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Institute
- Fakultät Informatik und Mathematik (1141)
- Labor für Technikfolgenabschätzung und Angewandte Ethik (LaTe) (185)
- Regensburg Medical Image Computing (ReMIC) (165)
- Institut für Sozialforschung und Technikfolgenabschätzung (IST) (145)
- Fakultät Angewandte Sozial- und Gesundheitswissenschaften (100)
- Labor Empirische Sozialforschung (93)
- Regensburg Strategic IT Management (ReSITM) (89)
- Labor für Digitalisierung (LFD) (84)
- Labor eHealth (eH) (66)
- Regensburg Center of Health Sciences and Technology - RCHST (59)
Begutachtungsstatus
- peer-reviewed (496)
- begutachtet (5)
Hintergrund und Fragestellung
Nichtregierungsorganisationen (NRO) sind ein wichtiger Bestandteil der Zivilgesellschaft und interagieren auch mit Regierungen, Unternehmen und anderen gesellschaftlichen Akteuren. Aufgrund der komplexer werdenden Arbeit von NROs scheint Künstliche Intelligenz (KI) Möglichkeiten zur Bewältigung aktueller und zukünftiger Herausforderungen zu bieten. Jedoch ist wenig über die Arbeit von NROs mit KI bekannt.
Methodik
Es wurden fünf explorative Interviews mit Vertreter*innen von Nichtregierungsorganisationen (NROs) zum Thema Wissen, Akzeptanz, Bedarfe und Risikoeinschätzungen geführt und ausgewertet. Dabei sind NROs aus verschiedenen Handlungsfeldern und Größe interviewt. Es wurden informelle Vorgespräche geführt, um eine erste Orientierung im Forschungsfeld zu generieren. Aus den Erkenntnissen der Vorgespräche
und den Ergebnissen des Scoping Reviews ist ein Leitfaden erstellt worden, der zur Orientierung für die Expert*inneninterviews dient. Im Anschluss wurden dann fünf explorative leitfadengestützte Expert*inneninterviews durchgeführt und qualitativ ausgewertet.
Ergebnisse
Ein zentraler Befund ist, dass das Thema KI gerade in den NROs ankommt und es noch keine gefestigten Strukturen und Vorstellungen zum Einsatz von KI gibt. KI wird in einzelnen spezifischen Projekten eingesetzt, ohne dass diese umfassend in Arbeitsabläufe integriert ist. Die Akzeptanz von KI ist generell positiv; die Technologie wird als potenzielle Lösung für strukturelle Herausforderungen und Unterstützung im Alltag gesehen. Die Nutzung von KI-Anwendungen beschränkt sich jedoch mit Ausnahme von Large Language Models auf Pilotprojekte. Mit jüngerem Alter und Technikaffinität ist eine höhere Akzeptanz verbunden. Besonders kritisch werden Anwendungen von KI im Sozial- oder Gesundheitsbereich als Ersatz für menschliche Interaktionen gesehen. Betont werden auch ethische Bedenken und eine hohe Bedeutsamkeit von Datenschutz.
Schlussfolgerung
Künstliche Intelligenz in Nichtregierungsorganisationen ist ein aufkommendes und sich entwickelndes Forschungsthema. Die Interviews unterstreichen den Bedarf an mehr Wissen, ethischen Richtlinien und finanziellen Ressourcen für eine effektive Nutzung von KI in NROs. Ein umfassendes Verständnis von KI und eine tiefergehende, systematische Integration in Arbeitsabläufe in diesen Organisationen müssen noch entwickelt werden.
Determinants of household electricity consumption measured by smart meters found by the authors in a scoping review were analyzed for the example of Germany utilizing the 2018 Survey of Income and Expenditure. All variables identified in the scoping review were covered in the survey (number and type of appliances, sociodemographic, and dwelling-related aspects). One can therefore use this large representative data set to test these relationships for German households. Expenditure on electricity is considered an indicator of household electricity consumption. The determinants show weak to moderate correlations with energy expenditure in bivariate analyses. The multivariate analysis shows effects of household-specific, dwelling-related, and appliance-specific factors. Models considering only one aspect overestimate this effect. Thus, all three aspects should be considered simultaneously when explaining residential electricity consumption. The largest effects are found for electricity as the main energy source for heating, the number of household members, as well as their presence at home. While household structure plays an important part in explaining residential energy consumption, dwelling and appliance-related aspects influence it as well. The latter aspects may be influenced by appropriate policy measures.
