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<p>Descemet membrane endothelial keratoplasty (DMEK) represents a pivotal advance in corneal transplantation, specifically targeting cases of endothelial dysfunction. Postoperative monitoring of endothelial cell density (ECD) is critical for graft health and patient outcomes. Therefore, this study explores the potential of artificial intelligence to automate corneal endothelial cell counting and density estimation in patients with DMEK using confocal microscopy images. We retrospectively train an encoder-decoder convolutional neural network for cell center detection using clinically annotated patient data. The trained model detects cell centers in images from unseen patients. The image processing techniques then define the region of interest and measure its size, enabling estimation of the density of the cell. The performance of the method is evaluated in DMEK patients at 1–18-month follow-up intervals. Our automatic method achieves a relative error of 23% compared to manual expert measurements. Expert ECD measurements from confocal and specular microscopy vary by 32% on average for the same patients. Our study provides a technical foundation for automated ECD estimation and has a potential to impact post-DMEK care. By enabling frequent ECD monitoring, we can detect graft complications earlier and tailor treatment using longitudinal ECD data.</p>

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Deep learning for assessing corneal endothelial cell density in patients after descemet membrane endothelial keratoplasty: towards improved evaluation

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年8月3日

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  • Emine Esra Karaca,
  • Anna Fabijańska,
  • Kasım Öztoprak,
  • Feyza Dicle Işık,
  • Ayça Bulut Ustael,
  • Özlem Evren Kemer,
  • Reza Hassanpour

摘要

Descemet membrane endothelial keratoplasty (DMEK) represents a pivotal advance in corneal transplantation, specifically targeting cases of endothelial dysfunction. Postoperative monitoring of endothelial cell density (ECD) is critical for graft health and patient outcomes. Therefore, this study explores the potential of artificial intelligence to automate corneal endothelial cell counting and density estimation in patients with DMEK using confocal microscopy images. We retrospectively train an encoder-decoder convolutional neural network for cell center detection using clinically annotated patient data. The trained model detects cell centers in images from unseen patients. The image processing techniques then define the region of interest and measure its size, enabling estimation of the density of the cell. The performance of the method is evaluated in DMEK patients at 1–18-month follow-up intervals. Our automatic method achieves a relative error of 23% compared to manual expert measurements. Expert ECD measurements from confocal and specular microscopy vary by 32% on average for the same patients. Our study provides a technical foundation for automated ECD estimation and has a potential to impact post-DMEK care. By enabling frequent ECD monitoring, we can detect graft complications earlier and tailor treatment using longitudinal ECD data.