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CASE STUDY

Acuity AI – AI-Powered Eye Health Intelligence

AI platform that helps ophthalmology clinics detect eye diseases earlier and faster, using image analysis and cloud-based tools to make diagnostics more accessible and consistent.

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Industry

Healthcare

Deployment

Cloud-based AI platform

Scope

Automated analysis of ocular imaging

Core KPI

Faster, more consistent eye exam reports

Overview

Eye health is critical, but diagnosing diseases like glaucoma, macular degeneration, and diabetic retinopathy requires expert review of detailed eye exams. This process is manual, time-consuming, and dependent on specialists, limiting speed and accessibility — especially in underserved regions.

Acuity AI was created to democratize ophthalmology diagnostics, making it faster, more scalable, and consistent through artificial intelligence.

The Challenge

  • Manual exam analysis required high specialist workload.

  • Risk of human variation in diagnosis.

  • Limited access to quality exams in remote or underserved areas.

  • Need for faster diagnostics to enable early disease detection.

Our Solution

We built Acuity AI, an intelligent platform for eye health:

  • Automated image analysis – AI detects patterns related to glaucoma, diabetic retinopathy, and other conditions.

  • Computer vision segmentation – precise detection of anatomical regions for accurate measurement.

  • Cloud-based platform – clinics upload images, receive real-time reports, and scale easily.

  • Decision support tools – structured reports assist specialists in final diagnosis.

Business Benefits

  • Faster diagnostics – reduces time from exam to actionable report.
  • Consistent analysis – minimizes human variation across clinicians.
  • Expanded access – makes quality diagnostics available in remote locations.
  • Decision support – detailed, data-driven reports enhance medical confidence.

How It Works

Image upload

Clinics capture and upload retinal exam images.

AI analysis

Computer vision segments and classifies disease patterns.

Report generation

Automated summaries highlight findings and risk levels.

Decision support

Specialists review reports for final validation.

Technical Highlights

  • Computer vision for analyzing and segmenting retinal images.

  • AI classifiers trained on medical datasets to detect eye diseases.

  • Cloud-native platform accessible from any clinic, scalable to demand.

  • Secure processing to protect sensitive patient data.

Key Results

Automated disease detection supporting early intervention.

Reduced specialist workload for initial exam reviews.

Increased access to eye care in underserved communities.

Pilot project running with partner clinics, moving toward clinical validation.

See It in Action

  • AI algorithms for eye disease detection.
  • Cloud-based web platform for image upload and analysis.
  • Automated reporting with decision support features.
  • Clinical validation in partnership with ophthalmology centers.

AI That Sees Ahead in Eye Care

Discover how AI can support ophthalmologists, expand access, and speed up diagnosis for critical eye conditions.

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