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<p>Artificial intelligence (AI) is increasingly used in gastrointestinal endoscopy for polyp detection and classification. However, most AI models are trained on images from multiple video processors, whereas clinical environments typically rely on a single processor. We developed EndoStyle, a StarGANv2-based style transfer system trained to mimic the visual characteristics of five different endoscopic processors. On a validation dataset, Fréchet Inception Distance and Learned Perceptual Image Patch Similarity indicated high visual fidelity and perceptual similarity across processors. Semantic similarity analysis using three foundation models confirmed that converted images were equally consistent with both content and style inputs. In a multicenter study, endoscopists considered real and converted images realistic at comparable rates. When used to augment polyp detection model training, synthetic images significantly improved precision and specificity, reducing false positives by over 40% on two distinct evaluation datasets. EndoStyle thus offers a practical solution for processor-specific AI generalization.</p>

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Gastrointestinal endoscopic image style transfer using EndoStyle to improve artificial intelligence prediction models

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年4月28日

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  • Joel Troya,
  • Ioannis Kafetzis,
  • Ronja Weber,
  • Yun Chiang,
  • Venkatesh Parayitam,
  • Philipp Sodmann,
  • Dieter Ziegler,
  • Frank Puppe,
  • Andreas Nüchter,
  • Alexander Meining,
  • Alexander Hann

摘要

Artificial intelligence (AI) is increasingly used in gastrointestinal endoscopy for polyp detection and classification. However, most AI models are trained on images from multiple video processors, whereas clinical environments typically rely on a single processor. We developed EndoStyle, a StarGANv2-based style transfer system trained to mimic the visual characteristics of five different endoscopic processors. On a validation dataset, Fréchet Inception Distance and Learned Perceptual Image Patch Similarity indicated high visual fidelity and perceptual similarity across processors. Semantic similarity analysis using three foundation models confirmed that converted images were equally consistent with both content and style inputs. In a multicenter study, endoscopists considered real and converted images realistic at comparable rates. When used to augment polyp detection model training, synthetic images significantly improved precision and specificity, reducing false positives by over 40% on two distinct evaluation datasets. EndoStyle thus offers a practical solution for processor-specific AI generalization.