Quantitative optical coherence tomography angiography: A review

Yao, Xincheng; Alam, Minhaj Nur; Le, David; Toslak, Devrim

doi:10.1177/1535370219899893

Cited by 69 publications

(71 citation statements)

References 156 publications

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“…Quantitative analysis of OCTA is essential to standardize objective interpretations of clinical outcomes. 257 Indeed, a concerted effort has been put forth to understand how Alzheimer's disease (AD) pathology may manifest in the retina as a means to assess the state of the AD brain. [258][259][260] OCTA has also been successfully evaluated as a tool to assess retinal changes in Parkinson's disease 261 and both schizophrenia and bipolar disorder.…”

Section: Optical Coherence Angiographymentioning

confidence: 99%

Enhanced medical diagnosis for dOCTors: a perspective of optical coherence tomography

et al. 2021

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Significance: After three decades, more than 75,000 publications, tens of companies being involved in its commercialization, and a global market perspective of about USD 1.5 billion in 2023, optical coherence tomography (OCT) has become one of the fastest successfully translated imaging techniques with substantial clinical and economic impacts and acceptance.Aim: Our perspective focuses on disruptive forward-looking innovations and key technologies to further boost OCT performance and therefore enable significantly enhanced medical diagnosis.Approach: A comprehensive review of state-of-the-art accomplishments in OCT has been performed. Results:The most disruptive future OCT innovations include imaging resolution and speed (single-beam raster scanning versus parallelization) improvement, new implementations for dual modality or even multimodality systems, and using endogenous or exogenous contrast in these hybrid OCT systems targeting molecular and metabolic imaging. Aside from OCT angiography, no other functional or contrast enhancing OCT extension has accomplished comparable clinical and commercial impacts. Some more recently developed extensions, e.g., optical coherence elastography, dynamic contrast OCT, optoretinography, and artificial intelligence enhanced OCT are also considered with high potential for the future. In addition, OCT miniaturization for portable, compact, handheld, and/or cost-effective capsule-based OCT applications, home-OCT, and self-OCT systems based on micro-optic assemblies or photonic integrated circuits will revolutionize new applications and availability in the near future. Finally, clinical translation of OCT including medical device regulatory challenges will continue to be absolutely essential.Conclusions: With its exquisite non-invasive, micrometer resolution depth sectioning capability, OCT has especially revolutionized ophthalmic diagnosis and hence is the fastest adopted imaging technology in the history of ophthalmology. Nonetheless, OCT has not been completely exploited and has substantial growth potential-in academics as well as in industry. This applies not only to the ophthalmic application field, but also especially to the original motivation of OCT to enable optical biopsy, i.e., the in situ imaging of tissue microstructure with a resolution approaching that of histology but without the need for tissue excision.

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Section: Optical Coherence Angiographymentioning

confidence: 99%

Enhanced medical diagnosis for dOCTors: a perspective of optical coherence tomography

et al. 2021

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“…An interesting fusion between mathematical modeling and AI seems possible with the advent of optical coherence tomography angiography (OCTA). OCTA demonstrates unparalleled ability to resolve the various plexiform vascular beds in the retina and generate quantitative data on vessel density, vascular tortuosity, foveal avascular zone area, 3 etc., enabling exciting possibilities of combined mathematical modeling and AI in understanding ophthalmic diseases with a retinal vascular component.…”

Section: Clinical Carementioning

confidence: 99%

What will AI Ophthalmology v2.0 look like?

Pasquale

2021

MAIO

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Artificial intelligence: from 'gee whiz' to 'wow'It is fair to say that the field of artificial intelligence (AI) has moved from the 'gee whiz' phase to the 'wow' phase. In the 'gee whiz' phase, a computer could be programmed to play checkers as well as a human player. Jumping ahead to the 'wow' phase, convoluted neural networks (CNNs) facilitate the detection of clinically significant levels of glaucomatous optic neuropathy, retinopathy of prematurity, and diabetic retinopathy with precision that rivals and even exceeds that of skilled clinicians. CNNs are computer algorithms that are organized in ways that allow for layered processing of data with a hierarchical architecture that mimics the cerebral visual cortex. The FDA has approved two AI algorithms -IDx 1 and EyeArt 2 -for their ability to detect referable diabetic retinopathy as well as a reading center manned by experts using robust clinical standards. The new journal, Modeling and Artificial Intelligence in Ophthalmology, will chronicle AI Ophthalmology version 2.0. What will AI Ophthalmology v2.0 look like?

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“…Examples include foveal avascular zone (FAZ) area, FAZ contour irregularity [13,25], vessel caliber, fractal dimension, tortuosity, density and geometric features of the vascular network [1,14,2,22]. Moreover, vessel density has been useful for identifying CKD [27] and both vessel density and perfusion density for Alzheimer's disease [31,30]. Example of Controls, DR and NoDR images are provided in Figure 1, where the NoDR label refers to diabetic patients without retinopathy.…”

Section: Introductionmentioning

confidence: 99%

DR Detection Using Optical Coherence Tomography Angiography (OCTA): A Transfer Learning Approach with Robustness Analysis

Andreeva

Fontanella

Giarratano

et al. 2020

Ophthalmic Medical Image Analysis

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OCTA imaging is an emerging modality for the discovery of retinal biomarkers in systemic disease. Several studies have already shown the potential of deep learning algorithms in the medical domain. However, they generally require large amount of manually graded images which may not always be available. In our study, we aim to investigate whether transfer learning can help in identifying patient status from a relatively small dataset. Additionally, we explore if data augmentation may help in improving our classification accuracy. Finally, for the first time, we propose a validation of our model on OCTA images acquired with a different device. OCTA scans from three different groups of participants were analysed: diabetic with and without retinopathy (DR and NoDR, respectively) and healthy subjects. We used the convolutional neural network architecture VGG16 and achieved 83.29% accuracy when classifying DR, NoDR and Controls. Our results demonstrate how transfer learning enables fairly accurate OCTA scan classification and augmentation based on geometric transformations helps in improving the classification accuracy further. Finally, we show how our model maintains consistent performance across OCTA imaging devices, without any re-training.

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Quantitative optical coherence tomography angiography: A review

Cited by 69 publications

References 156 publications

Enhanced medical diagnosis for dOCTors: a perspective of optical coherence tomography

Enhanced medical diagnosis for dOCTors: a perspective of optical coherence tomography

What will AI Ophthalmology v2.0 look like?

DR Detection Using Optical Coherence Tomography Angiography (OCTA): A Transfer Learning Approach with Robustness Analysis

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