Background Reliable documentation is essential for maintaining quality standards in endoscopy;however, in clinical practice, report quality varies. We developed an artificial intelligence (AI)-base d prototype for the...
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Background Reliable documentation is essential for maintaining quality standards in endoscopy;however, in clinical practice, report quality varies. We developed an artificial intelligence (AI)-base d prototype for the measurement of withdrawal and intervention times, and automatic *** A multiclass deep learning algorithm distinguishing different endoscopic image content was trained with 10557 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-bas ed measurement;photodocumentation was compared for documented *** 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 fur-ther validation, the system may improve standardized re-port ing, while decreasing the workload created by routine documentation.
Rapid climate change or climate crisis is one of the most serious emergencies of the 21st century, accounting for highly impactful and irreversible changes worldwide. Climate crisis can also affect the epidemiology an...
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Rapid climate change or climate crisis is one of the most serious emergencies of the 21st century, accounting for highly impactful and irreversible changes worldwide. Climate crisis can also affect the epidemiology and disease burden of gastrointestinal diseases because they have a connection with environmental factors and nutrition. Gastrointestinal endoscopy is a highly intensive procedure with a significant contribution to greenhouse gas (GHG) emissions. Moreover, endoscopy is the third highest generator of waste in healthcare facilities with significant contributions to carbon footprint. The main sources of direct carbon emission in endoscopy are use of high-powered consumption devices (e.g. computers, anesthesia machines, wash machines for reprocessing, scope processors, and lighting) and waste production derived mainly from use of disposable devices. Indirect sources of emissions are those derived from heating and cooling of facilities, processing of histological samples, and transportation of patients and materials. Consequently, sustainable endoscopy and climate change have been the focus of discussions between endoscopy providers and professional societies with the aim of taking action to reduce environmental impact. The term "green endoscopy" refers to the practice of gastroenterology that aims to raise awareness, assess, and reduce endoscopys environmental impact. Nevertheless, while awareness has been growing, guidance about practical interventions to reduce the carbon footprint of gastrointestinal endoscopy are lacking. This review aims to summarize current data regarding the impact of endoscopy on GHG emissions and possible strategies to mitigate this phenomenon. Further, we aim to promote the evolution of a more sustainable "green endoscopy".
Background and study aims Barrett's esophagus (BE) with low-grade dysplasia (LGD) is considered usually endoscopically invisible and the endoscopic features are not well described. This study aimed to: 1) evaluate...
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Background and study aims Barrett's esophagus (BE) with low-grade dysplasia (LGD) is considered usually endoscopically invisible and the endoscopic features are not well described. This study aimed to: 1) evaluate the frequency of visible BE-LGD;2) compare rates of BE-LGD detection in the community versus a Barrett's referral unit (BRU);and 3) evaluate the endoscopic features of *** and methods This was a retrospective analysis of a prospectively observed cohort of 497 patients referred to a BRU with dysplastic BE between 2008 and 2022. BE-LGD was defined as confirmation of LGD by expert gastrointestinal pathologist(s). Endoscopy reports, images and histology reports were reviewed to evaluate the frequency of endoscopically identifiable BE-LGD and their endoscopic *** A total of 135 patients (27.2%) had confirmed BE-LGD, of whom 15 (11.1%) had visible LGD identified in the community. After BRU assessment, visible LGD was detected in 68 patients (50.4%). Three phenotypes were observed: (A) Non-visible LGD;(B) Elevated (Paris 0-IIa) lesions;and (C) Flat (Paris 0-IIb) lesions with abnormal mucosal and/or vascular patterns with clear demarcation from regular flat BE. The majority (64.7%) of visible LGD was flat lesions with abnormal mucosal and vascular patterns. Endoscopic detection of BE-LGD increased over time (38.7% (2009-2012) vs. 54.3% (2018-2022)).Conclusions In this cohort, 50.4% of true BE-LGD was endoscopically visible, with increased recognition endoscopically over time and a higher rate of visible LGD detected at a BRU when compared with the community. BRU assessment of BE-LGD remains crucial;however, improving endoscopy surveillance quality in the community is equally important.
In this paper, we present a new robotic system to assist visually impaired people in unknown indoor and outdoor environments. The robotic system, which is equipped with a visual sensor, laser range finders, speaker, g...
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In this paper, we present a new robotic system to assist visually impaired people in unknown indoor and outdoor environments. The robotic system, which is equipped with a visual sensor, laser range finders, speaker, gives visually impaired people information about the environment around them. The laser data are analyzed using the clustering technique, making it possible to detect obstacles, steps and stairs. By using the visual sensor, the system is able to distinguish between objects and humans. The PC analyses the sensors data and send information to the visually impaired people by natural language or beep signal. The usefulness of the proposed system is examined experimentally.
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