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Artificial Intelligence In Ophthalmology Center

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The UIC Artificial Intelligence in Ophthalmology (Ai-O) is a center of excellence for Artificial Intelligence research, theory, applications, and education in ophthalmology.

Overview Heading link


As we amass more data, the greatest advances in ophthalmic healthcare solutions of the 21st century will be the creation of tools that can utilize and integrate diverse ophthalmic imaging, clinical, genetic, and socio-demographic data for complex AI solutions.


Our mission is to develop Human Centered-AI for patient care in an interpretable, ethical and reproducible manner.


  • Theory and Learning
  • Medical Imaging
  • Clinical Research
  • Translational
  • Public Health and Education

Ai-O Center Heading link

Located at the Eye and Ear Infirmary (EEI)

1855 W. Taylor St., Chicago, Illinois 60612

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I-ODA is a live databank that includes real-world longitudinal clinical ophthalmic images and patient metadata, with the purpose of advancing state-of-the-art computer vision applications in ophthalmology, and improving upon the translatable capacity of machine learning based applications.

Project lead: Joelle Hallak

Team: Nooshin Mojab, Abdullah Aleem, Manoj Prabhakar Nallabothula, Joe Baker, Darvin Yi

Auto Ptosis

Auto Ptosis is a pipeline for detecting and calculating the features to help diagnosis ptosis. This disease presents itself with the “drooping” or “falling” of the upper eyelid and can lead to an impairment of visual field. We tackle this task through several different levels of hand-crafted design: (1) we train a classification network to differentiate healthy and ptosis eyes in an end-to-end fashion, (2) we use keypoint detection networks to delineate eyelids and pupils to derive the MRD1 distance (the marginal reflex distance-1, the gold standard of ptosis diagnosis), and (3) we also calculate supporting features such as percentage of iris visible through the eyelids. Features such as the latter were too difficult to calculate before, but with machine learning, can be an automatic output from any front-facing photograph. Calculating novel metrics like these can help expand clinical workflow and eliminate the need for complex clinical imaging set-ups, potentially supporting efforts in telemedicine.

Project lead: Pete Setabutr

Team Lead: Darvin Yi

Team: Abdullah Aleem, Manoj Prabhakar Nallabothula

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The AI Utils project aims to create basic deep learning AI tools for internal research in UIC’s department of ophthalmology and bioengineering. This is done through an amalgamation of API’s, protocols, and software packages hosted in the AI Utils repository . Through dockering workflows, we will define potential folder hierarchies and easy to load models, based on current state of the art (SOTA) performances. For new projects, users will have to simply format their data according to AI Utils protocol, and training should become as simple as a single line command. Upon training a network, AI Utils will also provide an API for future inference calls through docker as well, which will enable easy deployment on cloud compute clusters or even our own backend servers. By well defining and documenting agreements between users and developers, we can strive to democratize AI.

Project lead: Darvin Yi

Team: Abdullah Aleem, Manoj Prabhakar Nallabothula, Joelle Hallak

Our Team Heading link