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Automated machine learning–based classification of proliferative and non-proliferative diabetic retinopathy using optical coherence tomography angiography vascular density maps

  • Retinal Disorders
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Abstract

Purpose

The study aims to classify the eyes with proliferative diabetic retinopathy (PDR) and non-proliferative diabetic retinopathy (NPDR) based on the optical coherence tomography angiography (OCTA) vascular density maps using a supervised machine learning algorithm.

Methods

OCTA vascular density maps (at superficial capillary plexus (SCP), deep capillary plexus (DCP), and total retina (R) levels) of 148 eyes from 78 patients with diabetic retinopathy (45 PDR and 103 NPDR) was used to classify the images to NPDR and PDR groups based on a supervised machine learning algorithm known as the support vector machine (SVM) classifier optimized by a genetic evolutionary algorithm.

Results

The implemented algorithm in three different models reached up to 85% accuracy in classifying PDR and NPDR in all three levels of vascular density maps. The deep retinal layer vascular density map demonstrated the best performance with a 90% accuracy in discriminating between PDR and NPDR.

Conclusions

The current study on a limited number of patients with diabetic retinopathy demonstrated that a supervised machine learning–based method known as SVM can be used to differentiate PDR and NPDR patients using OCTA vascular density maps.

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Data availability

The data generated during or/and analyzed during the current study are available from the corresponding author.

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Authors and Affiliations

  1. Retina Service, Eye Research Center, Farabi Eye Hospital, Tehran University of Medical Sciences, Qazvin Square, South Karegar Street, Tehran, Iran

    Elias Khalili Pour, Seyed Mohammad Mirshahvalad, Behzad Jafari, Kaveh Fadakar, Hooshang Faghihi, Ahmad Mirshahi, Fariba Ghassemi, Nazanin Ebrahimiadib, Masoud Mirghorbani, Fatemeh Bazvand & Hamid Riazi-Esfahani

  2. Department of Biomedical Engineering, Meybod University, Meybod, Iran

    Khosro Rezaee

  3. Faculty of Mathematical Sciences and Computer, Kharazmi University, Tehran, Iran

    Hossein Azimi

  4. Department of Ophthalmology, Gavin Herbert Eye Institute, University of California Irvine, Irvine, CA, USA

    Mohammad Riazi Esfahani

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  1. Elias Khalili Pour
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Correspondence to Hamid Riazi-Esfahani.

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All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional review board of Tehran University of Medical Sciences and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards (IRB Code: IR.TUMS.REC.1399.019).

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Khalili Pour, E., Rezaee, K., Azimi, H. et al. Automated machine learning–based classification of proliferative and non-proliferative diabetic retinopathy using optical coherence tomography angiography vascular density maps. Graefes Arch Clin Exp Ophthalmol 261, 391–399 (2023). https://doi.org/10.1007/s00417-022-05818-z

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