Towards 'automated gonioscopy': a deep learning algorithm for 360° angle assessment by swept-source optical coherence tomography.
Porporato Natalia, Tun Tin A, Baskaran Mani, Wong Damon W K, Husain Rahat, Fu Huazhu, Sultana Rehena, Perera Shamira, Schmetterer Leopold, Aung Tin
AI Summary
A deep learning algorithm accurately detected gonioscopic angle closure from SS-OCT scans, offering a promising foundation for future automated, non-contact glaucoma screening.
Abstract
Aims
To validate a deep learning (DL) algorithm (DLA) for 360° angle assessment on swept-source optical coherence tomography (SS-OCT) (CASIA SS-1000, Tomey Corporation, Nagoya, Japan).
Methods
This was a reliability analysis from a cross-sectional study. An independent test set of 39 936 SS-OCT scans from 312 phakic subjects (128 SS-OCT meridional scans per eye) was analysed. Participants above 50 years with no previous history of intraocular surgery were consecutively recruited from glaucoma clinics. Indentation gonioscopy and dark room SS-OCT were performed. Gonioscopic angle closure was defined as non-visibility of the posterior trabecular meshwork in ≥180° of the angle. For each subject, all images were analysed by a DL-based network based on the VGG-16 architecture, for gonioscopic angle-closure detection. Area under receiver operating characteristic curves (AUCs) and other diagnostic performance indicators were calculated for the DLA (index test) against gonioscopy (reference standard).
Results
Approximately 80% of the participants were Chinese, and more than half were women (57.4%). The prevalence of gonioscopic angle closure in this hospital-based sample was 20.2%. After analysing a total of 39 936 SS-OCT scans, the AUC of the DLA was 0.85 (95% CI:0.80 to 0.90, with sensitivity of 83% and a specificity of 87%) to classify gonioscopic angle closure with the optimal cut-off value of >35% of circumferential angle closure.
Conclusions
The DLA exhibited good diagnostic performance for detection of gonioscopic angle closure on 360° SS-OCT scans in a glaucoma clinic setting. Such an algorithm, independent of the identification of the scleral spur, may be the foundation for a non-contact, fast and reproducible 'automated gonioscopy' in future.
MeSH Terms
Shields Classification
Key Concepts5
The deep learning algorithm (DLA) demonstrated an Area Under Receiver Operating Characteristic Curve (AUC) of 0.85 (95% CI: 0.80 to 0.90) for classifying gonioscopic angle closure with an optimal cut-off value of >35% of circumferential angle closure, achieving a sensitivity of 83% and a specificity of 87% when compared against gonioscopy as the reference standard.
The deep learning algorithm (DLA) exhibited good diagnostic performance for detection of gonioscopic angle closure on 360° SS-OCT scans in a glaucoma clinic setting, suggesting it may be the foundation for a non-contact, fast and reproducible 'automated gonioscopy' independent of scleral spur identification.
A deep learning algorithm (DLA) based on the VGG-16 architecture was validated for 360° angle assessment on swept-source optical coherence tomography (SS-OCT) (CASIA SS-1000, Tomey Corporation, Nagoya, Japan).
The independent test set for validating the deep learning algorithm consisted of 39,936 SS-OCT scans from 312 phakic subjects (128 SS-OCT meridional scans per eye) who were above 50 years with no previous history of intraocular surgery and were consecutively recruited from glaucoma clinics.
The prevalence of gonioscopic angle closure in this hospital-based sample of 312 phakic subjects was 20.2%.
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