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 - THES A1 - Krenzer, Adrian T1 - Machine learning to support physicians in endoscopic examinations with a focus on automatic polyp detection in images and videos T1 - Maschinelles Lernen zur Unterstützung von Ärzten bei endoskopischen Untersuchungen mit Schwerpunkt auf der automatisierten Polypenerkennung in Bildern und Videos N2 - Deep learning enables enormous progress in many computer vision-related tasks. Artificial Intel- ligence (AI) steadily yields new state-of-the-art results in the field of detection and classification. Thereby AI performance equals or exceeds human performance. Those achievements impacted many domains, including medical applications. One particular field of medical applications is gastroenterology. In gastroenterology, machine learning algorithms are used to assist examiners during interventions. One of the most critical concerns for gastroenterologists is the development of Colorectal Cancer (CRC), which is one of the leading causes of cancer-related deaths worldwide. Detecting polyps in screening colonoscopies is the essential procedure to prevent CRC. Thereby, the gastroenterologist uses an endoscope to screen the whole colon to find polyps during a colonoscopy. Polyps are mucosal growths that can vary in severity. This thesis supports gastroenterologists in their examinations with automated detection and clas- sification systems for polyps. The main contribution is a real-time polyp detection system. This system is ready to be installed in any gastroenterology practice worldwide using open-source soft- ware. The system achieves state-of-the-art detection results and is currently evaluated in a clinical trial in four different centers in Germany. The thesis presents two additional key contributions: One is a polyp detection system with ex- tended vision tested in an animal trial. Polyps often hide behind folds or in uninvestigated areas. Therefore, the polyp detection system with extended vision uses an endoscope assisted by two additional cameras to see behind those folds. If a polyp is detected, the endoscopist receives a vi- sual signal. While the detection system handles the additional two camera inputs, the endoscopist focuses on the main camera as usual. The second one are two polyp classification models, one for the classification based on shape (Paris) and the other on surface and texture (NBI International Colorectal Endoscopic (NICE) classification). Both classifications help the endoscopist with the treatment of and the decisions about the detected polyp. The key algorithms of the thesis achieve state-of-the-art performance. Outstandingly, the polyp detection system tested on a highly demanding video data set shows an F1 score of 90.25 % while working in real-time. The results exceed all real-time systems in the literature. Furthermore, the first preliminary results of the clinical trial of the polyp detection system suggest a high Adenoma Detection Rate (ADR). In the preliminary study, all polyps were detected by the polyp detection system, and the system achieved a high usability score of 96.3 (max 100). The Paris classification model achieved an F1 score of 89.35 % which is state-of-the-art. The NICE classification model achieved an F1 score of 81.13 %. Furthermore, a large data set for polyp detection and classification was created during this thesis. Therefore a fast and robust annotation system called Fast Colonoscopy Annotation Tool (FastCAT) was developed. The system simplifies the annotation process for gastroenterologists. Thereby the i gastroenterologists only annotate key parts of the endoscopic video. Afterward, those video parts are pre-labeled by a polyp detection AI to speed up the process. After the AI has pre-labeled the frames, non-experts correct and finish the annotation. This annotation process is fast and ensures high quality. FastCAT reduces the overall workload of the gastroenterologist on average by a factor of 20 compared to an open-source state-of-art annotation tool. N2 - Deep Learning ermöglicht enorme Fortschritte bei vielen Aufgaben im Bereich der Computer Vision. Künstliche Intelligenz (KI) liefert ständig neue Spitzenergebnisse im Bereich der Erkennung und Klassifizierung. Dabei erreicht oder übertrifft die Leistung von KI teilweise die menschliche Leistung. Diese Errungenschaften