Introduction Primary colonoscopy and fecal immunochemical test (FIT) are the most commonly used colorectal cancer (CRC) screening modalities. Colon capsule endoscopy (CCE) might be an alternative. Data on the performance of CCE as a CRC screening tool in a screening population remain scarce. This is the first systematic review to provide an overview of the applicability of CCE as a CRC screening tool. Methods A systematic search was conducted of literature published up to September 2020. Studies reporting on CRC screening by second-generation CCE in an average-risk screening population were included. Results 582 studies were identified and 13 were included, comprising 2485 patients. Eight studies used CCE as a filter test after a positive FIT result and five studies used CCE for primary screening. The polyp detection rate of CCE was 24 % – 74 %. For polyps > 6 mm, sensitivity of CCE was 79 % – 96 % and specificity was 66 % – 97 %. For polyps ≥ 10 mm, sensitivity of CCE was 84 % – 97 %, which was superior to computed tomographic colonography (CTC). The CRC detection rate for completed CCEs was 93 % (25/27). Bowel preparation was adequate in 70 % – 92 % of examinations, and completion rates varied from 57 % to 92 %, depending on the booster used. No CCE-related complications were described. Conclusion CCE appeared to be a safe and effective tool for the detection of CRC and polyps in a screening setting. Accuracy was comparable to colonoscopy and superior to CTC, making CCE a good alternative to colonoscopy in CRC screening programs, although completion rates require improvement.

Background and study aims Colon capsule endoscopy (CCE) has the potential to explore the entire gastrointestinal tract. The aim of this study was to assess the applicability of CCE as pan-endoscopy. Patients and methods Healthy participants received CCE with bowel preparation (bisacodyl, polyethylene electrolyte glycol (PEG) + ascorbic acid) and booster regimen (metoclopramide, oral sulfate solution (OSS)). For each segment of the gastrointestinal tract, the following quality parameters were assessed: cleanliness, transit times, reading times, patient acceptance and safety of the procedure. When all gastrointestinal segments had cleansing score good or excellent, cleanliness of the whole gastrointestinal tract was assessed as good. Participants’ expected and perceived burden was assessed by questionnaires and participants were asked to grade the procedure (scale 0–10). All serious adverse events (SAEs) were documented. Results A total of 451 CCE procedures were analyzed. A good cleansing score was achieved in the stomach in 69.6%, in the SB in 99.1 % and in the colon in 76.6 %. Cleanliness of the whole gastrointestinal tract was good in 52.8 % of the participants. CCE median transit time of the whole gastrointestinal tract was 583 minutes IQR 303–659). The capsule reached the descending colon in 94.7 %. Median reading time per procedure was 70 minutes (IQR 57–83). Participants graded the procedure with a 7.8. There were no procedure-related SAEs. Conclusions CCE as pan-endoscopy has shown to be a safe procedure with good patient acceptance. When cleanliness of all gastrointestinal segments per patient, completion rate and reading time will be improved, CCE can be applied as a good non-invasive alternative to evaluate the gastrointestinal tract.

Background and aims: The applicability of colon capsule endoscopy in daily practice is limited by the accompanying labor-intensive reviewing time and the risk of inter-observer variability. Automated reviewing of colon capsule endoscopy images using artificial intelligence could be timesaving while providing an objective and reproducible outcome. This systematic review aims to provide an overview of the available literature on artificial intelligence for reviewing colonic mucosa by colon capsule endoscopy and to assess the necessary action points for its use in clinical practice. Methods: A systematic literature search of literature published up to January 2022 was conducted using Embase, Web of Science, OVID MEDLINE and Cochrane CENTRAL. Studies reporting on the use of artificial intelligence to review second-generation colon capsule endoscopy colonic images were included. Results: 1017 studies were evaluated for eligibility, of which nine were included. Two studies reported on computed bowel cleansing assessment, five studies reported on computed polyp or colorectal neoplasia detection and two studies reported on other implications. Overall, the sensitivity of the proposed artificial intelligence models were 86.5–95.5% for bowel cleansing and 47.4–98.1% for the detection of polyps and colorectal neoplasia. Two studies performed per-lesion analysis, in addition to per-frame analysis, which improved the sensitivity of polyp or colorectal neoplasia detection to 81.3–98.1%. By applying a convolutional neural network, the highest sensitivity of 98.1% for polyp detection was found. Conclusion: The use of artificial intelligence for reviewing second-generation colon capsule endoscopy images is promising. The highest sensitivity of 98.1% for polyp detection was achieved by deep learning with a convolutional neural network. Convolutional neural network algorithms should be optimized and tested with more data, possibly requiring the set-up of a large international colon capsule endoscopy database. Finally, the accuracy of the optimized convolutional neural network models need to be confirmed in a prospective setting.

No abstract

No abstract