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©The Author(s) 2024.
Artif Intell Gastrointest Endosc. Mar 8, 2024; 5(1): 90574
Published online Mar 8, 2024. doi: 10.37126/aige.v5.i1.90574
Published online Mar 8, 2024. doi: 10.37126/aige.v5.i1.90574
Table 1 Baseline characteristics of patients, n (%)
Patients, n = 30 | ||
Gender, female | 13 (43.3) | |
Age in years, mean (SD) [range] | 65.8 (8.4) [50-78] | |
Indication colonoscopy | ||
Bowel cancer screening program | 15 (50.0) | |
Surveillance | 10 (33.3) | |
Symptoms | 5 (16.7) | |
Family history positive for CRC | 5 (16.7) | |
BBPS, mean (SD) | 6.6 (1.4) | |
Number of colorectal polyps per patient1 | ||
1 colorectal polyp | 15 (50.0) | |
2 colorectal polyps | 9 (30.0) | |
3 colorectal polyps | 6 (20.0) |
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
AI4CRP1, % (95%CI), n = 51 | Self-critical AI4CRP1, % (95%CI), n = 37 | CAD EYE, % (95%CI), n = 49 | Endoscopist alone2, % (95%CI), n = 47 | AI-assisted endoscopist2,3, % (95%CI), n = 49 | |
Sensitivity | 82.1 (0.66-0.92) | 89.7 (0.72-0.97) | 74.2 (0.55-0.87) | 97.4 (0.85-1.00) | 97.4 (0.85-1.00) |
Specificity | 75.0 (0.43-0.93) | 87.5 (0.47-0.99) | 100.0 (0.78-1.00) | 77.8 (0.40-0.96) | 90.9 (0.57-1.00) |
PPV | 91.4 (0.76-0.98) | 96.3 (0.79-1.00) | 100.0 (0.82-1.00) | 94.9 (0.81-0.99) | 97.4 (0.85-1.00) |
NPV | 56.3 (0.31-0.79) | 70.0 (0.35-0.92) | 69.2 (0.48-0.85) | 87.5 (0.47-0.99) | 90.9 (0.57-1.00) |
Diagnostic accuracy | 80.4 (0.66-0.90) | 89.2 (0.74-0.96) | 83.7 (0.70-0.92) | 93.6 (0.81-0.98) | 95.9 (0.85-0.99) |
- 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
- URL: https://www.wjgnet.com/2689-7164/full/v5/i1/90574.htm
- DOI: https://dx.doi.org/10.37126/aige.v5.i1.90574