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Correlation of choroidal thickness with age in healthy subjects: automatic detection and segmentation using a deep learning model

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Abstract

Propose

The proposed deep learning model with a mask region-based convolutional neural network (Mask R-CNN) can predict choroidal thickness automatically. Changes in choroidal thickness with age can be detected with manual measurements. In this study, we aimed to investigate choroidal thickness in a comprehensive aspect in healthy eyes by utilizing the Mask R-CNN model.

Methods

A total of 68 eyes from 57 participants without significant ocular disease were recruited. The participants were allocated to one of three groups according to their age and underwent spectral domain optical coherence tomography (SD-OCT) or enhanced depth imaging OCT (EDI-OCT) centered on the fovea. Each OCT sequence included 25 slices. Physicians labeled the choroidal contours in all the OCT sequences. We applied the Mask R-CNN model for automatic segmentation. Comparisons of choroidal thicknesses were conducted according to age and prediction accuracy.

Results

Older age groups had thinner choroids, according to the automatic segmentation results; the mean choroidal thickness was 253.7 ± 41.9 μm in the youngest group, 206.8 ± 35.4 μm in the middle-aged group, and 152.5 ± 45.7 μm in the oldest group (p < 0.01). Measurements obtained using physician sketches demonstrated similar trends. We observed a significant negative correlation between choroidal thickness and age (p < 0.01). The prediction error was lower and less variable in choroids that were thinner than the cutoff point of 280 μm.

Conclusion

By observing choroid layer continuously and comprehensively. We found that the mean choroidal thickness decreased with age in healthy subjects. The Mask R-CNN model can accurately predict choroidal thickness, especially choroids thinner than 280 μm. This model can enable exploring larger and more varied choroid datasets comprehensively, automatically, and conveniently.

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Funding

All authors have no relevant financial or non-financial interests to disclose. No funding was received for conducting this study.

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Authors and Affiliations

  1. Department of Ophthalmology, Taichung Veterans General Hospital, No.1650, Sec. 4, Taiwan Blvd., Situn Dist., Taichung, Taiwan

    Chen Yu Lin, Wei Ping Hsia & Chia Jen Chang

  2. Department of Computer Science, Tunghai University, Taichung, Taiwan

    Yu Len Huang & Yang Wang

  3. Department of Optometry, Central Taiwan University of Science and Technology, Taichung, Taiwan

    Chia Jen Chang

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  1. Chen Yu Lin
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Correspondence to Chia Jen Chang.

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Conflict of interest

All authors declare that they have no affiliations with or involvement in any organization or entity with any financial interest (such as honoraria, educational grants, or participation in speakers’ bureaus) or non-financial interest (such as personal or professional relationships, affiliations, knowledge, or beliefs) in the subject matter or materials discussed in this manuscript.

Ethical approval

Approval was obtained from the Institutional Review Board (IRB) of Taichung Veteran General Hospital with case number: CE21201B. All procedures performed in this study involving human participants were in accordance with the ethical standards of IRB of Taichung Veteran General Hospital and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed consent

It is not necessary to obtain consent. Informed consent was granted exemption by ethical research committee of Taichung Veteran General Hospital. Images applied in the study were OCT sequence, which could be completely anonymized. The submission does not include information that may identify the participant.

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Lin, C.Y., Huang, Y.L., Hsia, W.P. et al. Correlation of choroidal thickness with age in healthy subjects: automatic detection and segmentation using a deep learning model. Int Ophthalmol 42, 3061–3070 (2022). https://doi.org/10.1007/s10792-022-02292-8

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  • DOI: https://doi.org/10.1007/s10792-022-02292-8

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