A deep learning model for diagnosis of inherited retinal diseases Article Swipe
Freshteh Jafarbeglou
,
Hamid Ahmadieh
,
Farahnaz Soleimani
,
Ali Karimi
,
Narsis Daftarian
,
Sahba Fekri
,
Tahmineh Motevasseli
,
Morteza Naderan
,
Babak Kamali Doust Azad
,
Abbas Sheikhtaheri
,
Farid Khorrami
,
Hemn Baghban Jaldian
,
Kia Bayat
,
Shahla Shahbazi
,
Masoud Yazdanpanah
,
Tahereh Sabbaghi
,
Kourosh Sheibani
,
Hadi Ghattan Kashani
,
Masoud Shariat Panahi
,
Hamideh Sabbaghi
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1038/s41598-025-04648-3
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1038/s41598-025-04648-3
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.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1038/s41598-025-04648-3
- https://www.nature.com/articles/s41598-025-04648-3.pdf
- OA Status
- gold
- Cited By
- 2
- References
- 41
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4411857942
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- OpenAlex ID
-
https://openalex.org/W4411857942Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1038/s41598-025-04648-3Digital Object Identifier
- Title
-
A deep learning model for diagnosis of inherited retinal diseasesWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-07-01Full publication date if available
- Authors
-
Freshteh Jafarbeglou, Hamid Ahmadieh, Farahnaz Soleimani, Ali Karimi, Narsis Daftarian, Sahba Fekri, Tahmineh Motevasseli, Morteza Naderan, Babak Kamali Doust Azad, Abbas Sheikhtaheri, Farid Khorrami, Hemn Baghban Jaldian, Kia Bayat, Shahla Shahbazi, Masoud Yazdanpanah, Tahereh Sabbaghi, Kourosh Sheibani, Hadi Ghattan Kashani, Masoud Shariat Panahi, Hamideh SabbaghiList of authors in order
- Landing page
-
https://doi.org/10.1038/s41598-025-04648-3Publisher landing page
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-
https://www.nature.com/articles/s41598-025-04648-3.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.nature.com/articles/s41598-025-04648-3.pdfDirect OA link when available
- Concepts
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Computer science, Retinal, Artificial intelligence, Computational biology, Bioinformatics, Medicine, Optometry, Ophthalmology, BiologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
2Total citation count in OpenAlex
- Citations by year (recent)
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2025: 2Per-year citation counts (last 5 years)
- References (count)
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41Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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