Impact of standard enhancement settings of endoscopy systems on performance of endoscopic artificial intelligence systems Article Swipe
Martijn R. Jong
,
Carolus H. J. Kusters
,
Querijn N.E. van Bokhorst
,
Jelmer B. Jukema
,
Rixta A. H. van Eijck van Heslinga
,
Kiki Fockens
,
Britt B. S. L. Houwen
,
Tim J. M. Jaspers
,
Tim G. W. Boers
,
Manon van der Vlugt
,
Evelien Dekker
,
Fons van der Sommen
,
Peter H. N. de With
,
Albert J. de Groof
,
Jacques Bergman
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1055/a-2530-1845
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1055/a-2530-1845
Background Artificial intelligence (AI) systems in endoscopy are predominantly developed and tested using high-quality imagery from expert centers. However, their performance may be different when applied in clinical practice, partly due to the diversity in post-processing enhancement settings used in endoscopy units. We evaluated the impact of post-processing enhancement settings on AI performance and tested specific data augmentation strategies to mitigate performance loss. Methods We used a computer-aided detection (CADe) system for Barrett’s neoplasia (6223 images, 906 patients) and a computer-aided diagnosis (CADx) system for colorectal polyps (3288 images, 969 patients), both trained on datasets acquired with Olympus equipment and with limited variability in enhancement settings. The CAD systems were then tested across a wide range of test sets, which comprised the same images, but displayed with different enhancement settings. Both CAD systems were then retrained using image enhancement-based data augmentation. The performance of the adjusted CAD systems was evaluated on the same test sets. Results Both systems displayed substantial performance variability over a range of enhancement settings (CADe: 83 %–92 % sensitivity, 84 %–91 % specificity; CADx: 78 %–85 % sensitivity, 45 %–63 % specificity). After retraining, variability in sensitivity and specificity was reduced to 2 % (P < 0.001) and 1 %, respectively (P = 0.003) for CADe, and 2 % (P = 0.03) and 8 %, respectively (P = 0.19) for CADx. Conclusion The performance of endoscopic AI systems can vary substantially depending on post-processing enhancement settings of the endoscopy unit. Specific data augmentation can mitigate this performance loss.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1055/a-2530-1845
- OA Status
- hybrid
- Cited By
- 3
- References
- 13
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4406980248
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4406980248Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1055/a-2530-1845Digital Object Identifier
- Title
-
Impact of standard enhancement settings of endoscopy systems on performance of endoscopic artificial intelligence systemsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2025Year of publication
- Publication date
-
2025-01-30Full publication date if available
- Authors
-
Martijn R. Jong, Carolus H. J. Kusters, Querijn N.E. van Bokhorst, Jelmer B. Jukema, Rixta A. H. van Eijck van Heslinga, Kiki Fockens, Britt B. S. L. Houwen, Tim J. M. Jaspers, Tim G. W. Boers, Manon van der Vlugt, Evelien Dekker, Fons van der Sommen, Peter H. N. de With, Albert J. de Groof, Jacques BergmanList of authors in order
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https://doi.org/10.1055/a-2530-1845Publisher landing page
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YesWhether a free full text is available
- OA status
-
hybridOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.1055/a-2530-1845Direct OA link when available
- Concepts
-
Medicine, CAD, Artificial intelligence, Endoscopy, Computer-aided diagnosis, Retraining, Computer science, Radiology, Business, Engineering drawing, Engineering, International tradeTop concepts (fields/topics) attached by OpenAlex
- Cited by
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3Total citation count in OpenAlex
- Citations by year (recent)
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2025: 3Per-year citation counts (last 5 years)
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13Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.tested | 11, 54, 111 |
| abstract_inverted_index.units. | 41 |
| abstract_inverted_index.Methods | 63 |
| abstract_inverted_index.Olympus | 97 |
| abstract_inverted_index.Results | 155 |
| abstract_inverted_index.applied | 25 |
| abstract_inverted_index.imagery | 14 |
| abstract_inverted_index.images, | 75, 88, 123 |
| abstract_inverted_index.limited | 101 |
| abstract_inverted_index.reduced | 194 |
| abstract_inverted_index.systems | 4, 108, 132, 147, 157, 231 |
| abstract_inverted_index.trained | 92 |
| abstract_inverted_index.However, | 18 |
| abstract_inverted_index.Specific | 244 |
| abstract_inverted_index.acquired | 95 |
| abstract_inverted_index.adjusted | 145 |
| abstract_inverted_index.centers. | 17 |
| abstract_inverted_index.clinical | 27 |
| abstract_inverted_index.datasets | 94 |
| abstract_inverted_index.mitigate | 60, 248 |
| abstract_inverted_index.settings | 37, 49, 167, 239 |
| abstract_inverted_index.specific | 55 |
| abstract_inverted_index.comprised | 120 |
| abstract_inverted_index.depending | 235 |
| abstract_inverted_index.detection | 68 |
| abstract_inverted_index.developed | 9 |
| abstract_inverted_index.diagnosis | 81 |
| abstract_inverted_index.different | 23, 127 |
| abstract_inverted_index.displayed | 125, 158 |
| abstract_inverted_index.diversity | 33 |
| abstract_inverted_index.endoscopy | 6, 40, 242 |
| abstract_inverted_index.equipment | 98 |
| abstract_inverted_index.evaluated | 43, 149 |
| abstract_inverted_index.neoplasia | 73 |
| abstract_inverted_index.patients) | 77 |
| abstract_inverted_index.practice, | 28 |
| abstract_inverted_index.retrained | 135 |
| abstract_inverted_index.settings. | 105, 129 |
| abstract_inverted_index.Artificial | 1 |
| abstract_inverted_index.Background | 0 |
| abstract_inverted_index.Conclusion | 225 |
| abstract_inverted_index.colorectal | 85 |
| abstract_inverted_index.endoscopic | 229 |
| abstract_inverted_index.patients), | 90 |
| abstract_inverted_index.strategies | 58 |
| abstract_inverted_index.Barrett’s | 72 |
| abstract_inverted_index.enhancement | 36, 48, 104, 128, 166, 238 |
| abstract_inverted_index.performance | 20, 52, 61, 142, 160, 227, 250 |
| abstract_inverted_index.retraining, | 187 |
| abstract_inverted_index.sensitivity | 190 |
| abstract_inverted_index.specificity | 192 |
| abstract_inverted_index.substantial | 159 |
| abstract_inverted_index.variability | 102, 161, 188 |
| abstract_inverted_index.augmentation | 57, 246 |
| abstract_inverted_index.high-quality | 13 |
| abstract_inverted_index.intelligence | 2 |
| abstract_inverted_index.respectively | 204, 219 |
| abstract_inverted_index.sensitivity, | 172, 181 |
| abstract_inverted_index.specificity; | 176 |
| abstract_inverted_index.augmentation. | 140 |
| abstract_inverted_index.predominantly | 8 |
| abstract_inverted_index.specificity). | 185 |
| abstract_inverted_index.substantially | 234 |
| abstract_inverted_index.computer-aided | 67, 80 |
| abstract_inverted_index.post-processing | 35, 47, 237 |
| abstract_inverted_index.enhancement-based | 138 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 96 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 15 |
| citation_normalized_percentile.value | 0.94245892 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |