TY - JOUR A1 - Walter, Steffen A1 - Gruss, Sascha A1 - Neidlinger, Jana A1 - Stross, Isabelle A1 - Hann, Alexander A1 - Wagner, Martin A1 - Seufferlein, Thomas A1 - Walter, Benjamin T1 - Evaluation of an Objective Measurement Tool for Stress Level Reduction by Individually Chosen Music During Colonoscopy—Results From the Study “ColoRelaxTone” JF - Frontiers in Medicine N2 - Background and Aims: Colonoscopy as standard procedure in endoscopy is often perceived as uncomfortable for patients. Patient's anxiety is therefore a significant issue, which often lead to avoidance of participation of relevant examinations as CRC-screening. Non-pharmacological anxiety management interventions such as music might contribute to relaxation in the phase prior and during endoscopy. Although music's anxiolytic effects have been reported previously, no objective measurement of stress level reduction has been reported yet. Focus of this study was to evaluate the objective measurement of the state of relaxation in patients undergoing colonoscopy. Methods: Prospective study (n = 196) performed at one endoscopic high-volume center. Standard colonoscopy was performed in control group. Interventional group received additionally self-chosen music over earphones. Facial Electromyography (fEMG) activity was obtained. Clinician Satisfaction with Sedation Instrument (CSSI) and Patients Satisfaction with Sedation Instrument (PSSI) was answered by colonoscopists and patients, respectively. Overall satisfaction with music accompanied colonoscopy was obtained if applicable. Results: Mean difference measured by fEMG via musculus zygomaticus major indicated a significantly lower stress level in the music group [7.700(±5.560) μV vs. 4.820(±3.330) μV; p = 0.001]. Clinician satisfaction was significantly higher with patients listening to music [82.69(±15.04) vs. 87.3(±15.02) pts.; p = 0.001]. Patient's satisfaction was higher but did not differ significantly. Conclusions: We conclude that self-chosen music contributes objectively to a reduced stress level for patients and therefore subjectively perceived satisfaction for endoscopists. Therefore, music should be considered as a non-pharmacological treatment method of distress reduction especially in the beginning of endoscopic procedures. KW - colonoscopy KW - anxiety KW - stress level KW - music KW - relaxation Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-212337 VL - 7 ER - TY - JOUR A1 - Sudarevic, Boban A1 - Troya, Joel A1 - Fuchs, Karl-Hermann A1 - Hann, Alexander A1 - Vereczkei, Andras A1 - Meining, Alexander T1 - Design and development of a flexible 3D-printed endoscopic grasping instrument JF - Applied Sciences N2 - (1) Background: Interventional endoscopic procedures are growing more popular, requiring innovative instruments and novel techniques. Three-dimensional printing has demonstrated great potential for the rapid development of prototypes that can be used for the early assessment of various concepts. In this work, we present the development of a flexible endoscopic instrument and explore its potential benefits. (2) Methods: The properties of the instrument, such as its maneuverability, flexibility, and bending force, were evaluated in a series of bench tests. Additionally, the effectiveness of the instrument was evaluated in an ex vivo porcine model by medical experts, who graded its properties and performance. Furthermore, the time necessary to complete various interventional endoscopic tasks was recorded. (3) Results: The instrument achieved bending angles of ±216° while achieving a bending force of 7.85 (±0.53) Newtons. The time needed to reach the operating region was 120 s median, while it took 70 s median to insert an object in a cavity. Furthermore, it took 220 s median to insert the instrument and remove an object from the cavity. (4) Conclusions: This study presents the development of a flexible endoscopic instrument using three-dimensional printing technology and its evaluation. The instrument demonstrated high bending angles and forces, and superior properties compared to the current state of the art. Furthermore, it was able to complete various interventional endoscopic tasks in minimal time, thus potentially leading to the improved safety and effectiveness of interventional endoscopic procedures in the future. KW - endoscopy KW - endoscopic intervention KW - 3D printing KW - endoscopic instruments KW - minimally invasive surgery KW - rapid prototyping Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-319186 SN - 2076-3417 VL - 13 IS - 9 ER - TY - JOUR