Data Mining and Machine Learning (DM2L)

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Investigator / Contact Person Siamak Yousefi, Ph.D., FARVO

Office 305-243-5411

Email siamak.yousefi@miami.edu

Research

Glaucoma Progression and Monitoring

Our group develops advanced computational models to improve prediction of glaucoma progression by integrating longitudinal visual fields (VFs), optic nerve imaging, retinal nerve fiber layer (RNFL) thickness, and clinical data. We focus on identifying early patterns of structural and functional decline, characterizing progression trajectories, and building individualized forecasting tools that can assist with treatment decisions and long-term glaucoma patient management.
Uveitis

We are leading the development of a uveitis research network (URN) to accumulate large multi-modal datasets of fundus photographs, OCT, angiography, and histology from experimental and clinical uveitis. Current efforts include precise quantitation of anatomical and pathological features as well as development of AI models for automated assessment of inflammatory activity, risk modeling, and creation of a multicenter data hub to support future clinical trials and collaborative studies in uveitis.
Corneal Ectasia Detection

We have led the development of a corneal research network (CRN) to accumulate large multi-modal datasets of cornea to address conditions such as keratoconus and ectasia. We develop a wide range of AI models to analyze corneal topography, tomography, and biomechanical measurements to detect early keratoconus and predict ectasia progression. By leveraging high-dimensional imaging features and longitudinal data, we work toward tools that enhance refractive screening, cross-linking decisions, and long-term corneal stability assessment.
Neuro-Ophthalmology

Our neuro-ophthalmology projects, in close collaboration with neuro-ophthalmology team, centers on building predictive models for IIH and other optic neuropathies using fundus images, OCT biomarkers, visual fields, and longitudinal EHR data. We aim to develop early-warning systems that can identify patients at high risk of irreversible visual loss and support timely, evidence-driven management.
Ocular Surface and Dry Eye Disease

Our ocular surface research focuses on automated analysis of slit-lamp imaging, confocal microscopy, keratoscopy, tear film metrics, and clinical examination findings. We aim to develop objective, reproducible biomarkers that can support accurate diagnosis, treatment selection, and monitoring of dry eye disease and related ocular surface disorders in both clinical and remote-care environments.
Ocular Oncology – Uveal Melanoma Characterization

We are building multimodal algorithms for the analysis of uveal melanoma using B-scan ultrasonography, cine loops, OCT, and fundus imaging. Ongoing projects include automated tumor segmentation, quantitative assessment of tumor dimensions, and models for predicting metastatic risk and treatment response to support clinical decision-making in ocular oncology.
AI-Enabled Screening and Tele-Ophthalmology Platforms

Our lab is working with other teams at BPEI to implement systems for eye screening. These platforms are designed to support remote triage, expand access to subspecialty-level assessment, and streamline clinical workflows across community and primary-care settings.
Foundation Models, Vision-Language Systems & Multimodal Fusion

We are exploring next-generation AI models that integrate imaging, structured data, and clinical text to support interpretation, report generation, and complex diagnostic reasoning. These efforts aim to develop ophthalmology-related generalist models capable of assisting across diverse clinical tasks and imaging modalities.
Whole-Eye AI Models

Our group works on integrative models that combine various ophthalmic imaging, functional testing, and systemic health records to understand both ocular conditions and systemic diseases. Our studies aim to uncover cross-disease signatures, improve risk stratification, and enable more personalized approaches to vision and systemic health.

Research

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Data Mining and Machine Learning (DM2L)

Data Mining and Machine Learning

Contact Us

Research

Glaucoma Progression and Monitoring

Uveitis

Corneal Ectasia Detection

Neuro-Ophthalmology

Ocular Surface and Dry Eye Disease

Ocular Oncology – Uveal Melanoma Characterization

AI-Enabled Screening and Tele-Ophthalmology Platforms

Foundation Models, Vision-Language Systems & Multimodal Fusion

Whole-Eye AI Models