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A new computer-aided diagnosis tool based on deep learning methods for automatic detection of retinal disorders from OCT images

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Abstract

Purpose

Early detection of retinal disorders using optical coherence tomography (OCT) images can prevent vision loss. Since manual screening can be time-consuming, tedious, and fallible, we present a reliable computer-aided diagnosis (CAD) software based on deep learning. Also, we made efforts to increase the interpretability of the deep learning methods, overcome their vague and black box nature, and also understand their behavior in the diagnosis.

Methods

We propose a novel method to improve the interpretability of the used deep neural network by embedding the rich semantic information of abnormal areas based on the ophthalmologists’ interpretations and medical descriptions in the OCT images. Finally, we trained the classification network on a small subset of the online publicly available University of California San Diego (UCSD) dataset with an overall of 29,800 OCT images.

Results

The experimental results on the 1000 test OCT images show that the proposed method achieves the overall precision, accuracy, sensitivity, and f1-score of 97.6%, 97.6%, 97.6%, and 97.59%, respectively. Also, the heat map images provide a clear region of interest which indicates that the interpretability of the proposed method is increased dramatically.

Conclusion

The proposed software can help ophthalmologists in providing a second opinion to make a decision, and primitive automated diagnoses of retinal diseases and even it can be used as a screening tool, in eye clinics. Also, the improvement of the interpretability of the proposed method causes to increase in the model generalization, and therefore, it will work properly on a wide range of other OCT datasets.

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Data availability

Ref Data underlying the results presented in this paper are available in [9, 15].

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Acknowledgements

The authors would like to thank Mr. Sajed Rakhshani of School of Advanced Technologies in Medicine, Medical Image and Signal Processing Research Center, Isfahan University of Medical Sciences, Isfahan, Iran, for his useful comments and valuable guidance throughout the research. This work was also supported by the Student Research Committee of Isfahan University of Medical Sciences under grant number 1401278.

Funding

This article has no associated award funding.

Author information

Authors and Affiliations

  1. Department of Bioelectrics and Biomedical Engineering, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Isfahan, Iran

    Reza Alizadeh Eghtedar & Alireza Vard

  2. Medical Image & Signal Processing Research Center, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Isfahan, Iran

    Alireza Vard

  3. Student Research Committee, Isfahan University of Medical Sciences, Isfahan, Iran

    Reza Alizadeh Eghtedar

  4. Department of Ophthalmology, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran

    Mohammad Malekahmadi & Alireza Peyman

  5. Isfahan Eye Research Center, Department of Ophthalmology, Isfahan University of Medical Sciences, Isfahan, Iran

    Mohammad Malekahmadi & Alireza Peyman

Authors
  1. Reza Alizadeh Eghtedar
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  2. Alireza Vard
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  3. Mohammad Malekahmadi
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  4. Alireza Peyman
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Contributions

AV and RAE designed and implemented methods, developed software tool, wrote the main manuscript text, and prepared all the table and figures. MM and AP verified our segmentation ground truths, evaluated the results, and gave helpful advises about the proposed software tool. All authors reviewed the manuscript.

Corresponding author

Correspondence to Alireza Vard.

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Conflict of interest

The authors have no relevant financial or non-financial interests to disclose.

Ethical approval

This study was approved by the ethics committees of Isfahan University of Medical Sciences (Iran) with Approval IR.MUI.RESEARCH.REC.1401.399. Any of the authors did not perform any studies with human participants or animals in this paper.

Consent to participation

This research does not directly involve human subjects, and only the online publicly available University of California San Diego (UCSD) dataset is used in this study.

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This study does not contain any individual person’s data.

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Alizadeh Eghtedar, R., Vard, A., Malekahmadi, M. et al. A new computer-aided diagnosis tool based on deep learning methods for automatic detection of retinal disorders from OCT images. Int Ophthalmol 44, 110 (2024). https://doi.org/10.1007/s10792-024-03033-9

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