Factors Affecting Completion Rate and Bowel Cleansing Quality in a Colon Capsule Endoscopy Cohort: Comparing AI and Manual Evaluations

IF 3 4区 计算机科学 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC
Benedicte Schelde-Olesen, Gunnar Baatrup, Jan-Matthias Braun, Jürgen Herp, Lasse Kaalby, Anastasios Koulaouzidis, Thomas Bjørsum-Meyer, Ulrik Deding, the CareForColon2015 group and the AICE consortium
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Abstract

In the trial CareForColon2015 (CFC2015) we included more than 2000 colon cancer screening participants for colon capsule endoscopy (CCE). We detected distinct fluctuations in the rate of complete investigations defined as CCE with both complete transit and adequate bowel cleansing. We aimed to investigate possible contributing factors to incomplete investigations and compare fluctuations in manual and artificial intelligence (AI) generated cleansing evaluations. We retrieved CCE videos and relevant information from CFC2015. We considered the following factors as possible contributors to completion rate fluctuations: different outpatient clinics for CCE, individual nurses instructing participants, number of participants at the information session, timing of the last dose of bowel preparation, timing of capsule ingestion, time gap between the last dose of bowel preparation and capsule ingestion, manual CCE reader-cleansing evaluations. During the trial, the prokinetic prucalopride was added to the regimen. We compared the completion rates between different time periods of the trial. Monthly completion rates ranges from 56.6% to 75.7%. The fluctuations were caused by variation in cleansing quality and not in the proportion of complete transit. Early capsule ingestion showed increased odds of a complete investigation. The timing of the last dose of bowel preparation was associated with cleansing quality. Fluctuations in cleansing quality were detected by manual reading in all colonic segments but by the AI algorithm predominantly in the right colon. Timing of bowel preparation and capsule ingestion is an important factor for the bowel cleansing quality in CCE. Using AI for cleansing quality monitoring in a CCE cohort can be an important measure for early identification of changes.

Trial Registration: ClinicalTrials.gov: NCT04049357

Abstract Image

结肠胶囊内窥镜队列中影响完成率和肠道清洁质量的因素:比较人工智能和人工评估
在CareForColon2015 (CFC2015)试验中,我们纳入了2000多名结肠癌筛查参与者进行结肠胶囊内窥镜检查(CCE)。我们发现了完全检查率的明显波动,定义为CCE的完全运输和充分的肠道清洁。我们的目的是调查可能导致调查不完整的因素,并比较人工和人工智能(AI)生成的清洁评估的波动。我们从CFC2015中检索CCE视频和相关信息。我们考虑了以下因素作为完成人率波动的可能影响因素:CCE的不同门诊诊所、指导参与者的个别护士、参加信息会议的参与者人数、最后一次肠准备的时间、胶囊摄入的时间、最后一次肠准备和胶囊摄入之间的时间间隔、人工CCE读卡器清洁评估。在试验期间,原动力普鲁卡必利被添加到方案中。我们比较了试验不同时期的完成率。每月完成率为56.6%至75.7%。这种波动是由清洁质量的变化引起的,而不是由完全运输的比例引起的。早期胶囊摄入增加了完全调查的可能性。最后一次肠准备的时间与清洁质量有关。人工读取所有结肠段检测到清洁质量的波动,但人工智能算法主要在右结肠检测到清洁质量的波动。肠准备和胶囊摄入的时间是CCE中肠清洁质量的重要因素。在CCE队列中使用人工智能进行清洁质量监测可能是早期识别变化的重要措施。试验注册:ClinicalTrials.gov: NCT04049357
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来源期刊
International Journal of Imaging Systems and Technology
International Journal of Imaging Systems and Technology 工程技术-成像科学与照相技术
CiteScore
6.90
自引率
6.10%
发文量
138
审稿时长
3 months
期刊介绍: The International Journal of Imaging Systems and Technology (IMA) is a forum for the exchange of ideas and results relevant to imaging systems, including imaging physics and informatics. The journal covers all imaging modalities in humans and animals. IMA accepts technically sound and scientifically rigorous research in the interdisciplinary field of imaging, including relevant algorithmic research and hardware and software development, and their applications relevant to medical research. The journal provides a platform to publish original research in structural and functional imaging. The journal is also open to imaging studies of the human body and on animals that describe novel diagnostic imaging and analyses methods. Technical, theoretical, and clinical research in both normal and clinical populations is encouraged. Submissions describing methods, software, databases, replication studies as well as negative results are also considered. The scope of the journal includes, but is not limited to, the following in the context of biomedical research: Imaging and neuro-imaging modalities: structural MRI, functional MRI, PET, SPECT, CT, ultrasound, EEG, MEG, NIRS etc.; Neuromodulation and brain stimulation techniques such as TMS and tDCS; Software and hardware for imaging, especially related to human and animal health; Image segmentation in normal and clinical populations; Pattern analysis and classification using machine learning techniques; Computational modeling and analysis; Brain connectivity and connectomics; Systems-level characterization of brain function; Neural networks and neurorobotics; Computer vision, based on human/animal physiology; Brain-computer interface (BCI) technology; Big data, databasing and data mining.
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