Data-Driven, Feature-Agnostic Deep Learning vs Retinal Nerve Fiber Layer Thickness for the Diagnosis of Glaucoma

Petersen, Christine A.; Mehta, Parmita; Lee, Aaron

doi:10.1001/jamaophthalmol.2019.6143

Cited by 5 publications

(4 citation statements)

References 6 publications

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“…In this study, we found that the novel biomarkers, extracted by our autoencoder network form segmented OCT images, could classify glaucoma and non-glaucoma eyes with high accuracy (diagnostic accuracy: 94% and AUC: 0.96). In the literature, several studies have reported glaucoma diagnosis using different techniques, e.g., the use MLCs with demographic or morphometric parameters [10,36,37,38], the use of supervised or unsupervised AI networks with visual field maps [39,40,41], color fundus images [25,42,43], RNFL maps [40,44], and 2D or 3D raw OCT scans [21,45]. To the best of our knowledge, no studies have yet reported a glaucoma diagnosis solely from segmented OCT images.…”

Section: Discussionmentioning

confidence: 99%

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Describing the Structural Phenotype of the Glaucomatous Optic Nerve Head Using Artificial Intelligence

Panda¹,

Cheong²,

Tun³

et al. 2020

Preprint

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The central event in glaucoma is slow and irreversible damage of retinal ganglion cell axons [1, 2]. Retinal ganglion cells carry visual information from the retina to the brain. When damaged, they undergo programmed cell death (apoptosis) resulting in vision loss. The main site of retinal ganglion cell damage occurs within the optic nerve head (ONH) located at the back of the eye [3]. To date, it is widely accepted that clinical evaluation and documentation of the ONH is essential for the diagnosis and the monitoring of glaucoma [4, 5].However, providing an accurate glaucoma diagnosis is a complex endeavor that is time consuming for clinicians. It is subjective and heavily dependent on a clinician's experience/expertise, and in more complex cases, requires a battery of multiple clinical tests. Such tests often need to be repeated at multiple patients' visits to overcome their inherent subjectivity and patient variability to confirm a diagnosis. As stated by the World Glaucoma Association: "As yet there is no widely-accepted method of combining the results of several [glaucoma] tests [to provide an improved diagnosis]". In fact, the use of multiple glaucoma diagnostic tests has been found to increase the likelihood of false-positives yielding to over-treatment [6].Axonal damage in glaucoma is typically evaluated through two tests that assess ONH structure and visual function. In routine clinical practice, functional measures of axonal loss are frequently assessed with visual field testing. During testing, flashes of light reach various regions of the retina to generate a map of its sensitivity to light. Structural measures of axonal loss are assessed with optical coherence tomography (OCT) -a 3D imaging modality that allows fast, high-resolution and non-invasive visualization of the ONH [7]. Using OCT, studies have found that retinal nerve fiber layer (RNFL) thinning was strongly associated with glaucoma severity [8, 9,10,11]. Because RNFL thinning generally precedes detectable functional defects in glaucoma patients [12], RNFL thickness (assessed through OCT) has remained a gold-standard parameter for glaucoma.However, Li and Jampel concluded that: "OCT was not a sufficient stand-alone test for the detection of glaucoma or for triage use in primary care" [7]. We argue this is because the vast amount of information available in a 3D OCT scan of the ONH has not been fully exploited. RNFL thickness and the more recent minimum-rim-width are 1-dimensional (depth) parameters representative of neural tissues only. However, connective tissues also exhibit complex 3-dimensional changes during the development and progression of glaucoma, including, but not limited to: post-laminar deformations [13], changes in LC and choroidal thickness [14,15], peripapillary atrophy [16], scleral canal expansion [17], posterior migration of the LC [18], and peripapillary scleral bowing [19]. To date, no clinical solutions currently consider changes in both neural and connective tissues simultaneously, which may also explain the lack in ...

