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©The Author(s) 2025.
Artif Intell Gastroenterol. Nov 8, 2025; 6(3): 107528
Published online Nov 8, 2025. doi: 10.35712/aig.v6.i3.107528
Published online Nov 8, 2025. doi: 10.35712/aig.v6.i3.107528
Table 1 Basic characteristics of patients
| Variable | Total (n = 60) |
| Age (years), median (interquartile range) | 61.5 (20-76) |
| Age | |
| < 30 years | 4 (6.7) |
| 30-39 years | 4 (6.7) |
| 40-49 years | 6 (10.0) |
| 50-59 years | 13 (21.7) |
| 60-69 years | 23 (38.3) |
| > 70 years | 10 (16.7) |
| Gender | |
| Female | 30 (50.0) |
| Male | 30 (50.0) |
| Ethnicity | |
| Javanese | 28 (46.7) |
| Sundanese | 5 (8.3) |
| Minangkabau | 2 (3.3) |
| Batak | 9 (15.0) |
| Chinese | 9 (15.0) |
| Other | 7 (11.7) |
| Obesity | |
| Yes | 12 (20.0) |
| No | 48 (80.0) |
| Inflammatory bowel disease | |
| Yes | 18 (30.0) |
| No | 42 (70.0) |
| Diabetes mellitus | |
| Yes | 12 (20.0) |
| No | 48 (80.0) |
| Family history of polyps | |
| Yes | 8 (13.3) |
| No | 52 (86.7) |
| Family history of colorectal cancer | |
| Yes | 6 (10.0) |
| No | 54 (90.0) |
Table 2 Basic characteristics of patients based on the size, location, and morphology of polyps
| Variable | Total (n = 100) |
| Polyp size | |
| 1-5 mm | 56 (56) |
| 6-9 mm | 11 (11) |
| ≥ 10 mm | 33 (33) |
| Polyp location | |
| Rectosigmoid polyp | 51 (51) |
| Descending colon polyp | 17 |
| Transverse colon polyp | 8 (8) |
| Ascending colon polyp | 19 (19) |
| Cecum polyp | 5 |
| Morfologi polyp | |
| Sessile polyp | 75 |
| Semipedunculated polyp | 9 (9) |
| Pedunculated polyp | 16 (16) |
| Flat polyp | 0 (0) |
| Other polyp | 0 (0) |
Table 3 Comparison of artificial intelligence (computer-assisted diagnosis eye function) findings with histopathology results
| Examination result | Histopathology | |||
| Hyperplastic | Neoplastic | Total | ||
| Computer-assisted diagnosis eye | Hyperplastic | 57 | 7 | 64 |
| Neoplastic | 15 | 21 | 36 | |
| Total | 72 | 28 | 100 | |
| Sensitivity (%) | 79.17 | |||
| Specificity (%) | 75.00 | |||
| Positive predictive value (%) | 89.06 | |||
| Negative predictive value (%) | 58.33 | |||
| Accuracy | 78.00 | |||
Table 4 Comparison of artificial intelligence (computer-assisted diagnosis eye function) findings with histopathology results in diminutive polyps
| Examination result | Histopathology | |||
| Hyperplastic | Neoplastic | Total | ||
| Computer-assisted diagnosis eye | Hyperplastic | 44 | 2 | 46 |
| Neoplastic | 7 | 3 | 10 | |
| Total | 51 | 5 | 56 | |
| Sensitivity (%) | 86.27 | |||
| Specificity (%) | 60.00 | |||
| Positive predictive value (%) | 95.65 | |||
| Negative predictive value (%) | 30.00 | |||
| Accuracy | 83.93 | |||
Table 5 Comparison of artificial intelligence (computer-assisted diagnosis eye function) findings with histopathology results in non-diminutive polyps
| Examination result | Histopathology | |||
| Hyperplastic | Neoplastic | Total | ||
| Computer-assisted diagnosis eye | Hyperplastic | 13 | 5 | 18 |
| Neoplastic | 8 | 18 | 26 | |
| Total | 21 | 23 | 44 | |
| Sensitivity (%) | 61.90 | |||
| Specificity (%) | 78.26 | |||
| Positive predictive value (%) | 72.22 | |||
| Negative predictive value (%) | 69.23 | |||
| Accuracy | 70.45 | |||
Table 6 Comparison of artificial intelligence (computer-assisted diagnosis eye function) findings with histopathology results in left-sided colon polyps
| Examination result | Histopathology | |||
| Hyperplastic | Neoplastic | Total | ||
| Computer-assisted diagnosis eye | Hyperplastic | 41 | 3 | 44 |
| Neoplastic | 11 | 13 | 24 | |
| Total | 52 | 16 | 68 | |
| Sensitivity (%) | 78.85 | |||
| Specificity (%) | 81.25 | |||
| Positive predictive value (%) | 93.18 | |||
| Negative predictive value (%) | 54.17 | |||
| Accuracy | 79.41 | |||
Table 7 Comparison of artificial intelligence (computer-assisted diagnosis eye function) findings with histopathology results in right-sided colon polyps
| Examination result | Histopathology | |||
| Hyperplastic | Neoplastic | Total | ||
| Computer-assisted diagnosis eye | Hyperplastic | 16 | 4 | 20 |
| Neoplastic | 4 | 8 | 12 | |
| Total | 20 | 12 | 32 | |
| Sensitivity (%) | 80.00 | |||
| Specificity (%) | 66.67 | |||
| Positive predictive value (%) | 80.00 | |||
| Negative predictive value (%) | 66.67 | |||
| Accuracy | 75.00 | |||
Table 8 Comparison of artificial intelligence (computer-assisted diagnosis eye function) findings with histopathology results in sessile polyps
| Examination result | Histopathology | |||
| Hyperplastic | Neoplastic | Total | ||
| Computer-assisted diagnosis eye | Hyperplastic | 53 | 5 | 58 |
| Neoplastic | 12 | 5 | 17 | |
| Total | 65 | 10 | 75 | |
| Sensitivity (%) | 81.54 | |||
| Specificity (%) | 50.00 | |||
| Positive predictive value (%) | 91.38 | |||
| Negative predictive value (%) | 29.41 | |||
| Accuracy | 77.33 | |||
Table 9 Comparison of artificial intelligence (computer-assisted diagnosis eye function) findings with histopathology results in non-sessile polyps
| Examination result | Histopathology | |||
| Hyperplastic | Neoplastic | Total | ||
| Computer-assisted diagnosis eye | Hyperplastic | 4 | 2 | 6 |
| Neoplastic | 3 | 16 | 19 | |
| Total | 7 | 18 | 25 | |
| Sensitivity (%) | 57.14 | |||
| Specificity (%) | 88.89 | |||
| Positive predictive value (%) | 66.67 | |||
| Negative predictive value (%) | 84.21 | |||
| Accuracy | 80.00 | |||
- Citation: Asputra H, Maulahela H, Fauzi A, Rumende CM, Pitarini A, Sari NK, Shatri H. Diagnostic value of artificial intelligence computer-assisted diagnosis (computer assisted-diagnosis eye function) for colorectal polyps. Artif Intell Gastroenterol 2025; 6(3): 107528
- URL: https://www.wjgnet.com/2644-3236/full/v6/i3/107528.htm
- DOI: https://dx.doi.org/10.35712/aig.v6.i3.107528