Even though artificial intelligence and machine learning have demonstrated remarkable performances in medical image computing, their accountability and transparency level must be improved to transfer this success into clinical practice. The reliability of machine learning decisions must be explained and interpreted, especially for supporting the medical diagnosis.For this task, the deep learning techniques’ black-box nature must somehow be lightened up to clarify its promising results. Hence, we aim to investigate the impact of the ResNet-50 deep convolutional design for Barrett’s esophagus and adenocarcinoma classification. For such a task, and aiming at proposing a two-step learning technique, the output of each convolutional layer that composes the ResNet-50 architecture was trained and classified for further definition of layers that would provide more impact in the architecture. We showed that local information and high-dimensional features are essential to improve the classification for our task. Besides, we observed a significant improvement when the most discriminative layers expressed more impact in the training and classification of ResNet-50 for Barrett’s esophagus and adenocarcinoma classification, demonstrating that both human knowledge and computational processing may influence the correct learning of such a problem.
Hintergrund und Fragestellung
Nichtregierungsorganisationen (NRO) sind ein wichtiger Bestandteil der Zivilgesellschaft und interagieren auch mit Regierungen, Unternehmen und anderen gesellschaftlichen Akteuren. Aufgrund der komplexer werdenden Arbeit von NROs scheint Künstliche Intelligenz (KI) Möglichkeiten zur Bewältigung aktueller und zukünftiger Herausforderungen zu bieten. Jedoch ist wenig über die Arbeit von NROs mit KI bekannt. Daher beschäftigt sich das Projekt KINiro in diesem ersten Working Paper mit der Frage, welche (nicht-)wissenschaftlichen Erkenntnisse zum Themenkomplex NROs und KI bereits vorliegen.
Methodik
Es wurde ein Scoping Review zur Erfassung (nicht-)wissenschaftlicher Texte zu NROs und KI durchgeführt. Die systematische Literaturrecherche wurde in den Datenbanken Web of Science, Science Gate und WISO durchgeführt. In den Review wurden schließlich 14 Titel eingeschlossen und qualitativ analysiert.
Ergebnisse
Die Mehrheit der gefundenen Treffer sind Pressemitteilungen. Unter den Treffern befinden sich lediglich zwei (wissenschaftliche) Studien. Die NROs setzen sich auf verschiedenen Ebenen mit der Thematik von KI auseinander, wobei sich zwei Herangehensweisen unterscheiden lassen. Einige NROs nehmen am gesellschaftlichen Diskurs über den Einsatz von KI teil und treiben diesen in theoretischer Hinsicht voran, ohne die Technik dabei selbst zu nutzen. Andere NROs integrieren die KI-Systeme praktisch in ihre Arbeitsabläufe oder führen Projekte zum Zweck der NRO mit KI-Unterstützung durch. Für die Entwicklung von KI-Anwendungen wird mit For-Profit-Unternehmen kooperiert und die Expertise der Unternehmen mit Daten der NROs kombiniert. Durch den Einsatz von KI erhoffen sich NROs einen gezielteren Einsatz von Ressourcen. Hierbei zeigt sich, dass für die Nutzung in KI-Systemen ein interdisziplinärer Konsens über Standards in Datenerhebung und Speicherung als notwendig angesehen wird.
Schlussfolgerung
Aus der geringen Anzahl an gefundenen Texten, insbesondere (wissenschaftlichen) Studien, und dem Veröffentlichungszeitraum, der in den vergangenen sieben Jahren liegt, lässt sich schließen, dass es sich um einen jungen Forschungsbereich handelt. Die ausgeschlossenen Titel zeigen auf, dass NROs aktuell noch häufiger mit der Digitalisierunge allgemein beschäftigt sind und die Auseinandersetzung mit KI erst noch am Anfang steht.
Künstliche Intelligenz (KI) stellt eine Schlüsseltechnologie des gesellschaftlichen Wandels im 21. Jahrhundert dar. Mittlerweile werden zahlreiche technologische Anwendungen genutzt, die auf maschinellem Lernen und den damit verbundenen Möglichkeiten der Datensamm¬lung, -nutzung und -verwertung aufbauen. Indem KI große Datenmengen beherrschbar und verborgene Muster und Zusammenhänge sichtbar macht, wird vieles schneller, einfacher und effizienter – sei es im Alltag, in der Arbeit oder in Organisationen. Offen bleibt jedoch nach wie vor die Frage, welche tiefgreifenden und teilweise latenten Folgen für den Menschen als soziales Wesen und das gesellschaftliche Zusammenleben mit dem Einsatz und der Entwick¬lung von KI verbunden sind. Wie wandelt sich das Verhältnis von Mensch und Technik durch KI und wie ist dieser Wandel zu bewerten? Welche Chancen, aber auch Risiken eröffnen sich durch den Einsatz und die Entwicklung von KI für Mensch und Gesellschaft? Welchen Grenzen unterliegt der Wandel und welche Gestaltungsmöglichkeiten bieten sich? Und nicht zuletzt: Was und wer bestimmt die Entwicklungspfade, die KI nimmt – mit welchen Folgen und für wen?
