Impact of acquisition area on deep-learning-based glaucoma detection in different plexuses in OCTA

Glaucoma is a group of neurodegenerative diseases that can lead to irreversible blindness. Yet, the progression can be slowed down if diagnosed and treated early enough. Optical coherence tomography angiography (OCTA) can non-invasively provide valuable information about the retinal microcirculation that has shown to be correlated with the onset of the disease. The vessel density (VD) is the most commonly used biomarker to quantify this vascular information. However, different studies showed that there is a great impact of the acquisition area on the performance of the VD to distinguish between glaucoma patients and a healthy control group. It also seems that the separate capillary plexuses are differently affected by the disease and therefore also influence the results. So in this study we investigate the impact of the acquisition area (3 <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mo>×</mml:mo></mml:math> 3 <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mtext>mm</mml:mtext></mml:math> macular scan, 6.44 <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mo>×</mml:mo></mml:math> 6.4 <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mtext>mm</mml:mtext></mml:math> macular scan, 6 <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mo>×</mml:mo></mml:math> 6 <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mtext>mm</mml:mtext></mml:math> optic nerve head (ONH) scan) and the different plexuses on the machine-learning-based distinction between glaucoma patients and healthy controls. The results yielded that the 6 <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mo>×</mml:mo></mml:math> 6 <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mtext>mm</mml:mtext></mml:math> ONH show the best performance over all plexuses. Moreover the deep learning-based approach outperforms the VD as a biomarker on every acquisition area and plexus. In addition to that, it also performs better than traditional biomarkers obtained from the OCT scans that are used in the clinical routine for diagnosis and progression tracking of glaucoma. Consequently, OCTA scans of the ONH might be a useful addition to OCT when studying glaucoma.

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Publication Details

Journal
Scientific Reports
Published
2024-09-02
DOI
https://doi.org/10.1038/s41598-024-71235-3
Citations
1
Primary Topic
Glaucoma and retinal disorders
Type
article
Field-Weighted Citation Impact
0.54

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OCT
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Impact of acquisition area on deep-learning-based glaucoma detection in different plexuses in OCTA

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1 citations
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article

Impact of acquisition area on deep-learning-based glaucoma detection in different plexuses in OCTA

Bettina Hohberger, Lennart Husvogt, Tobias Würfl, Julia Schottenhamml, Stefan B. Ploner, Christian Y. Mardin, Andreas Maier, Robert Lämmer
article en
1 citations

Abstract

Glaucoma is a group of neurodegenerative diseases that can lead to irreversible blindness. Yet, the progression can be slowed down if diagnosed and treated early enough. Optical coherence tomography angiography (OCTA) can non-invasively provide valuable information about the retinal microcirculation that has shown to be correlated with the onset of the disease. The vessel density (VD) is the most commonly used biomarker to quantify this vascular information. However, different studies showed that there is a great impact of the acquisition area on the performance of the VD to distinguish between glaucoma patients and a healthy control group. It also seems that the separate capillary plexuses are differently affected by the disease and therefore also influence the results. So in this study we investigate the impact of the acquisition area (3 <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mo>×</mml:mo></mml:math> 3 <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mtext>mm</mml:mtext></mml:math> macular scan, 6.44 <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mo>×</mml:mo></mml:math> 6.4 <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mtext>mm</mml:mtext></mml:math> macular scan, 6 <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mo>×</mml:mo></mml:math> 6 <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mtext>mm</mml:mtext></mml:math> optic nerve head (ONH) scan) and the different plexuses on the machine-learning-based distinction between glaucoma patients and healthy controls. The results yielded that the 6 <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mo>×</mml:mo></mml:math> 6 <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mtext>mm</mml:mtext></mml:math> ONH show the best performance over all plexuses. Moreover the deep learning-based approach outperforms the VD as a biomarker on every acquisition area and plexus. In addition to that, it also performs better than traditional biomarkers obtained from the OCT scans that are used in the clinical routine for diagnosis and progression tracking of glaucoma. Consequently, OCTA scans of the ONH might be a useful addition to OCT when studying glaucoma.

Scientific ReportsVol. 14(1)
Friedrich-Alexander-Universität Erlangen-Nürnberg (DE), Universitätsklinikum Erlangen (DE)
Universitätsklinikum Erlangen
Openalex Percentile: Top 33%
Glaucoma and retinal disorders
0.54
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