A1 - Lux, Thomas J. A1 - Banck, Michael A1 - Saßmannshausen, Zita A1 - Troya, Joel A1 - Krenzer, Adrian A1 - Fitting, Daniel A1 - Sudarevic, Boban A1 - Zoller, Wolfram G. A1 - Puppe, Frank A1 - Meining, Alexander A1 - Hann, Alexander T1 - Pilot study of a new freely available computer-aided polyp detection system in clinical practice JF - International Journal of Colorectal Disease N2 - 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. KW - colonoscopy KW - polyp KW - artificial intelligence KW - deep learning KW - CADe Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-324459 VL - 37 IS - 6 ER - TY - JOUR A1 - Krenzer, Adrian A1 - Makowski, Kevin A1 - Hekalo, Amar A1 - Fitting, Daniel A1 - Troya, Joel A1 - Zoller, Wolfram G. A1 - Hann, Alexander A1 - Puppe, Frank T1 - Fast machine learning annotation in the medical domain: a semi-automated video annotation tool for gastroenterologists JF - BioMedical Engineering OnLine N2 - Background Machine learning, especially deep learning, is becoming more and more relevant in research and development in the medical domain. For all the supervised deep learning applications, data is the most critical factor in securing successful implementation and sustaining the progress of the machine learning model. Especially gastroenterological data, which often involves endoscopic videos, are cumbersome to annotate. Domain experts are needed to interpret and annotate the videos. To support those domain experts, we generated a framework. With this framework, instead of annotating every frame in the video sequence, experts are just performing key annotations at the beginning and the end of sequences with pathologies, e.g., visible polyps. Subsequently, non-expert annotators supported by machine learning add the missing annotations for the frames in-between. Methods In our framework, an expert reviews the video and annotates a few video frames to verify the object’s annotations for the non-expert. In a second step, a non-expert has visual confirmation of the given object and can annotate all following and preceding frames with AI assistance. After the expert has finished, relevant frames will be selected and passed on to an AI model. This information allows the AI model to detect and mark the desired object on all following and preceding frames with an annotation. Therefore, the non-expert can adjust and modify the AI predictions and export the results, which can then be used to train the AI model. Results Using this framework, we were able to reduce workload of domain experts on average by a factor of 20 on our data. This is primarily due to the structure of the framework, which is designed to minimize the workload of the domain expert. Pairing this framework with a state-of-the-art semi-automated AI model enhances the annotation speed further. Through a prospective study with 10 participants, we show that semi-automated annotation using our tool doubles the annotation speed of non-expert annotators compared to a well-known state-of-the-art annotation tool. Conclusion In summary, we introduce a framework for fast expert annotation for gastroenterologists, which reduces the workload of the domain expert considerably while maintaining a very high annotation quality. The framework incorporates a semi-automated annotation system utilizing trained object detection models. The software and framework are open-source. KW - object detection KW - machine learning KW - deep learning KW - annotation KW - endoscopy KW - gastroenterology KW - automation Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-300231 VL - 21 IS - 1 ER - TY - JOUR A1 - Krenzer, Adrian A1 - Banck, Michael A1 - Makowski, Kevin A1 - Hekalo, Amar A1 - Fitting, Daniel A1 - Troya, Joel A1 - Sudarevic, Boban A1 - Zoller, Wolfgang G. A1 - Hann, Alexander A1 - Puppe, Frank T1 - A real-time polyp-detection system with clinical application in colonoscopy using deep convolutional neural networks JF - Journal of Imaging N2 - 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. KW - machine learning KW - deep learning KW - endoscopy KW - gastroenterology KW - automation KW - object detection KW - video object detection KW - real-time Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-304454 SN - 2313-433X VL - 9 IS - 2 ER - TY - JOUR A1 - Hann, Alexander A1 - Graf, Louisa A1 - Seufferlein, Thomas A1 - Zizer, Eugen T1 - Gastrointestinal Bleeding Diagnosed by Capsule Endoscopy – A Change towards More Patients with Bleeding-related Drugs JF - Journal of Advances in Medicine and Medical Research N2 - Background: