Scholarlink

<p>Retinitis pigmentosa (RP) is an inherited progressive retinal degeneration that shows symptoms of night blindness, visual field loss, declining of vision and eventually, blindness. Currently, gene therapy and retinal prosthesis are available, but the indication for these treatments is limited. In this study, we report on the development of a diagnostic and prognostic model for RP based on large-scale deep learning (DL) models pre-trained with fundus images. The EfficientNetB4 model performed best in diagnosing RP with an AUC of 0.94. The diagnosis of RP with this model is superior in cases with good vision. For visual prognosis, we applied machine learning survival analysis to DL-derived image features and clinical metadata, using a strict patient-level split to avoid data leakage. The hybrid model combining imaging and clinical data outperformed models based on either modality alone, especially in female patients. Time-dependent AUC analysis showed that prognostic performance was highest between 500 and 1400 days after examination. SHAP-based interpretability analysis revealed that the features contributing to RP diagnosis and those associated with prognosis were distinct. While our findings demonstrate the added value of fundus images in visual outcome prediction, further validation using external and multi-center datasets is necessary for clinical translation.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Leveraging large scale deep learning models for diagnosis and visual outcome prediction in retinitis pigmentosa

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年1月8日

查看全文
  • Tatsuya Nagai,
  • Koya Homma,
  • Yuto Kawamata,
  • Masahito Yoshihara,
  • Eiryo Kawakami,
  • Takayuki Baba

摘要

Retinitis pigmentosa (RP) is an inherited progressive retinal degeneration that shows symptoms of night blindness, visual field loss, declining of vision and eventually, blindness. Currently, gene therapy and retinal prosthesis are available, but the indication for these treatments is limited. In this study, we report on the development of a diagnostic and prognostic model for RP based on large-scale deep learning (DL) models pre-trained with fundus images. The EfficientNetB4 model performed best in diagnosing RP with an AUC of 0.94. The diagnosis of RP with this model is superior in cases with good vision. For visual prognosis, we applied machine learning survival analysis to DL-derived image features and clinical metadata, using a strict patient-level split to avoid data leakage. The hybrid model combining imaging and clinical data outperformed models based on either modality alone, especially in female patients. Time-dependent AUC analysis showed that prognostic performance was highest between 500 and 1400 days after examination. SHAP-based interpretability analysis revealed that the features contributing to RP diagnosis and those associated with prognosis were distinct. While our findings demonstrate the added value of fundus images in visual outcome prediction, further validation using external and multi-center datasets is necessary for clinical translation.