Deep-learning Classifier With an Ultrawide-field Scanning Laser Ophthalmoscope Detects Glaucoma Visual Field Severity

Masumoto, Hiroki; Tabuchi, Hitoshi; Nakakura, Shunsuke; Ishitobi, Naofumi; Miki, Masayuki; Enno, Hiroki

doi:10.1097/ijg.0000000000000988

Cited by 52 publications

(33 citation statements)

References 20 publications

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“…Masumoto et al used 1379 Optomap images to detect glaucoma overall with 81.3% sensitivity and 80.2% specificity; values were higher for more severe glaucoma (Table 4). 76…”

Section: Optic Disc Imagingmentioning

confidence: 99%

Deep learning in ophthalmology: The technical and clinical considerations

Ting

Peng

Varadarajan

et al. 2019

Progress in Retinal and Eye Research

372

237

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“…Masumoto et al used 1379 Optomap images to detect glaucoma overall with 81.3% sensitivity and 80.2% specificity; values were higher for more severe glaucoma (Table 4). 76…”

Section: Optic Disc Imagingmentioning

confidence: 99%

Deep learning in ophthalmology: The technical and clinical considerations

Ting

Peng

Varadarajan

et al. 2019

Progress in Retinal and Eye Research

372

237

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“…However, most of those techniques focused on classifying glaucoma are based on information from visual field measurements, fundus camera images, or measurements of retinal nerve fiber layer ( rnfl ) thickness. The use of slo images, which are usually captured during the optical coherence tomography ( oct ) acquisition, for glaucoma classification using deep learning ( dl ) methods has not received, until recently, as much attention [ 15 , 16 ].…”

Section: Introductionmentioning

confidence: 99%

Glaucoma classification based on scanning laser ophthalmoscopic images using a deep learning ensemble method

et al. 2021

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This study aimed to assess the utility of optic nerve head (onh) en-face images, captured with scanning laser ophthalmoscopy (slo) during standard optical coherence tomography (oct) imaging of the posterior segment, and demonstrate the potential of deep learning (dl) ensemble method that operates in a low data regime to differentiate glaucoma patients from healthy controls. The two groups of subjects were initially categorized based on a range of clinical tests including measurements of intraocular pressure, visual fields, oct derived retinal nerve fiber layer (rnfl) thickness and dilated stereoscopic examination of onh. 227 slo images of 227 subjects (105 glaucoma patients and 122 controls) were used. A new task-specific convolutional neural network architecture was developed for slo image-based classification. To benchmark the results of the proposed method, a range of classifiers were tested including five machine learning methods to classify glaucoma based on rnfl thickness—a well-known biomarker in glaucoma diagnostics, ensemble classifier based on inception v3 architecture, and classifiers based on features extracted from the image. The study shows that cross-validation dl ensemble based on slo images achieved a good discrimination performance with up to 0.962 of balanced accuracy, outperforming all of the other tested classifiers.

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“…This process is time-consuming and costly, but also makes ophthalmology one of the specialities particularly well-suited to DL techniques and its real-world application. The application of DL to ophthalmic images, such as digital fundus photographs and visual fields, has been reported to achieve the automated screening and diagnosis of common vision-threatening diseases, including diabetic retinopathy (DR) ( Abramoff et al, 2016 ; Gulshan et al, 2016 ; Raumviboonsuk et al, 2019 ; Ting et al, 2017 ), glaucoma ( Liu et al, 2019 ; Li et al, 2018 ; Masumoto et al, 2018a ; Asaoka et al, 2016 ), age-related macular degeneration (AMD) ( Grassmann et al, 2018 ; Burlina et al, 2017 ) and retinopathy of prematurity (ROP) ( Brown et al, 2018 ) with high accuracy. As such, DL may prove to be a valuable and viable adjunct to the existing diagnostic processes, and there may be a role for it to serve as an alternative to ophthalmologists and trained human image graders.…”

Section: Digital Technologymentioning

confidence: 99%

“…However, several limitations exist and further studies are required in this area. It can be difficult for DL, and humans, to classify glaucoma in the eyes with less severe disease manifestations or multiple comorbid eye conditions, especially high myopia ( Li et al, 2018 ; Masumoto et al, 2018a ), which requires a larger image database. Furthermore, in order to develop a more dependable screening method, other clinical parameters, including IOP, central corneal thickness and glaucoma genetic informativity biomarkers ( Craig et al, 2020 ) should be integrated.…”

Section: Digital Innovations For Eye Diseasesmentioning

confidence: 99%

Digital technology, tele-medicine and artificial intelligence in ophthalmology: A global perspective

Liu

Ting

et al. 2021

Progress in Retinal and Eye Research

397

273

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The simultaneous maturation of multiple digital and telecommunications technologies in 2020 has created an unprecedented opportunity for ophthalmology to adapt to new models of care using tele-health supported by digital innovations. These digital innovations include artificial intelligence (AI), 5th generation (5G) telecommunication networks and the Internet of Things (IoT), creating an inter-dependent ecosystem offering opportunities to develop new models of eye care addressing the challenges of COVID-19 and beyond. Ophthalmology has thrived in some of these areas partly due to its many image-based investigations. Tele-health and AI provide synchronous solutions to challenges facing ophthalmologists and healthcare providers worldwide. This article reviews how countries across the world have utilised these digital innovations to tackle diabetic retinopathy, retinopathy of prematurity, age-related macular degeneration, glaucoma, refractive error correction, cataract and other anterior segment disorders. The review summarises the digital strategies that countries are developing and discusses technologies that may increasingly enter the clinical workflow and processes of ophthalmologists. Furthermore as countries around the world have initiated a series of escalating containment and mitigation measures during the COVID-19 pandemic, the delivery of eye care services globally has been significantly impacted. As ophthalmic services adapt and form a “new normal”, the rapid adoption of some of telehealth and digital innovation during the pandemic is also discussed. Finally, challenges for validation and clinical implementation are considered, as well as recommendations on future directions.

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Deep-learning Classifier With an Ultrawide-field Scanning Laser Ophthalmoscope Detects Glaucoma Visual Field Severity

Abstract: Despite using an ultrawide-field scanning laser ophthalmoscope, DL can detect glaucoma characteristics and glaucoma visual field defect severity with high reliability.

Cited by 52 publications

References 20 publications

Deep learning in ophthalmology: The technical and clinical considerations

Deep learning in ophthalmology: The technical and clinical considerations

Glaucoma classification based on scanning laser ophthalmoscopic images using a deep learning ensemble method

Digital technology, tele-medicine and artificial intelligence in ophthalmology: A global perspective

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