Background
This study evaluated the effect of an artificial intelligence (AI)-based clinical decision support system on the performance and diagnostic confidence of endoscopists in their assessment of Barrett’s esophagus (BE).
Methods
96 standardized endoscopy videos were assessed by 22 endoscopists with varying degrees of BE experience from 12 centers. Assessment was randomized into two video sets: group A (review first without AI and second with AI) and group B (review first with AI and second without AI). Endoscopists were required to evaluate each video for the presence of Barrett’s esophagus-related neoplasia (BERN) and then decide on a spot for a targeted biopsy. After the second assessment, they were allowed to change their clinical decision and confidence level.
Results
AI had a stand-alone sensitivity, specificity, and accuracy of 92.2%, 68.9%, and 81.3%, respectively. Without AI, BE experts had an overall sensitivity, specificity, and accuracy of 83.3%, 58.1%, and 71.5%, respectively. With AI, BE nonexperts showed a significant improvement in sensitivity and specificity when videos were assessed a second time with AI (sensitivity 69.8% [95%CI 65.2%–74.2%] to 78.0% [95%CI 74.0%–82.0%]; specificity 67.3% [95%CI 62.5%–72.2%] to 72.7% [95%CI 68.2%–77.3%]). In addition, the diagnostic confidence of BE nonexperts improved significantly with AI.
Conclusion
BE nonexperts benefitted significantly from additional AI. BE experts and nonexperts remained significantly below the stand-alone performance of AI, suggesting that there may be other factors influencing endoscopists’ decisions to follow or discard AI advice.
Aims
Recent evidence suggests the possibility of intraprocedural phase recognition in surgical operations as well as endoscopic interventions such as peroral endoscopic myotomy and endoscopic submucosal dissection (ESD) by AI-algorithms. The intricate measurement of intraprocedural phase distribution may deepen the understanding of the procedure. Furthermore, real-time quality assessment as well as automation of reporting may become possible. Therefore, we aimed to develop an AI-algorithm for intraprocedural phase recognition during ESD.
Methods
A training dataset of 364385 single images from 9 full-length ESD videos was compiled. Each frame was classified into one procedural phase. Phases included scope manipulation, marking, injection, application of electrical current and bleeding. Allocation of each frame was only possible to one category. This training dataset was used to train a Video Swin transformer to recognize the phases. Temporal information was included via logarithmic frame sampling. Validation was performed using two separate ESD videos with 29801 single frames.
Results
The validation yielded sensitivities of 97.81%, 97.83%, 95.53%, 85.01% and 87.55% for scope manipulation, marking, injection, electric application and bleeding, respectively. Specificities of 77.78%, 90.91%, 95.91%, 93.65% and 84.76% were measured for the same parameters.
Conclusions
The developed algorithm was able to classify full-length ESD videos on a frame-by-frame basis into the predefined classes with high sensitivities and specificities. Future research will aim at the development of quality metrics based on single-operator phase distribution.
Aims
While AI has been successfully implemented in detecting and characterizing colonic polyps, its role in therapeutic endoscopy remains to be elucidated. Especially third space endoscopy procedures like ESD and peroral endoscopic myotomy (POEM) pose a technical challenge and the risk of operator-dependent complications like intraprocedural bleeding and perforation. Therefore, we aimed at developing an AI-algorithm for intraprocedural real time vessel detection during ESD and POEM.
Methods
A training dataset consisting of 5470 annotated still images from 59 full-length videos (47 ESD, 12 POEM) and 179681 unlabeled images was used to train a DeepLabV3+neural network with the ECMT semi-supervised learning method. Evaluation for vessel detection rate (VDR) and time (VDT) of 19 endoscopists with and without AI-support was performed using a testing dataset of 101 standardized video clips with 200 predefined blood vessels. Endoscopists were stratified into trainees and experts in third space endoscopy.