Video capsule endoscopy (VCE) is the standard procedure for a work-up of a suspected bleeding source after negative gastroscopy and colonoscopy. Popularity of this procedure increased in the last decade. In this work we aimed to identify the changes in patient characteristics and how those changes influence bleeding related findings. In particular the assumed higher risk of gastrointestinal bleeding of the new oral anticoagulants (nOAC) compared to phenprocoumon was of interest. Methods: Consecutive VCE examinations performed at our center from January 2004 to March 2018 were identified retrospectively. Baseline characteristics of the patients, VCE results and treatment that was initiated were analyzed. Results: 560 VCE were included in the analysis. The rate of VCE per month increased from 2.3/month in the period of January 2004 – December 2012 up to 5.0/month in January 2013 – March 2018. Accompanied by this increase the examined patients suffered from significantly more comorbidities (72 vs. 82%, p 0.001) and used a higher number of bleeding-related drugs (47 vs. 66%, p <0.001), especially nOACs. Age above 65 and bleeding-related drugs were significantly associated with angiodysplasias found on VCE examinations. NOACs and phenprocoumon showed no difference in their correlation to angiodysplasias. Conclusion: This single center retrospective analysis revealed a steep increase in VCE examinations over the last years with an increase in the prevalence of comorbidities and the use of bleeding-related drugs. Interestingly, use of both nOACs and phenprocoumon did not result in a significant higher rate of angiodysplasias in the VCE. KW - video capsule endoscopy KW - small bowel bleeding KW - nOAC KW - VCE Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-256687 SN - 2456-8899 VL - 32 IS - 19 ER - TY - JOUR A1 - Brand, Markus A1 - Troya, Joel A1 - Krenzer, Adrian A1 - Saßmannshausen, Zita A1 - Zoller, Wolfram G. A1 - Meining, Alexander A1 - Lux, Thomas J. A1 - Hann, Alexander T1 - Development and evaluation of a deep learning model to improve the usability of polyp detection systems during interventions JF - United European Gastroenterology Journal N2 - Background The efficiency of artificial intelligence as computer-aided detection (CADe) systems for colorectal polyps has been demonstrated in several randomized trials. However, CADe systems generate many distracting detections, especially during interventions such as polypectomies. Those distracting CADe detections are often induced by the introduction of snares or biopsy forceps as the systems have not been trained for such situations. In addition, there are a significant number of non-false but not relevant detections, since the polyp has already been previously detected. All these detections have the potential to disturb the examiner's work. Objectives Development and evaluation of a convolutional neuronal network that recognizes instruments in the endoscopic image, suppresses distracting CADe detections, and reliably detects endoscopic interventions. Methods A total of 580 different examination videos from 9 different centers using 4 different processor types were screened for instruments and represented the training dataset (519,856 images in total, 144,217 contained a visible instrument). The test dataset included 10 full-colonoscopy videos that were analyzed for the recognition of visible instruments and detections by a commercially available CADe system (GI Genius, Medtronic). Results The test dataset contained 153,623 images, 8.84% of those presented visible instruments (12 interventions, 19 instruments used). The convolutional neuronal network reached an overall accuracy in the detection of visible instruments of 98.59%. Sensitivity and specificity were 98.55% and 98.92%, respectively. A mean of 462.8 frames containing distracting CADe detections per colonoscopy were avoided using the convolutional neuronal network. This accounted for 95.6% of all distracting CADe detections. Conclusions Detection of endoscopic instruments in colonoscopy using artificial intelligence technology is reliable and achieves high sensitivity and specificity. Accordingly, the new convolutional neuronal network could be used to reduce distracting CADe detections during endoscopic procedures. Thus, our study demonstrates the great potential of artificial intelligence technology beyond mucosal assessment. KW - CADe KW - colonoscopy KW - deep learning KW - instrument KW - intervention Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-312708 VL - 10 IS - 5 ER -