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SIIM26 logo footer
  • Attend
    • Hotels + Venue
    • Who Should Attend?
    • Networking Events
    • Justify Your Attendance!
    • Preliminary Program
  • Program
    • Program Agenda
    • Featured Education
      • General Sessions
      • Learning Labs
      • Scientific Presentations
      • SMIT Picks & Tips
    • Roadmaps
      • Cardiology
      • Dermatology
      • Ophthalmology
      • Pathology
      • Radiology
      • SMIT Picks & Tips
    • Expo Learning
    • Continuing Education
    • Speakers List
    • Preliminary Program
    • Speaker Resources
  • TECH Expo
    • Exhibitors
    • Alphabetical Exhibitor List
    • Product Categories
    • Expo Learning
    • Floor Plan
  • Pre-Conference
    • Epic Users Group Meeting
    • SIIM-ACR Data Science Summit
  • Exhibit + Sponsor
    • How To Sign Up
    • Sponsor + Advertise
    • Startup Street
    • Booth Contract
    • Rules & Regulations
    • Vendor Prospectus
    • Exhibitor Resources
    • Suitcase Marketing & Unauthorized Research Policy
  • Register
Portrait of Syed Haqqani, MS, at the SIIM Annual Meeting, showcasing a professional speaker in medical imaging.
Event Speaker

Syed Haqqani, MS

Data Scientist, ODSHI
National Eye Institute, National Institutes of Health

Syed Haqqani, PhD is a Data Scientist at the National Eye Institute, where he applies AI, machine learning, and biomedical analytics to advance ophthalmic imaging research and vision science. His work focuses on developing data-driven tools that support disease detection, image interpretation, and translational innovation in eye health and precision medicine.

Sessions

Ophthalmic Imaging AI: Computer Vision for Retinal Disease Prediction

Presented by Kerry E. Ashby, MS, John Giannini, PhD, Syed Haqqani, MS, Amberlynn Reed, MPH in Ophthalmology, Radiology Jun 10, 2026 | 1:45 PM.

Artificial intelligence is rapidly expanding the diagnostic value of ophthalmic imaging, transforming retinal photographs and optical coherence tomography (OCT) scans into rich sources of predictive clinical insight. As deep learning and computer vision methods mature, retinal imaging is increasingly being used not only to document ocular abnormalities, but also to identify subtle biomarkers associated with progressive diseases…

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