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Related Concept Videos

Visual Agnosia01:12

Visual Agnosia

263
Visual agnosia is a condition characterized by the inability to recognize visually presented objects despite having normal vision. For instance, a person with visual agnosia can describe the shape and color of an object but cannot identify or name it. This impairment does not affect their visual field, acuity, color vision, brightness discrimination, language, or memory. An example of this condition in a social setting is someone at a dinner party asking for "that silver thing with a round...
263
Prosopagnosia01:24

Prosopagnosia

217
Prosopagnosia, also known as face blindness, is the inability to recognize faces. In severe cases, individuals with prosopagnosia may not recognize close family members, including parents and spouses, by their faces. For instance, someone with prosopagnosia might walk past their child in a crowd, only realizing their mistake upon noticing their child's distinctive backpack or favorite jacket. Prosopagnosia specifically impairs facial recognition, while the recognition of other objects or...
217

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Identifying geographic atrophy.

Leanne Clevenger1, Aleksandra Rachitskaya

  • 1Cleveland Clinic, Cole Eye Institute, Cleveland, Ohio, USA.

Current Opinion in Ophthalmology
|March 21, 2023
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This summary is machine-generated.

Accurate identification and monitoring of geographic atrophy (GA), a late stage of age-related macular degeneration (AMD), are crucial. Advances in optical coherence tomography (OCT) and deep learning show promise for diagnosing and tracking GA progression.

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Area of Science:

  • Ophthalmology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Age-related macular degeneration (AMD) is a primary cause of vision loss.
  • Geographic atrophy (GA) represents a late, advanced stage of AMD.
  • Early detection and monitoring of GA are vital for patient management.

Conclusions:

  • Accurate identification of GA and its risk factors is essential for patient counseling and prognosis.
  • Understanding GA progression is critical for selecting patients for emerging targeted therapies.
  • Future research should focus on the clinical utility and generalizability of deep learning tools in GA management.