Sumeia Elkazza

Sumeia Elkazza is a Lecturer in Computer Science at the School of Computing, Engineering and Digital Technologies, Teesside University. She holds a PhD in Intelligent Sensing and Communication from Newcastle University, with a thesis focusing on prognosis prediction in retinal vein occlusion using machine learning and deep learning. Her MSc (Distinction) and BSc (First Class) are from Northumbria University and Benghazi University, respectively. Her research emphasizes AI applications in medical imaging, particularly ophthalmology, with a focus on feature extraction and predictive modeling for retinal diseases. She teaches programming (Python, C/C++) and machine learning.

Her research interests span artificial intelligence, machine learning, and deep learning applied to medical imaging, including retinal vein occlusion prognosis and anti-VEGF treatment prediction. Collaborations involve clinical decision support systems and ophthalmic disease analysis. Recent work includes developing machine learning models to match clinician performance in forecasting visual outcomes for retinal vein occlusion patients.

• Education:
  • PhD: Newcastle University (Intelligent Sensing & Communication)
  • MSc: Northumbria University (Computer Science, Distinction)
  • BSc: Benghazi University (Computer Science, First Class)

Research Focus: Elkazza’s work bridges AI and healthcare, particularly in ophthalmology. She explores machine learning techniques for medical image analysis, focusing on retinal diseases like macular edema and vein occlusion. Her models aim to enhance clinical decision-making through accurate prognosis and treatment efficacy prediction.

Grants/Advising: She is currently accepting PhD students in AI/ML for medical applications. No specific grants are listed, but her research aligns with clinical collaboration frameworks.