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<p>Keratitis is a major cause of visual impairment worldwide, with timely and accurate diagnosis being critical to preventing irreversible vision loss. Existing deep learning (DL) models rely heavily on high-quality slit-lamp (HQS) images, limiting their applicability in real-world clinical scenarios where low-quality slit-lamp (LQS) images are frequently encountered due to acquisition-related artifacts. Here, we propose an automated keratitis diagnosis framework for LQS images (AKDF-LQSI) that integrates image enhancement with lesion-aware classification. A random degradation algorithm is designed to simulate common clinical artifacts, enabling paired LQS–HQS training. The framework incorporates an improved generative adversarial network to restore diagnostic features and a context-aware classifier to enhance lesion representation. AKDF-LQSI demonstrates robust performance when evaluated on a multicenter dataset of 10,498 slit-lamp images, achieving an AUC exceeding 0.949. This approach presents a practical solution for keratitis diagnosis from degraded images, with strong potential to reduce diagnostic disparities in resource-limited environments.</p>

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Automated diagnosis of keratitis from low-quality slit-lamp images using an improved generative adversarial network

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月3日

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  • Jiewei Jiang,
  • Siyuan Song,
  • Qihai Yao,
  • Kuan Li,
  • Tongtong Lin,
  • Yu Xin,
  • Yiwei Cui,
  • Ning Wang,
  • Yifan Bai,
  • Xiaoci Luo,
  • Wei Chen,
  • Mingmin Zhu,
  • Lang He,
  • Zhongwen Li

摘要

Keratitis is a major cause of visual impairment worldwide, with timely and accurate diagnosis being critical to preventing irreversible vision loss. Existing deep learning (DL) models rely heavily on high-quality slit-lamp (HQS) images, limiting their applicability in real-world clinical scenarios where low-quality slit-lamp (LQS) images are frequently encountered due to acquisition-related artifacts. Here, we propose an automated keratitis diagnosis framework for LQS images (AKDF-LQSI) that integrates image enhancement with lesion-aware classification. A random degradation algorithm is designed to simulate common clinical artifacts, enabling paired LQS–HQS training. The framework incorporates an improved generative adversarial network to restore diagnostic features and a context-aware classifier to enhance lesion representation. AKDF-LQSI demonstrates robust performance when evaluated on a multicenter dataset of 10,498 slit-lamp images, achieving an AUC exceeding 0.949. This approach presents a practical solution for keratitis diagnosis from degraded images, with strong potential to reduce diagnostic disparities in resource-limited environments.