROUND: Measurement of colorectal polyp size during endoscopy is mainly performed visually. In this work, we propose a novel polyp size measurement system (Poseidon) based on artificial intelligence (AI) using the auxiliary waterjet as a measurement reference. METHODS: Visual estimation, biopsy forceps-based estimation, and Poseidon were compared using a computed tomography colonography-based silicone model with 28 polyps of defined sizes. Four experienced gastroenterologists estimated polyp sizes visually and with biopsy forceps. Furthermore, the gastroenterologists recorded images of each polyp with the waterjet in proximity for the application of Poseidon. Additionally, Poseidon's measurements of 29 colorectal polyps during routine clinical practice were compared with visual estimates. RESULTS: In the silicone model, visual estimation had the largest median percentage error of 25.1 % (95 %CI 19.1 %-30.4 %), followed by biopsy forceps-based estimation: median 20.0 % (95 %CI 14.4 %-25.6 %). Poseidon gave a significantly lower median percentage error of 7.4 % (95 %CI 5.0 %-9.4 %) compared with other methods. During routine colonoscopies, Poseidon presented a significantly lower median percentage error (7.7 %, 95 %CI 6.1 %-9.3 %) than visual estimation (22.1 %, 95 %CI 15.1 %-26.9 %). CONCLUSION: In this work, we present a novel AI-based method for measuring colorectal polyp size with significantly higher accuracy than other common sizing methods.

A Real-Time Polyp-Detection System with Clinical Application in Colonoscopy Using Deep Convolutional Neural Networks.

Colorectal cancer (CRC) is a leading cause of cancer-related deaths worldwide. The best method to prevent CRC is with a colonoscopy. During this procedure, the gastroenterologist searches for polyps. However, there is a potential risk of polyps being missed by the gastroenterologist. Automated detection of polyps helps to assist the gastroenterologist during a colonoscopy. There are already publications examining the problem of polyp detection in the literature. Nevertheless, most of these systems are only used in the research context and are not implemented for clinical application. Therefore, we introduce the first fully open-source automated polyp-detection system scoring best on current benchmark data and implementing it ready for clinical application. To create the polyp-detection system (ENDOMIND-Advanced), we combined our own collected data from different hospitals and practices in Germany with open-source datasets to create a dataset with over 500,000 annotated images. ENDOMIND-Advanced leverages a post-processing technique based on video detection to work in real-time with a stream of images. It is integrated into a prototype ready for application in clinical interventions. We achieve better performance compared to the best system in the literature and score a F1-score of 90.24% on the open-source CVC-VideoClinicDB benchmark.

Innovations in GI-endoscopy.

Gastrointestinal endoscopy covers both diagnosis and therapy. Due to its diagnostic accuracy and minimal invasiveness, several innovations have been made within the last years including artificial intelligence and endoscopic tumor resection. The present review highlights some of these innovation. In addition, a special focus is set on the experience made by our own research group trying to combine the expertise of endoscopists/ physicians as well as engineers and computer scientists.

A video based benchmark data set (ENDOTEST) to evaluate computer-aided polyp detection systems.

BACKGROUND AND AIMS: Computer-aided polyp detection (CADe) may become a standard for polyp detection during colonoscopy. Several systems are already commercially available. We report on a video-based benchmark technique for the first preclinical assessment of such systems before comparative randomized trials are to be undertaken. Additionally, we compare a commercially available CADe system with our newly developed one. METHODS: ENDOTEST consisted in the combination of two datasets. The validation dataset contained 48 video-snippets with 22,856 manually annotated images of which 53.2% contained polyps. The performance dataset contained 10 full-length screening colonoscopies with 230,898 manually annotated images of which 15.8% contained a polyp. Assessment parameters were accuracy for polyp detection and time delay to first polyp detection after polyp appearance (FDT). Two CADe systems were assessed: a commercial CADe system (GI-Genius, Medtronic), and a self-developed new system (ENDOMIND). The latter being a convolutional neuronal network trained on 194,983 manually labeled images extracted from colonoscopy videos recorded in mainly six different gastroenterologic practices. RESULTS: On the ENDOTEST, both CADe systems detected all polyps in at least one image. The per-frame sensitivity and specificity in full colonoscopies was 48.1% and 93.7%, respectively for GI-Genius; and 54% and 92.7%, respectively for ENDOMIND. Median FDT of ENDOMIND with 217 ms (Inter-Quartile Range(IQR)8-1533) was significantly faster than GI-Genius with 1050 ms (IQR 358-2767, p = 0.003). CONCLUSIONS: Our benchmark ENDOTEST may be helpful for preclinical testing of new CADe devices. There seems to be a correlation between a shorter FDT with a higher sensitivity and a lower specificity for polyp detection.

Frame-by-Frame Analysis of a Commercially Available Artificial Intelligence Polyp Detection System in Full-Length Colonoscopies.

INTRODUCTION: Computer-aided detection (CADe) helps increase colonoscopic polyp detection. However, little is known about other performance metrics like the number and duration of false-positive (FP) activations or how stable the detection of a polyp is. METHODS: 111 colonoscopy videos with total 1,793,371 frames were analyzed on a frame-by-frame basis using a commercially available CADe system (GI-Genius, Medtronic Inc.). Primary endpoint was the number and duration of FP activations per colonoscopy. Additionally, we analyzed other CADe performance parameters, including per-polyp sensitivity, per-frame sensitivity, and first detection time of a polyp. We additionally investigated whether a threshold for withholding CADe activations can be set to suppress short FP activations and how this threshold alters the CADe performance parameters. RESULTS: A mean of 101 ± 88 FPs per colonoscopy were found. Most of the FPs consisted of less than three frames with a maximal 66-ms duration. The CADe system detected all 118 polyps and achieved a mean per-frame sensitivity of 46.6 ± 26.6%, with the lowest value for flat polyps (37.6 ± 24.8%). Withholding CADe detections up to 6 frames length would reduce the number of FPs by 87.97% (p < 0.001) without a significant impact on CADe performance metrics. CONCLUSIONS: The CADe system works reliable but generates many FPs as a side effect. Since most FPs are very short, withholding short-term CADe activations could substantially reduce the number of FPs without impact on other performance metrics. Clinical practice would benefit from the implementation of customizable CADe thresholds.

Pilot study of a new freely available computer-aided polyp detection system in clinical practice.

PURPOSE: Computer-aided polyp detection (CADe) systems for colonoscopy are already presented to increase adenoma detection rate (ADR) in randomized clinical trials. Those commercially available closed systems often do not allow for data collection and algorithm optimization, for example regarding the usage of different endoscopy processors. Here, we present the first clinical experiences of a, for research purposes publicly available, CADe system. METHODS: We developed an end-to-end data acquisition and polyp detection system named EndoMind. Examiners of four centers utilizing four different endoscopy processors used EndoMind during their clinical routine. Detected polyps, ADR, time to first detection of a polyp (TFD), and system usability were evaluated (NCT05006092). RESULTS: During 41 colonoscopies, EndoMind detected 29 of 29 adenomas in 66 of 66 polyps resulting in an ADR of 41.5%. Median TFD was 130 ms (95%-CI, 80-200 ms) while maintaining a median false positive rate of 2.2% (95%-CI, 1.7-2.8%). The four participating centers rated the system using the System Usability Scale with a median of 96.3 (95%-CI, 70-100). CONCLUSION: EndoMind's ability to acquire data, detect polyps in real-time, and high usability score indicate substantial practical value for research and clinical practice. Still, clinical benefit, measured by ADR, has to be determined in a prospective randomized controlled trial.