Deep Learning Classification of Angle Closure based on Anterior Segment OCT

Shan, Jing; Li, Zhixi; Ma, Ping; Tun, Tin A.; Yonamine, Sean; Wu, Yuqi; Baskaran, Mani; Nongpiur, Monisha E.; Chen, Dake; Aung, Tin; Li, Shuning; He, Mingguang; Yang, Yangfan; Han, Ying

doi:10.1016/j.ogla.2023.06.011

Cited by 6 publications

(2 citation statements)

References 34 publications

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“…Several deep learning approaches for discrimination between open angle and angle closure have been proposed, [23][24][25][26][27][28] but it is unclear whether these models work better for detecting gonioscopic angle closure compared with non-deep learning approaches. The reported deep learning models are also limited by the fact that the anatomical features that characterise gonioscopic angle closure (ie, the degree and the extent of angle narrowing) are obscured in the 'black box'.…”

Section: Deep Learning Versus Non-deep Learning Approaches For Detect...mentioning

confidence: 99%

Diagnostic criteria of anterior segment swept-source optical coherence tomography to detect gonioscopic angle closure

Guo,

Zhang,

et al. 2024

Br J Ophthalmol

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AimsTo compare the diagnostic performance of 360° anterior segment optical coherence tomography assessment by applying normative percentile cut-offs versus iris trabecular contact (ITC) for detecting gonioscopic angle closure.MethodsIn this multicentre study, 394 healthy individuals were included in the normative dataset to derive the age-specific and angle location-specific normative percentiles of angle open distance (AOD500) and trabecular iris space area (TISA500) which were measured every 10° for 360°. 119 healthy participants and 170 patients with angle closure by gonioscopy were included in the test dataset to investigate the diagnostic performance of three sets of criteria for detection of gonioscopic angle closure: (1) the 10th and (2) the 5th percentiles of AOD500/TISA500, and (3) ITC (ie, AOD500/TISA500=0 mm/mm2). The number of angle locations with angle closure defined by each set of the criteria for each eye was used to generate the receiver operating characteristic (ROC) curve for the discrimination between gonioscopic angle closure and open angle.ResultsOf the three sets of diagnostic criteria examined, the area under the ROC curve was greatest for the 10th percentile of AOD500 (0.933), whereas the ITC criterion AOD500=0 mm showed the smallest area under the ROC (0.852) and the difference was statistically significant with or without adjusting for age and axial length (p<0.001). The criterion ≥90° of AOD500 below the 10th percentile attained the best sensitivity 87.6% and specificity 84.9% combination for detecting gonioscopic angle closure.ConclusionsApplying the normative percentiles of angle measurements yielded a higher diagnostic performance than ITC for detecting angle closure on gonioscopy.

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Section: Deep Learning Versus Non-deep Learning Approaches For Detect...mentioning

confidence: 99%

Diagnostic criteria of anterior segment swept-source optical coherence tomography to detect gonioscopic angle closure

Guo,

Zhang,

et al. 2024

Br J Ophthalmol

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“…In supervised learning, CNNs are trained on labeled datasets, while in unsupervised learning, unsupervised methods like autoencoders are utilized for feature extraction. Semisupervised learning, such as transfer learning, are also commonly described, as pre-trained models can be fine-tuned with smaller labeled datasets to improve performance [5,23]. However, a well-known attribute of CNNs is their inherent bias towards translationinvariant object recognition [24] which permits the interpretation of features outside of their spatial context [25], leaving models vulnerable to artifactual errors.…”

Section: Introductionmentioning

confidence: 99%

Multi-Dataset Comparison of Vision Transformers and Convolutional Neural Networks for Detecting Glaucomatous Optic Neuropathy from Fundus Photographs

Hwang,

Chen,

Han

et al. 2023

Bioengineering

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Glaucomatous optic neuropathy (GON) can be diagnosed and monitored using fundus photography, a widely available and low-cost approach already adopted for automated screening of ophthalmic diseases such as diabetic retinopathy. Despite this, the lack of validated early screening approaches remains a major obstacle in the prevention of glaucoma-related blindness. Deep learning models have gained significant interest as potential solutions, as these models offer objective and high-throughput methods for processing image-based medical data. While convolutional neural networks (CNN) have been widely utilized for these purposes, more recent advances in the application of Transformer architectures have led to new models, including Vision Transformer (ViT,) that have shown promise in many domains of image analysis. However, previous comparisons of these two architectures have not sufficiently compared models side-by-side with more than a single dataset, making it unclear which model is more generalizable or performs better in different clinical contexts. Our purpose is to investigate comparable ViT and CNN models tasked with GON detection from fundus photos and highlight their respective strengths and weaknesses. We train CNN and ViT models on six unrelated, publicly available databases and compare their performance using well-established statistics including AUC, sensitivity, and specificity. Our results indicate that ViT models often show superior performance when compared with a similarly trained CNN model, particularly when non-glaucomatous images are over-represented in a given dataset. We discuss the clinical implications of these findings and suggest that ViT can further the development of accurate and scalable GON detection for this leading cause of irreversible blindness worldwide.

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Generative Artificial Intelligence Enhancements for Reducing Image-based Training Data Requirements

Chen,

Han,

Duncan

et al. 2024

Ophthalmology Science

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Deep Learning Classification of Angle Closure based on Anterior Segment OCT

Cited by 6 publications

References 34 publications

Diagnostic criteria of anterior segment swept-source optical coherence tomography to detect gonioscopic angle closure

Diagnostic criteria of anterior segment swept-source optical coherence tomography to detect gonioscopic angle closure

Multi-Dataset Comparison of Vision Transformers and Convolutional Neural Networks for Detecting Glaucomatous Optic Neuropathy from Fundus Photographs

Generative Artificial Intelligence Enhancements for Reducing Image-based Training Data Requirements

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