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Purpose: To evaluate the clinical usefulness of a deep learning-based detection device for multiple abnormal findings on retinal fundus photographs for readers with varying expertise.

Methods: Fourteen ophthalmologists (six residents, eight specialists) assessed 399 fundus images with respect to 12 major ophthalmologic findings, with or without the assistance of a deep learning algorithm, in two separate reading sessions. Sensitivity, specificity, and reading time per image were compared.

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Introduction: One of the most common causes of vision loss in the elderly population worldwide is age-related macular degeneration (AMD). Subsequently, the number of people affected by AMD is estimated to reach approximately 288 million by the year 2040. The aim of this study was to develop an model that simulates various aspects of the complex AMD pathogenesis.

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Purpose: To understand the microperimetry response characteristics of regions with a truly nonresponding location, which will be useful when considering criteria for end-stage atrophic age-related macular degeneration (AMD).

Methods: A simulation model was developed using data from 128 participants with bilateral large drusen at baseline seen over 36 months at 6-month intervals. One hundred thousand pairs of real-world microperimetry testing results were simulated separately with and without one truly nonresponding location, where the sensitivity of one randomly selected location for the former group was derived from the distribution of responses from a truly nonresponding location at the optic nerve head from 60 healthy participants.

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Purpose: In age-related macular degeneration (AMD), choriocapillaris flow deficits (CCFDs) under soft drusen can be measured using established compensation strategies. This study investigated whether CCFDs can be quantified under calcified drusen (CaD).

Methods: CCFDs were measured in normal eyes (n = 30) and AMD eyes with soft drusen (n = 30) or CaD (n = 30).

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