van der Zander QEW, Schreuder RM, Thijssen A, Kusters CHJ, Dehghani N, Scheeve T, Winkens B, van der Ende - van Loon MCM, de With PHN, van der Sommen F, Masclee AAM, Schoon EJ. Artificial intelligence for characterization of diminutive colorectal polyps: A feasibility study comparing two computer-aided diagnosis systems. Artif Intell Gastrointest Endosc 2024; 5(1): 90574 [DOI: 10.37126/aige.v5.i1.90574]
Corresponding Author of This Article
Quirine Eunice Wennie van der Zander, MD, MSc, Researcher, Department of Gastroenterology and Hepatology, Maastricht University Medical Center, Postbus 5800, Maastricht 6202 AZ, Netherlands. q.vanderzander@maastrichtuniversity.nl
Research Domain of This Article
Gastroenterology & Hepatology
Article-Type of This Article
Prospective Study
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
Table 2 Baseline characteristics for colorectal polyps, n (%)
Colorectal polyps, n = 51
Location
Cecum
7 (13.7)
Ascending colon
8 (15.7)
Transverse colon
15 (29.4)
Descending colon
5 (9.8)
Sigmoid
10 (19.6)
Rectum
6 (11.8)
Size, mean (SD) [range]
2.8 (1.0) [2-5]
Morphology
Sessile (Paris Is)
45 (88.2)
Flat-elevated (Paris IIa)
6 (11.8)
Histopathology
Tubular adenoma, LGD
32 (62.7)
Tubulovillous adenoma, LGD
1 (2.0)
Sessile serrated lesion, no dysplasia
6 (11.8)
Hyperplastic polyp, no dysplasia
12 (23.5)
Resection technique – cold snare
51 (100.0)
Table 3 Diagnostic performances of artificial intelligence for ColoRectal polyps in different image enhancement modes
AI4CRP (n = 51)
BLI, % (95%CI)
HDWL, % (95%CI)
LCI, % (95%CI)
Multimodal imaging, % (95%CI)
Sensitivity
82.1 (0.66-0.92)
59.0 (0.42-0.74)
76.9 (0.60-0.88)
71.8 (0.55-0.84)
Specificity
75.0 (0.43-0.93)
91.7 (0.60-1.00)
83.3 (0.51-0.97)
91.7 (0.60-1.00)
PPV
91.4 (0.76-0.98)
95.8 (0.77-1.00)
93.8 (0.78-0.99)
96.6 (0.80-1.00)
NPV
56.3 (0.31-0.79)
40.7 (0.23-0.61)
52.6 (0.29-0.75)
50.0 (0.29-0.71)
Diagnostic accuracy
80.4 (0.66-0.90)
66.7 (0.52-0.79)
78.4 (0.64-0.88)
76.5 (0.62-0.87)
Table 4 Diagnostic performance of artificial intelligence for ColoRectal polyps, self-critical artificial intelligence for ColoRectal polyps, CAD EYE, and the endoscopist
Citation: van der Zander QEW, Schreuder RM, Thijssen A, Kusters CHJ, Dehghani N, Scheeve T, Winkens B, van der Ende - van Loon MCM, de With PHN, van der Sommen F, Masclee AAM, Schoon EJ. Artificial intelligence for characterization of diminutive colorectal polyps: A feasibility study comparing two computer-aided diagnosis systems. Artif Intell Gastrointest Endosc 2024; 5(1): 90574