Predicting Visual Acuity by Using Machine Learning in Patients Treated for Neovascular Age-Related Macular Degeneration

Rohm, Markus; Tresp, Volker; Müller, Michael; Kern, Christoph; Manakov, Ilja; Weiß, M.; Sim, Dawn A; Priglinger, Siegfried; Keane, Pearse A.; Kortuem, Karsten

doi:10.1016/j.ophtha.2017.12.034

Cited by 91 publications

(72 citation statements)

References 43 publications

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“… 9 Most analyses of retinal images evaluate only one type of imaging modality, a major problem in scientific rigor, and use human graders or AI algorithms analyzing only one modality, such as OCT layers. 26 As a preliminary step to using AI to overlay the plethora of different types of retinal images and functional tests, we evaluated overlay methods using AI and conventional warping algorithms from different imaging modalities, optics, and cameras. Our eventual goal is to be able to overlay multiple platforms.…”

Section: Discussionmentioning

confidence: 99%

Artificial Intelligence for Automated Overlay of Fundus Camera and Scanning Laser Ophthalmoscope Images

Cavichini

Bartsch

et al. 2020

Trans. Vis. Sci. Tech.

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Purpose The purpose of this study was to evaluate the ability to align two types of retinal images taken on different platforms; color fundus (CF) photographs and infrared scanning laser ophthalmoscope (IR SLO) images using mathematical warping and artificial intelligence (AI). Methods We collected 109 matched pairs of CF and IR SLO images. An AI algorithm utilizing two separate networks was developed. A style transfer network (STN) was used to segment vessel structures. A registration network was used to align the segmented images to each. Neither network used a ground truth dataset. A conventional image warping algorithm was used as a control. Software displayed image pairs as a 5 × 5 checkerboard grid composed of alternating subimages. This technique permitted vessel alignment determination by human observers and 5 masked graders evaluated alignment by the AI and conventional warping in 25 fields for each image. Results Our new AI method was superior to conventional warping at generating vessel alignment as judged by masked human graders ( P < 0.0001). The average number of good/excellent matches increased from 90.5% to 94.4% with AI method. Conclusions AI permitted a more accurate overlay of CF and IR SLO images than conventional mathematical warping. This is a first step toward developing an AI that could allow overlay of all types of fundus images by utilizing vascular landmarks. Translational Relevance The ability to align and overlay imaging data from multiple instruments and manufacturers will permit better analysis of this complex data helping understand disease and predict treatment.

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Section: Discussionmentioning

confidence: 99%

Artificial Intelligence for Automated Overlay of Fundus Camera and Scanning Laser Ophthalmoscope Images

Cavichini

Bartsch

et al. 2020

Trans. Vis. Sci. Tech.

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“…AI has been applied to various adult ophthalmic diseases, including diabetic retinopathy [1,[74][75][76][77], AMD [78][79][80][81][82][83], sight-threatening retinal disease [2,[84][85][86][87][88][89], glaucoma [90][91][92], intraocular lens calculation [93], and keratoconus [94]. It has also been used for robotassisted repair of epiretinal membranes [95], retinal vessel segmentation [96][97][98][99], and systemic disease prediction from fundus images [100].…”

Section: Non-pediatric Applicationsmentioning

confidence: 99%

Artificial intelligence for pediatric ophthalmology

Reid

Eaton

2019

Current Opinion in Ophthalmology

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Purpose of reviewDespite the impressive results of recent artificial intelligence (AI) applications to general ophthalmology, comparatively less progress has been made toward solving problems in pediatric ophthalmology using similar techniques. This article discusses the unique needs of pediatric ophthalmology patients and how AI techniques can address these challenges, surveys recent applications of AI to pediatric ophthalmology, and discusses future directions in the field. Recent findingsThe most significant advances involve the automated detection of retinopathy of prematurity (ROP), yielding results that rival experts. Machine learning (ML) has also been successfully applied to the classification of pediatric cataracts, prediction of post-operative complications following cataract surgery, detection of strabismus and refractive error, prediction of future high myopia, and diagnosis of reading disability via eye tracking. In addition, ML techniques have been used for the study of visual development, vessel segmentation in pediatric fundus images, and ophthalmic image synthesis. SummaryAI applications could significantly benefit clinical care for pediatric ophthalmology patients by optimizing disease detection and grading, broadening access to care, furthering scientific discovery, and improving clinical efficiency. These methods need to match or surpass physician performance in clinical trials before deployment with patients. Due to widespread use of closed-access data sets and software implementations, it is difficult to directly compare the performance of these approaches, and reproducibility is poor. Open-access data sets and software implementations could alleviate these issues, and encourage further AI applications to pediatric ophthalmology.

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“…Ein besonders spannender Punkt bei der Anwendung von KI im Rahmen einer AMD ist die Fähigkeit der Prädiktion. Möglichkeiten, die sich hieraus ergeben, sind das individuelle Abschätzen des Progressionsrisikos der Erkrankung [51 -54], das Outcome nach Anti-VEGF-Injektionen [55,56] sowie den notwendigen Therapiebedarf an Anti-VEGF-Injektionen [57]. und Schmidt-Erfurth et al (2018) nutzten maschinelles Lernen zur Bestimmung des Progressionsrisiko einer frühen bzw.…”

Section: Prädiktionunclassified

“…Neben der rein morphologischen Progressionsbeurteilung können bei der exsudativen AMD mithilfe von künstlicher Intelligenz auch bereits Aussagen über den aktuellen Visus u. a. anhand von Parametern einer OCT-Aufnahme gemacht werden [55] oder sogar Aussagen über den Visus nach 3 bzw. 12 Monaten im Zusammenhang mit einer Anti-VEGF-Therapie getroffen werden [56].…”

Section: Prädiktionunclassified

Chancen von künstlicher Intelligenz und Big Data für die Diagnostik und Behandlung der altersabhängigen Makuladegeneration

Treder

Eter

2019

Klin Monatsbl Augenheilkd

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ZusammenfassungDie altersabhängige Makuladegeneration (AMD) ist der führende Grund für Erblindung in der westlichen Welt. Für die neovaskuläre Verlaufsform steht mit der intravitrealen Anti-VEGF-Injektion (VEGF: vascular endothelial growth factor) eine wirksame Therapie zur Verfügung. In den letzten Jahren haben die multimodale Bildgebung und die standardisierte elektronische Patientendokumentation geholfen, die Diagnostik und das Management der AMD-Patienten zu verbessern. Mit dem Aufkommen der künstlichen Intelligenz und Big Data ergeben sich in diesem Zusammenhang viele Chancen für die Zukunft. Dieser Artikel soll einen Überblick über mögliche Anwendungen geben.

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Predicting Visual Acuity by Using Machine Learning in Patients Treated for Neovascular Age-Related Macular Degeneration

Abstract: Machine learning allowed VA to be predicted for 3 months with a comparable result to VA measurement reliability. For a forecast after 12 months of therapy, VA prediction may help to encourage patients adhering to intravitreal therapy.

Cited by 91 publications

References 43 publications

Artificial Intelligence for Automated Overlay of Fundus Camera and Scanning Laser Ophthalmoscope Images

Artificial Intelligence for Automated Overlay of Fundus Camera and Scanning Laser Ophthalmoscope Images

Artificial intelligence for pediatric ophthalmology

Chancen von künstlicher Intelligenz und Big Data für die Diagnostik und Behandlung der altersabhängigen Makuladegeneration

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