VISION CARE AI-AI-powered retina diagnostic tool

Example

In an OCT scan showing retinal thinning, VISION CARE AI can automatically detect abnormalities in the retinal layers, helping identify diseases like diabetic macular edema or macular degeneration.

Scenario

An ophthalmologist working in a rural clinic with limited access to specialized diagnostic tools can use VISION CARE AI to enhance their diagnostic capabilities by analyzing retinal images and cross-referencing results with their clinical findings.

Example

VISION CARE AI assists in remote consultations by analyzing retinal scans sent from clinics to specialists and providing preliminary assessments, aiding in faster decision-making for patient care.

Scenario

A remote clinic sends a patient’s fundus images to a central hospital. VISION CARE AI analyzes the images, flags potential retinal tears, and suggests the need for immediate intervention. This helps the clinic prioritize urgent cases.

Example

By aggregating patient data over time, VISION CARE AI provides trend analysis, such as tracking the progression of conditions like glaucoma or diabetic retinopathy.

Scenario

In a large ophthalmology practice, VISION CARE AI monitors patients’ retinal health over time, alerting the ophthalmologist to any significant changes that may require alterations in treatment plans.

Example

VISION CARE AI enables multiple specialists to view, analyze, and discuss retinal scans in real-time, promoting global collaboration on complex cases.

Scenario

A complex case of suspected retinoblastoma is shared between specialists in different countries, with VISION CARE AI facilitating real-time image annotations and diagnostic discussions, leading to a collaborative decision on patient management.