A deep learning model for diagnosis of inherited retinal diseases

A deep learning model for diagnosis of inherited retinal diseases

Abstract 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...

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Main Authors: 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
Format: Article
Language:English
Published: Nature Portfolio 2025-07-01
Series:Scientific Reports
Subjects:
Artificial intelligence
Machine learning algorithms
Deep learning architectures
Inherited retinal diseases
Retinitis pigmentosa
Stargardt disease
Online Access:https://doi.org/10.1038/s41598-025-04648-3
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Summary:Abstract 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.
ISSN:2045-2322

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