wirken sich auf viele Bereiche aus, darunter auch auf medizinische Anwendungen. Ein besonderer Bereich der medizinischen Anwendungen ist die Gastroenterologie. In der Gastroenterologie werden Algorithmen des maschinellen Lernens eingesetzt, um den Untersucher bei medizinischen Eingriffen zu unterstützen. Eines der größten Probleme für Gastroenterologen ist die Entwicklung von Darmkrebs, die weltweit eine der häufigsten krebsbedingten Todesursachen ist. Die Erkennung von Polypen bei Darmspiegelungen ist das wichtigste Verfahren zur Vorbeugung von Darmkrebs. Dabei untersucht der Gastroenterologe den Dickdarm im Rahmen einer Koloskopie, um z.B. Polypen zu finden. Polypen sind Schleimhautwucherungen, die unterschiedlich stark ausgeprägt sein können. Diese Arbeit unterstützt Gastroenterologen bei ihren Untersuchungen mit automatischen Erkennungssystemen und Klassifizierungssystemen für Polypen. Der Hauptbeitrag ist ein Echtzeitpolypenerkennungssystem. Dieses System kann in jeder gastroenterologischen Praxis weltweit mit Open- Source-Software installiert werden. Das System erzielt Erkennungsergebnisse auf dem neusten Stand der Technik und wird derzeit in einer klinischen Studie in vier verschiedenen Praxen in Deutschland evaluiert. In dieser Arbeit werden zwei weitere wichtige Beiträge vorgestellt: Zum einen ein Polypenerkennungssystem mit erweiterter Sicht, das in einem Tierversuch getestet wurde. Polypen verstecken sich oft hinter Falten oder in nicht untersuchten Bereichen. Daher verwendet das Polypenerkennungssystem mit erweiterter Sicht ein Endoskop, das von zwei zusätzlichen Kameras unterstützt wird, um hinter diese Falten zu sehen. Wenn ein Polyp entdeckt wird, erhält der Endoskopiker ein visuelles Signal. Während das Erkennungssystem die beiden zusätzlichen Kameraeingaben verarbeitet, konzentriert sich der Endoskopiker wie gewohnt auf die Hauptkamera. Das zweite sind zwei Polypenklassifizierungsmodelle, eines für die Klassifizierung anhand der Form (Paris) und das andere anhand der Oberfläche und Textur (NICE-Klassifizierung). Beide Klassifizierungen helfen dem Endoskopiker bei der Behandlung und Entscheidung über den erkannten Polypen. Die Schlüsselalgorithmen der Dissertation erreichen eine Leistung, die dem neuesten Stand der Technik entspricht. Herausragend ist, dass das auf einem anspruchsvollen Videodatensatz getestete Polypenerkennungssystem einen F1-Wert von 90,25 % aufweist, während es in Echtzeit arbeitet. Die Ergebnisse übertreffen alle Echtzeitsysteme für Polypenerkennung in der Literatur. Darüber hinaus deuten die ersten vorläufigen Ergebnisse einer klinischen Studie des Polypenerkennungssystems auf eine hohe Adenomdetektionsrate ADR hin. In dieser Studie wurden alle Polypen durch das Polypenerkennungssystem erkannt, und das System erreichte einen hohe Nutzerfreundlichkeit von 96,3 (maximal 100). Bei der automatischen Klassifikation von Polypen basierend auf der Paris Klassifikations erreichte das in dieser Arbeit entwickelte System einen F1-Wert von 89,35 %, was dem neuesten Stand der Technik entspricht. Das NICE-Klassifikationsmodell erreichte eine F1- Wert von 81,13 %. Darüber hinaus wurde im Rahmen dieser Arbeit ein großer Datensatz zur Polypenerkennung und -klassifizierung erstellt. Dafür wurde ein schnelles und robustes Annotationssystem namens FastCAT entwickelt. Das System vereinfacht den Annotationsprozess für Gastroenterologen. Die Gastroenterologen annotieren dabei nur die wichtigsten Teile des endoskopischen Videos. Anschließend werden diese Videoteile von einer Polypenerkennungs-KI vorverarbeitet, um den Prozess zu beschleunigen. Nachdem die KI die Bilder vorbeschriftet hat, korrigieren und vervollständigen Nicht-Experten die Annotationen. Dieser Annotationsprozess ist schnell und gewährleistet eine hohe Qualität. FastCAT reduziert die Gesamtarbeitsbelastung des Gastroenterologen im Durchschnitt um den Faktor 20 im Vergleich zu einem Open-Source-Annotationstool auf dem neuesten Stand der Technik. KW - Deep Learning KW - Maschinelles Lernen KW - Maschinelles Sehen KW - Machine Learning KW - Object Detection KW - Medical Image Analysis KW - Computer Vision Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-319119 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 - 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 - 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 -