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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引用次数: 0
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.
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.