Retinal Imaging for Early Diagnosis and Patient Management

Retinal Imaging

for Early Diagnosis and Patient Management by Tan Sher Lynn

Retinal imaging involves the creation of a two-dimensional image of the three-dimensional retinal tissue. Such techniques are indispensable for diagnosis and management of the disease, allowing practitioners to view the condition directly and plan treatment accordingly.

During the 13 th Asia-Pacific Vitreoretina Society Congress (APVRS 2019) in Shanghai, China, last November, Dr. Srinivas Sadda presented the topic of “Wide Field Fundus Photography – Montage, Wide, Ultrawide, Which Is Better?”

According to Dr. Sadda, there has been recent innovation in the technology, instruments and modalities of widefield imaging. For example, the Monaco

(Optos, Dunfermline, Scotland) integrates OCT with ultra-widefield retinal imaging to feature color, autofluorescence and OCT scans for both eyes, in as little as two minutes. Meanwhile, confocal 4-color widefield imaging is a multimodal scanning laser ophthalmoscopy (cSLO), providing widefield color (red, green, blue and infrared), with retromode and OCT/OCTA imaging. He added that the rapid pace of advances in widefield imaging has necessitated the consideration of a new nomenclature for describing these images.

Each day, AI takes one step closer as a tool for disease diagnosis and management.

In his presentation “Autofluorescence Imaging: An Adjunct or a MustHave”, Dr. Frank G. Holz discussed the growing spectrum of applications for fundus autofluorescence (FAF), a relatively new imaging modality that focuses on the fluorescent properties of pigments in the retina to generate images; these images help doctors view various disease processes from a different perspective, aiding in the understanding of the pathophysiology of different retinal disorders.

“Autofluorescence imaging refines phenotyping and differential diagnosis of retinal diseases,” shared Dr. Holz.

“It helps in the identification of early disease stages to allow for earlier intervention and better outcomes, monitoring disease progression in the context of natural history and interventional studies, and the identification of prognostic biomarkers, as well as novel clinical endpoints closely linked to visual function. I think that it is a must-have adjunct to other screening modalities,” he explained.

It is a definite ‘yes’ for Dr. Min Wang, who presented “Multicolor Imaging — Better than Conventional Color Fundus Photo?” According to Dr. Wang, multicolor images are taken with the cSLO to capture three simultaneous reflection images with three individual laser wavelengths: blue, green and infrared. These different wavelengths of monochromatic light penetrate the retinal surface at different depths to show details at the various layers of the retina, generating a single multispectral, color-like high resolution and high contrast image by merging the information of these images.

“Multicolor scanning laser imaging is an innovative technology for fundus imaging, offering detail and clarity not available from traditional color fundus photographs (CFP). It has many clinical applications and in some cases may be used to replace conventional CFP. Further studies will need to validate the retinal details seen on multicolor imaging with conventional CFP and other imaging modalities,” he said. In his presentation, “Artificial Intelligence Applications on Color Images – Uncovering New Information”, Dr. Adnan Tufail noted that artificial intelligence (AI) and machine learning are already with us and will enhance our understanding of the retina by allowing quantification of humanidentifiable factors for association testing. Using sparse labels, we will also gain insight into novel anatomical associations that may enhance understanding of retinal disease or development.

Presenting on the topic “Predicting Cardiovascular Risk and Anemia from Fundus Photos Using AI”, Dr. Akinori Mitani explained that AI is the science of making things smart; machine learning refers to machines that learn from examples; and deep learning is a particular kind of machine learning. giving us a better understanding of what appears in retinal images and providing additional value to retinal image screening,” he said, adding that deep neural networks can be adapted for diabetic screening as well.

As advancements continue to develop in the field of retinal imaging, so will our understanding of ocular diseases. These exciting imaging modalities have transformed our knowledge in the molecular pathogenesis of retinal diseases and are playing an increasing role in the early diagnosis and management of patients. And now, with the growing incorporation of AI technology in imaging modalities, AI integration will eventually transform from a luxury into a necessity.

Editor’s Note:

“The deep learning system consists of layers of simple trainable mathematical units. It is highly accurate and learns features from raw data. Deep learning can help us find novel signals in medical images, such as cardiovascular risk factors and hemoglobin concentration in anemia,

The 13 th Asia-Pacific Vitreo-retina Society

Congress (APVRS 2019) was held in Shanghai, China, on November 22-24, 2019. Media MICE Pte Ltd, PIE magazine’s parent company was the Official Media Partner at APVRS 2019. Reporting for this story also took place at APVRS 2019.

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DME, diabetic macular edema; OCT, optical coherence tomography. 1. Nehmé A and Edelman J. Invest Ophthalmol Vis Sci 2008;49(5):2030–2038. 2. Holekamp N. The role of corticosteroid implants in DME. Available at: http://retinatoday.com/ 2015/04/the-role-of-corticosteroid-implants-in-dme. Accessed March 2020. 3. Campochiario PA et al. Am J Ophthalmol 2016;168:13–23. 4. Malclès A et al. Retina 2017;37(4):753–760. 5. Matonti F et al. Eur J Ophthamol 2016;26(5):454–459. 6. Aknin I and Melki L. Ophthalmolgica 2016;235:187–188. 7. Allergan. OZURDEX ® . Summary of Product Characteristics. October 2019. 8. Boyer SB et al. Ophthalmology 2014;121(10):1904–1914.

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