| Autor: |
Jong, M., Jaspers, T., Kusters, C., Jukema, J., Fockens, K., van Eijck van Heslinga, R., Boers, T., Van Der Sommen, F., De With, P., De Groof, J., Bergman, J. |
| Předmět: |
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| Zdroj: |
Endoscopy; 2024 Supplement 2, Vol. 56, pS79-S79, 1p |
| Abstrakt: |
This article discusses the potential challenges faced by artificial intelligence (AI) systems in accurately detecting Barrett's neoplasia, a condition affecting the esophagus, when exposed to varying image quality in community hospitals. The study developed a CADe (computer-aided detection) system using a high-quality training set and evaluated its performance on test sets with different image qualities. The results showed that the CADe system performed significantly worse on moderate and low-quality test sets compared to high-quality ones. However, the study also found that implementing robustness enhancing strategies, such as diversified training data and targeted data augmentation, improved the system's performance and decreased the performance drop on lower-quality test sets. The findings suggest that these strategies can increase the likelihood of successful implementation of AI systems in clinical practice. [Extracted from the article] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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