scholarly journals Colon capsule endoscopy versus CT colonography after incomplete colonoscopy. Application of artificial intelligence algorithms to identify complete colonic investigations

2020 ◽  
Vol 8 (7) ◽  
pp. 782-789 ◽  
Author(s):  
U Deding ◽  
J Herp ◽  
A‐L Havshoei ◽  
M Kobaek‐Larsen ◽  
MM Buijs ◽  
...  
Cancers ◽  
2020 ◽  
Vol 12 (11) ◽  
pp. 3367
Author(s):  
Ulrik Deding ◽  
Lasse Kaalby ◽  
Henrik Bøggild ◽  
Eva Plantener ◽  
Mie Kruse Wollesen ◽  
...  

Following incomplete colonoscopy (IC) patients often undergo computed tomography colonography (CTC), but colon capsule endoscopy (CCE) may be an alternative. We compared the completion rate, sensitivity and diagnostic yield for polyp detection from CCE and CTC following IC. A systematic literature search resulted in twenty-six studies. Extracted data included inter alia, complete/incomplete investigations and polyp findings. Pooled estimates of completion rates of CCE and CTC and complete colonic view rates (CCE reaching the most proximal point of IC) of CCE were calculated. Per patient diagnostic yields of CCE and CTC were calculated stratified by polyp sizes. CCE completion rate and complete colonic view rate were 76% (CI 95% 68–84%) and 90% (CI 95% 83–95%). CTC completion rate was 98% (CI 95% 96–100%). Diagnostic yields of CTC and CCE were 10% (CI 95% 7–15%) and 37% (CI 95% 30–43%) for any size, 13% (CI 95% 9–18%) and 21% (CI 95% 12–32%) for >5-mm and 4% (CI 95% 2–7%) and 9% (CI 95% 3–17%) for >9-mm polyps. No study performed a reference standard follow-up after CCE/CTC in individuals without findings, rendering sensitivity calculations unfeasible. The increased diagnostic yield of CCE could outweigh its slightly lower complete colonic view rate compared to the superior CTC completion rate. Hence, CCE following IC appears feasible for an introduction to clinical practice. Therefore, randomized studies investigating CCE and/or CTC following incomplete colonoscopy with a golden standard reference for the entire population enabling estimates for sensitivity and specificity are needed.


2018 ◽  
Vol 87 (6) ◽  
pp. AB471
Author(s):  
Shinichi Katsuki ◽  
Kenichi Utano ◽  
Tomoki Matsuda ◽  
Tomoki Fujita ◽  
Katsuhiko Mitsuzaki ◽  
...  

2014 ◽  
Vol 79 (2) ◽  
pp. 307-316 ◽  
Author(s):  
Konstantinos Triantafyllou ◽  
Nikos Viazis ◽  
Panagiotis Tsibouris ◽  
Georgios Zacharakis ◽  
Chryssostomos Kalantzis ◽  
...  

2015 ◽  
Vol 110 ◽  
pp. S623-S624
Author(s):  
Rabia Ali ◽  
David J. Hass ◽  
Ira Schmelkin ◽  
Toyia James-Stevenson ◽  
Jack A. Di Palma ◽  
...  

2014 ◽  
Vol 79 (6) ◽  
pp. 1029-1030 ◽  
Author(s):  
Carlos Fernandes ◽  
Rolando Pinho ◽  
Teresa Pinto Pais ◽  
Iolanda Ribeiro ◽  
João Carvalho

2015 ◽  
Vol 81 (5) ◽  
pp. AB381
Author(s):  
Grainne Holleran ◽  
Barry Hall ◽  
Mary Hussey ◽  
Deirdre Mcnamara

2011 ◽  
Vol 73 (4) ◽  
pp. AB439-AB440
Author(s):  
Konstantinos Triantafyllou ◽  
Nikos Viazis ◽  
Panagiotis Tsibouris ◽  
George Zacharakis ◽  
Cryssostomos Kalantzis ◽  
...  

2018 ◽  
Vol 6 (10) ◽  
pp. 1556-1562 ◽  
Author(s):  
Mary Hussey ◽  
Grainne Holleran ◽  
Roisin Stack ◽  
Neil Moran ◽  
Claudio Tersaruolo ◽  
...  

2021 ◽  
Vol 09 (08) ◽  
pp. E1264-E1268
Author(s):  
Miguel Mascarenhas Saraiva ◽  
João P. S. Ferreira ◽  
Hélder Cardoso ◽  
João Afonso ◽  
Tiago Ribeiro ◽  
...  

AbstractColon capsule endoscopy (CCE) is a minimally invasive alternative to conventional colonoscopy. Most studies on CCE focus on colorectal neoplasia detection. The development of automated tools may address some of the limitations of this diagnostic tool and widen its indications for different clinical settings. We developed an artificial intelligence model based on a convolutional neural network (CNN) for the automatic detection of blood content in CCE images. Training and validation datasets were constructed for the development and testing of the CNN. The CNN detected blood with a sensitivity, specificity, and positive and negative predictive values of 99.8 %, 93.2 %, 93.8 %, and 99.8 %, respectively. The area under the receiver operating characteristic curve for blood detection was 1.00. We developed a deep learning algorithm capable of accurately detecting blood or hematic residues within the lumen of the colon based on colon CCE images.


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