Artificial Intelligence Applied to Colonoscopy: Is It Time to Take a Step Forward?
Abstract
:Simple Summary
Abstract
1. Introduction
2. Computer-Aided Polyp Detection (CADe)
2.1. Computed Aid Quality Improvement (CAQ)
2.2. Computer-Aided Polyp Diagnosis (CADx)
2.3. Prediction of the Depth of Submucosal Invasion
2.4. Assessment of the Colon Preparation
2.5. Other Issues
3. Challenges, Drawbacks and Areas of Improvement
4. Conclusions and Future Directions
Supplementary Materials
Author Contributions
Funding
Conflicts of Interest
Abbreviations
AI | artificial intelligence |
CADe | computer-aided polyp detection |
CADx | computer-assisted diagnosis |
CRC | colorectal cancer |
ADR | adenoma detection rate |
ASGE | American Association of Gastrointestinal Endoscopy |
ESGE | European Society of Gastrointestinal Endoscopy |
PCCRC | postcolonoscopy CRC |
PDR | polyp detection rate |
RCTs | randomized controlled trials |
CC | conventional colonoscopy |
FP | false-positive detection |
HD | high detector |
LD | low detector |
CAQ | computer-aided quality improvement |
PIVI | Preservation and Incorporation of Valuable endoscopic Innovations |
NBI | narrow-band imaging |
BLI | blue-light imaging |
WLE | white-light endoscopy |
NICE | narrow-band imaging International Colorectal Endoscopic classification |
JNET | Japanese Expert Team NBI classification |
CNN | convolutional neural network |
BBPS | Boston Bowel Preparation Scale. |
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Studies (N) | Patients (N, Range) | Indications | ADR *, CADe ** vs. CC † (OR/RR §, 95% CI) | AADR ‡, CADe vs. CC (OR, 95% CI) | SSLDR #, CADe vs. CC (OR, 95% CI) | APC ¶, CADe vs. CC (OR, 95% CI) | |
---|---|---|---|---|---|---|---|
Shah S.J. 2022 [27] | 14 | 10,928 (230–2352) | Symptoms and screening | 1.52 (1.39–1.67), p = 0.04 | NR | NR | ≤10 mm: 2.29 (1.97–2.26), p < 0.001 >10 mm: 1.93 (1.18–3.16), p < 0.01 |
Huang D.2022 [23] | 10 | 6629 (128–1058) | Screening and surveillance | 1.45 (1.32–1.59), p < 0.001 | NR | NR | 1.66 (1.52–1.81), p < 0.001 |
Zhang Y. 2021 [28] | 7 | 5427 (369–1058) | Symptoms and screening | 1.72 (1.52–1.95), p < 0.001 | 0.70 (0.50–0.97), p = 0.03 | 0.87 (0.61–1.23), p = 0.43 | NR |
Nazarian S. 2021 [26] | 8 | 5577 (150–1058) | Symptoms and screening | 1.53 (1.32–1.77), p < 0.001 | NR | NR | NR |
Li J. 2021 [24] | 5 | 4311 (623–1058) | Symptoms and screening | 1.75 (1.52–2.01), p < 0.001 | NR | NR | NR |
Hassan C. 2021 [22] | 5 | 4354 (623–1058) | Symptoms, screening and surveillance | 1.44 (1.27–1.62), p < 0.01 | 1.35 (0.74–2.47), p = 0.33 | NR | Overall: 1.70 (1.53–1.89), p < 0.01 ≤5 mm: 1.69 (1.48–1.84), p < 0.000 6–10 mm: 1.44 (1.19–1.75), p < 0.000 >10 mm: 1.46 (1.04–2.06), p < 0.03 |
Deliwala S. 2021 [21] | 6 | 4996 (623–1058) | Symptoms and screening | 1.77 (1.50–2.08), p < 0.001 | NR | NR | NR |
Barua I. 2021 [20] | 5 | 4311 (623–1058) | Symptoms and screening | 1.52 (1.31–1.77), p < 0.001 | NR | NR | NR |
Ashat M. 2021 [19] | 6 | 5058 (659–1058) | Symptoms and screening | 1.76 (1.55–2.00), p < 0.001 | NR | NR | NR |
Mohan B.P. 2021 [25] | 6 | 4962 (623–1058) | Symptoms, screening and surveillance | 1.50 (1.30–1.72), p < 0.0001 | 1.00 (0.74–1.36), p = 0.93 | 1.29 (0.89–1.89), p = 0.18 | NR |
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Gimeno-García, A.Z.; Hernández-Pérez, A.; Nicolás-Pérez, D.; Hernández-Guerra, M. Artificial Intelligence Applied to Colonoscopy: Is It Time to Take a Step Forward? Cancers 2023, 15, 2193. https://doi.org/10.3390/cancers15082193
Gimeno-García AZ, Hernández-Pérez A, Nicolás-Pérez D, Hernández-Guerra M. Artificial Intelligence Applied to Colonoscopy: Is It Time to Take a Step Forward? Cancers. 2023; 15(8):2193. https://doi.org/10.3390/cancers15082193
Chicago/Turabian StyleGimeno-García, Antonio Z., Anjara Hernández-Pérez, David Nicolás-Pérez, and Manuel Hernández-Guerra. 2023. "Artificial Intelligence Applied to Colonoscopy: Is It Time to Take a Step Forward?" Cancers 15, no. 8: 2193. https://doi.org/10.3390/cancers15082193