Accuracy of ultra-wide-field fundus ophthalmoscopy-assisted deep learning, a machine-learning technology, for detecting age-related macular degeneration

Matsuba, Shinji; Tabuchi, Hitoshi; Ohsugi, Hideharu; Enno, Hiroki; Ishitobi, Naofumi; Masumoto, Hiroki; Kiuchi, Yoshiaki

doi:10.1007/s10792-018-0940-0

Cited by 77 publications

(50 citation statements)

References 25 publications

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“…There are studies in the ophthalmology literature on the use of fundus cameras and the suitability of the automatic diagnosis of retinal diseases. 58 Matsuba et al 59 reported in their study that patients with AMD could be detected with high sensitivity using deep learning and Optos imaging, without ophthalmological examination.…”

Section: Use Of Fundus Autofluorescence In Geographic Atrophymentioning

confidence: 99%

The Use of Fundus Autofluorescence in Dry Age-Related Macular Degeneration

Şahinoğlu-Keşkek¹,

Batıoğlu²

2021

tjo

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Fundus autofluorescence (FAF) has been a well-known imaging method for quite some time. However, with developing technologies and novel imaging devices, FAF is being used more often to diagnose and monitor retinal diseases. The density of lipofuscin (LF) and other fluorophores in the retina have a determining role in FAF images. In dry age-related macular degeneration (AMD), hyperautofluorescence is seen in cases of increasing LF in the retina pigment epithelium, whereas hypoautofluorescence is detected in decreasing LF resulting from geographic atrophy. In recent years, studies have shown that FAF images provide prognostic information in patients with AMD. This review aims to highlight the importance of FAF imaging in dry AMD.

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Section: Use Of Fundus Autofluorescence In Geographic Atrophymentioning

confidence: 99%

The Use of Fundus Autofluorescence in Dry Age-Related Macular Degeneration

Şahinoğlu-Keşkek¹,

Batıoğlu²

2021

tjo

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“…We use Gradient-weighted Class Activation Mapping (GradCAM) [14] to generate image-and label-wise explanations because it is a well-established method (e.g. [18,19,20]) and because it is generally faithful to how the explained model works. Other methods have been shown to be akin to simple edge detectors and to yield very similar heatmaps even when replacing the trained model weights with random weights [21,22].…”

Section: Image-wise Explanationsmentioning

confidence: 99%

Global explainability in aligned image modalities

Engelmann¹,

Storkey²,

Bernabéu³

2021

Preprint

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Deep learning (DL) models are very effective on many computer vision problems and increasingly used in critical applications. They are also inherently black box. A number of methods exist to generate image-wise explanations that allow practitioners to understand and verify model predictions for a given image. Beyond that, it would be desirable to validate that a DL model generally works in a sensible way, i.e. consistent with domain knowledge and not relying on undesirable data artefacts. For this purpose, the model needs to be explained globally. In this work, we focus on image modalities that are naturally aligned such that each pixel position represents a similar relative position on the imaged object, as is common in medical imaging. We propose the pixel-wise aggregation of image-wise explanations as a simple method to obtain label-wise and overall global explanations. These can then be used for model validation, knowledge discovery, and as an efficient way to communicate qualitative conclusions drawn from inspecting image-wise explanations. We further propose Progressive Erasing Plus Progressive Restoration (PEPPR) as a method to quantitatively validate that these global explanations are faithful to how the model makes its predictions. We then apply these methods to ultra-widefield retinal images, a naturally aligned modality. We find that the global explanations are consistent with domain knowledge and faithfully reflect the model's workings.Preprint. Under review.

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“…It has been shown that artificial intelligence can be used to develop algorithms that automatically detect retinal diseases, for example, AMD and DR. 32 The first algorithm to classify severity of DR received Federal Drug Administration (FDA) approval in the USA in 2018. 33 Tools for automatic OCT pattern recognition and referral decision-making have already been published and could be integrated into a cloud-based referral platform.…”

Section: Future Applications and Economic Impactmentioning

confidence: 99%

Implementation of a cloud-based referral platform in ophthalmology: making telemedicine services a reality in eye care

Kern

Kortuem

et al. 2019

Br J Ophthalmol

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BackgroundHospital Eye Services (HES) in the UK face an increasing number of optometric referrals driven by progress in retinal imaging. The National Health Service (NHS) published a 10-year strategy (NHS Long-Term Plan) to transform services to meet this challenge. In this study, we implemented a cloud-based referral platform to improve communication between optometrists and ophthalmologists.MethodsRetrospective cohort study conducted at Moorfields Eye Hospital, Croydon (NHS Foundation Trust, London, UK). Patients classified into the HES referral pathway by contributing optometrists have been included into this study. Main outcome measures was the reduction of unnecessary referrals.ResultsAfter reviewing the patient’s data in a web-based interface 54 (52%) out of 103 attending patients initially classified into the referral pathway did not need a specialist referral. Fourteen (14%) patients needing urgent treatment were identified. Usability was measured in duration for data input and reviewing which was an average of 9.2 min (median: 5.4; IQR: 3.4–8.7) for optometrists and 3.0 min (median: 3.0; IQR: 1.7–3.9) min for ophthalmologists. A variety of diagnosis was covered by this tool with dry age-related macular degeneration (n=34) being most common.ConclusionAfter implementation more than half of the HES referrals have been avoided. This platform offers a digital-first solution that enables rapid-access eye care for patients in community optometrists, facilitates communication between healthcare providers and may serve as a foundation for implementation of artificial intelligence.

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Accuracy of ultra-wide-field fundus ophthalmoscopy-assisted deep learning, a machine-learning technology, for detecting age-related macular degeneration

Abstract: A combination of DCNN with Optos images is not better than a medical examination; however, it can identify exudative AMD with a high level of accuracy. Our system is considered useful for screening and telemedicine.

Cited by 77 publications

References 25 publications

The Use of Fundus Autofluorescence in Dry Age-Related Macular Degeneration

The Use of Fundus Autofluorescence in Dry Age-Related Macular Degeneration

Global explainability in aligned image modalities

Implementation of a cloud-based referral platform in ophthalmology: making telemedicine services a reality in eye care

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