Results
The AI algorithm had a mean VDR of 93.5% and a median VDT of 0.32 seconds. AI support was associated with a statistically significant increase in VDR from 54.9% to 73.0% and from 59.0% to 74.1% for trainees and experts, respectively. VDT significantly decreased from 7.21 sec to 5.09 sec for trainees and from 6.10 sec to 5.38 sec for experts in the AI-support group. False positive (FP) readings occurred in 4.5% of frames. FP structures were detected significantly shorter than true positives (0.71 sec vs. 5.99 sec).
Conclusions
AI improved VDR and VDT of trainees and experts in third space endoscopy and may reduce performance variability during training. Further research is needed to evaluate the clinical impact of this new technology.
Aims
Artificial Intelligence (AI) systems in gastrointestinal endoscopy are narrow because they are trained to solve only one specific task. Unlike Narrow-AI, general AI systems may be able to solve multiple and unrelated tasks. We aimed to understand whether an AI system trained to detect, characterize, and segment early Barrett’s neoplasia (Barrett’s AI) is only capable of detecting this pathology or can also detect and segment other diseases like early squamous cell cancer (SCC).
Methods
120 white light (WL) and narrow-band endoscopic images (NBI) from 60 patients (1 WL and 1 NBI image per patient) were extracted from the endoscopic database of the University Hospital Augsburg. Images were annotated by three expert endoscopists with extensive experience in the diagnosis and endoscopic resection of early esophageal neoplasias. An AI system based on DeepLabV3+architecture dedicated to early Barrett’s neoplasia was tested on these images. The AI system was neither trained with SCC images nor had it seen the test images prior to evaluation. The overlap between the three expert annotations („expert-agreement“) was the ground truth for evaluating AI performance.
Results
Barrett’s AI detected early SCC with a mean intersection over reference (IoR) of 92% when at least 1 pixel of the AI prediction overlapped with the expert-agreement. When the threshold was increased to 5%, 10%, and 20% overlap with the expert-agreement, the IoR was 88%, 85% and 82%, respectively. The mean Intersection Over Union (IoU) – a metric according to segmentation quality between the AI prediction and the expert-agreement – was 0.45. The mean expert IoU as a measure of agreement between the three experts was 0.60.
Conclusions
In the context of this pilot study, the predictions of SCC by a Barrett’s dedicated AI showed some overlap to the expert-agreement. Therefore, features learned from Barrett’s cancer-related training might be helpful also for SCC prediction. Our results allow different possible explanations. On the one hand, some Barrett’s cancer features generalize toward the related task of assessing early SCC. On the other hand, the Barrett’s AI is less specific to Barrett’s cancer than a general predictor of pathological tissue. However, we expect to enhance the detection quality significantly by extending the training to SCC-specific data. The insight of this study opens the way towards a transfer learning approach for more efficient training of AI to solve tasks in other domains.
Aims
Endoscopic retrograde cholangiopancreaticography (ERCP) is the gold standard in the diagnosis as well as treatment of diseases of the pancreatobiliary tract. However, it is technically complex and has a relatively high complication rate. In particular, cannulation of the papillary ostium remains challenging. The aim of this study is to examine whether a deep-learning algorithm can be used to detect the major duodenal papilla and in particular the papillary ostium reliably and could therefore be a valuable tool for inexperienced endoscopists, particularly in training situation.
Methods
We analyzed a total of 654 retrospectively collected images of 85 patients. Both the major duodenal papilla and the ostium were then segmented. Afterwards, a neural network was trained using a deep-learning algorithm. A 5-fold cross-validation was performed. Subsequently, we ran the algorithm on 5 prospectively collected videos of ERCPs.
Results
5-fold cross-validation on the 654 labeled data resulted in an F1 value of 0.8007, a sensitivity of 0.8409 and a specificity of 0.9757 for the class papilla, and an F1 value of 0.5724, a sensitivity of 0.5456 and a specificity of 0.9966 for the class ostium. Regardless of the class, the average F1 value (class papilla and class ostium) was 0.6866, the sensitivity 0.6933 and the specificity 0.9861. In 100% of cases the AI-detected localization of the papillary ostium in the prospectively collected videos corresponded to the localization of the cannulation performed by the endoscopist.
Conclusions
In the present study, the neural network was able to identify the major duodenal papilla with a high sensitivity and high specificity. In detecting the papillary ostium, the sensitivity was notably lower. However, when used on videos, the AI was able to identify the location of the subsequent cannulation with 100% accuracy. In the future, the neural network will be trained with more data. Thus, a suitable tool for ERCP could be established, especially in the training situation.