Deep Learning in Anterior Segment OCT Optimizing Biometric Analysis and Scleral Spur Detection Abstract
superior accuracy in measurements and scleral
Swept-source anterior segment optical coherence
spur detection due to their ability to learn complex
tomography (AS-OCT) is increasingly becoming a central
relationships within large datasets. Second, DL offers
tool in ophthalmology, providing high-resolution, cross-
a significant gain in efficiency by automating previously
sectional images of the anterior chamber. Swept-source
manual tasks, allowing for faster analysis and improved
provides the depth and clarity that other types of OCT
workflow. Finally, DL methodologies provide an objective
and Scheimpflug imaging do not. However, the extraction
approach, potentially reducing the subjectivity inherent in
of quantitative biometric data and the identification of
human interpretation.
the scleral spur remain time-consuming and susceptible to high variability. This limitation hinders the widespread
This paper explores the potential of DL-powered AS-
adoption of quantitative AS-OCT analysis in anterior
OCT analysis to transform clinical practice. Using the
chamber angle and metrics assessment
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in clinical practice.
Platform (Heidelberg Engineering GmbH, Heidelberg, Germany) as an example,
Deep learning (DL) algorithms offer a
we showcase the performance of DL
revolutionary solution to this challenge.
algorithms in real-world clinical settings.
Recent studies have demonstrated
By demonstrating the feasibility and
that DL models can achieve expert-
benefits of DL integration, we pave
level performance in both quantitative
the way for a future where automated,
biometry and scleral spur detection
objective and efficient AS-OCT analysis
tasks. This paper delves into the
empowers clinicians to provide enhanced
application of DL for SS-OCT image
patient care.
analysis, specifically focusing on its ability to optimize these processes. The advantages of DL compared to traditional methods are multifaceted. First, DL algorithms can achieve
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