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<p>To evaluate the performance of a multi-input deep learning (DL) model in detecting two common inherited retinal diseases (IRDs), i.e. retinitis pigmentosa (RP) and Stargardt disease (STGD), and differentiating them from healthy eyes. This cross-sectional study includes 391 cases, consisting of 158 subjects with RP, 62 patients with STGD, and 171 healthy individuals. The image dataset is publicly available at <a href="http://en.riovs.sbmu.ac.ir/Access-to-Inherited-Retinal-Diseases-Image-Bank">http://en.riovs.sbmu.ac.ir/Access-to-Inherited-Retinal-Diseases-Image-Bank</a>. Separate networks using the same hyperparameters were trained and tested on the dataset. Two single-input MobileNetV2 networks were employed for color fundus photography (CFP) and infrared (IR) images, and a multi-input MobileNetV2 network was applied using both imaging modalities simultaneously. The single-input MobileNetV2 achieved 94.44% diagnostic accuracy using CFP, and 94.44% accuracy employing IR images, respectively. The multi-input MobileNetV2 network outperformed both single-input networks with an accuracy of 96.3%. The impact of single-input and multi-input architectures was further evaluated on state-of-the-art neural network models and machine learning algorithms. The deep learning networks utilized in this study achieved high performance for detection of IRDs. Application of a multi-input network employing both CFP and IR image inputs improves the overall performance of the model and its diagnostic accuracy.</p>

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A deep learning model for diagnosis of inherited retinal diseases

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年7月2日

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  • Freshteh Jafarbeglou,
  • Hamid Ahmadieh,
  • Farnaz Soleimani,
  • Ali Karimi,
  • Narsis Daftarian,
  • Sahba Fekri,
  • Tahmineh Motevasseli,
  • Morteza Naderan,
  • Babak Kamali Doust Azad,
  • Abbas Sheikhtaheri,
  • Farid Khorrami,
  • Hemn Baghban Jaldian,
  • Kia Bayat,
  • Saeideh Shahbazi,
  • Mahdi Yazdanpanah,
  • Tahereh Sabbaghi,
  • Kourosh Sheibani,
  • Hadi Ghattan Kashani,
  • Masoud Shariat Panahi,
  • Hamideh Sabbaghi

摘要

To evaluate the performance of a multi-input deep learning (DL) model in detecting two common inherited retinal diseases (IRDs), i.e. retinitis pigmentosa (RP) and Stargardt disease (STGD), and differentiating them from healthy eyes. This cross-sectional study includes 391 cases, consisting of 158 subjects with RP, 62 patients with STGD, and 171 healthy individuals. The image dataset is publicly available at http://en.riovs.sbmu.ac.ir/Access-to-Inherited-Retinal-Diseases-Image-Bank. Separate networks using the same hyperparameters were trained and tested on the dataset. Two single-input MobileNetV2 networks were employed for color fundus photography (CFP) and infrared (IR) images, and a multi-input MobileNetV2 network was applied using both imaging modalities simultaneously. The single-input MobileNetV2 achieved 94.44% diagnostic accuracy using CFP, and 94.44% accuracy employing IR images, respectively. The multi-input MobileNetV2 network outperformed both single-input networks with an accuracy of 96.3%. The impact of single-input and multi-input architectures was further evaluated on state-of-the-art neural network models and machine learning algorithms. The deep learning networks utilized in this study achieved high performance for detection of IRDs. Application of a multi-input network employing both CFP and IR image inputs improves the overall performance of the model and its diagnostic accuracy.