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Section: Discussionmentioning

confidence: 99%

“…Currently, a popular trend for diagnosis is the use of CNN networks. These deep-learningbased approaches have proved their diagnostic efficacy with high AUC values [21,43,45,54]. These methods can also highlight the regions-of-importance of the ONH tissue through class activation maps (CAM) that are useful for diagnosis.…”

Section: Discussionmentioning

confidence: 99%

Describing the Structural Phenotype of the Glaucomatous Optic Nerve Head Using Artificial Intelligence

Panda¹,

Cheong²,

Tun³

et al. 2020

Preprint

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“…The segmentation-free DL algorithm is superior to the traditional RNFL thickness parameter in the diagnosis of glaucoma damage in OCT scanning, especially in the early stages of the disease. Petersen et al (61) also reported that the DL model for detecting glaucoma using non-segmented SD-OCT performed better than the RNFL thickness parameters extracted by automatic segmentation. Some reports have examined the role of OCT in diagnostic decisions when combined with other relevant clinical information.…”

Section: Rnflmentioning

confidence: 99%

The use of deep learning technology for the detection of optic neuropathy

Li¹,

Wan²

2022

Quant Imaging Med Surg

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“…This ability decreases the need for human input by eliminating manual segmentation and pre-processing. Recently, two studies reported that a feature agnostic CNN model performs better when processing unsegmented SD-OCT scans than segmented retinal layers 29 to detect glaucoma. Maetschke and colleagues28 proposed a DL model that can detect glaucoma directly from raw OCT volumes of the ONH using a 3D CNN with an AUC of 0.94.…”

Section: Introductionmentioning

confidence: 99%

Glaucoma Detection and Feature Visualization from OCT Images Using Deep Learning

Akter

Perry

Fletcher

et al. 2023

Preprint

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Purpose: In this paper, we aimed to clinically interpret Temporal-Superior-Nasal-Inferior-Temporal (TSNIT) retinal optical coherence tomography (OCT) images in a convolutional neural network (CNN) model to differentiate between normal and glaucomatous optic neuropathy. Methods: Three modified pre-trained deep learning (DL) models: SqueezeNet, ResNet18, and VGG16, were fine-tuned for transfer learning to visualize CNN features and detect glaucoma using 780 segmented and 780 raw TSNIT OCT B-scans of 370 glaucomatous and 410 normal images. The performance of the DL models was further investigated with Grad-CAM activation function to visualize which regions of the images are considered for the prediction of the two classes. Results: For glaucoma detection, VGG16 performed better than SqueezeNet and ResNet18 models, with the highest AUC (0.988) on validation data and accuracy of 93% for test data. Moreover, identical classification results were obtained from raw and segmented images. For feature localization, three models accurately identify the distinct retinal regions of the TSNIT images for glaucoma and normal eyes. Conclusion: This evidence-based result demonstrates the remarkable effectiveness of using raw TSNIT OCT B-scan for automated glaucoma detection using DL techniques which mitigates the black box problem of artificial intelligence (AI) and increases the transparency and reliability of the DL model for clinical interpretation. Moreover, the results imply that the raw TSNIT OCT scan can be used to detect glaucoma without any prior segmentation or pre-processing, which may be an attractive feature in large-scale screening applications.

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Data-Driven, Feature-Agnostic Deep Learning vs Retinal Nerve Fiber Layer Thickness for the Diagnosis of Glaucoma

Abstract: A statistical approach to the evaluation of covariate effects on the receiver operating characteristic curves of diagnostic tests in glaucoma.

Cited by 5 publications

References 6 publications

Describing the Structural Phenotype of the Glaucomatous Optic Nerve Head Using Artificial Intelligence

Describing the Structural Phenotype of the Glaucomatous Optic Nerve Head Using Artificial Intelligence

The use of deep learning technology for the detection of optic neuropathy

Glaucoma Detection and Feature Visualization from OCT Images Using Deep Learning

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