Staff profile

I am a Postdoctoral Research Associate at Durham University, specializing in enhancing the quality of home-based Optical Coherence Tomography (OCT) images. I am also a member of the Engineering Ethics Committee, reflecting my commitment to ethical practices in research and innovation. With a PhD in Biomedical Engineering (Bioelectronics), I bring expertise in AI-driven medical innovations, including drug discovery, biomarker detection, and imaging analysis. Previously, I reduced equipment downtime and optimized costs as a medical equipment expert, earning recognition for enhancing service quality. Proficient in Python and MATLAB, I develop advanced AI tools to bridge engineering and medicine, driving healthcare innovation and better patient care.

• Artificial Intelligence
• Machine learning / Deep Learning
• Image Processing (Medical Image Processing, Computer Vision, Graph based image processing, Image modeling, Noise reduction methods)
• Time-frequency methods (Sparse representations, Dictionary learning, X-lets)
• Medical Data Analysis
• Image Quality Assessment

Journal Article

• Analyzing Retinal Vessel Morphology in MS Using Interpretable AI on Deep Learning-Segmented IR-SLO Images
  
  Soltanipour, A., Arian, R., Aghababaei, A., Ashtari, F., Zhou, Y., Keane, P. A., & Kafieh, R. (2025). Analyzing Retinal Vessel Morphology in MS Using Interpretable AI on Deep Learning-Segmented IR-SLO Images. Bioengineering, 12(8), Article 847. https://doi.org/10.3390/bioengineering12080847
• CircWaveDL: Modeling of optical coherence tomography images based on a new supervised tensor-based dictionary learning for classification of macular abnormalities
  
  Arian, R., Vard, A., Kafieh, R., Plonka, G., & Rabbani, H. (2025). CircWaveDL: Modeling of optical coherence tomography images based on a new supervised tensor-based dictionary learning for classification of macular abnormalities. Artificial Intelligence in Medicine, 160, Article 103060. https://doi.org/10.1016/j.artmed.2024.103060
• Discrimination of multiple sclerosis using scanning laser ophthalmoscopy images with autoencoder-based feature extraction
  
  Aghababaei, A., Arian, R., Soltanipour, A., Ashtari, F., Rabbani, H., & Kafieh, R. (2024). Discrimination of multiple sclerosis using scanning laser ophthalmoscopy images with autoencoder-based feature extraction. Multiple Sclerosis and Related Disorders, 88, Article 105743. https://doi.org/10.1016/j.msard.2024.105743
• SLO-Net: Enhancing Multiple Sclerosis Diagnosis Beyond Optical Coherence Tomography Using Infrared Reflectance Scanning Laser Ophthalmoscopy Images.
  
  Arian, R., Aghababaei, A., Soltanipour, A., Khodabandeh, Z., Rakhshani, S., Iyer, S. B., Ashtari, F., Rabbani, H., & Kafieh, R. (2024). SLO-Net: Enhancing Multiple Sclerosis Diagnosis Beyond Optical Coherence Tomography Using Infrared Reflectance Scanning Laser Ophthalmoscopy Images. Translational Vision Science & Technology, 13(7), Article 13. https://doi.org/10.1167/tvst.13.7.13
• Automatic Choroid Vascularity Index Calculation in Optical Coherence Tomography Images with Low-Contrast Sclerochoroidal Junction Using Deep Learning
  
  Arian, R., Mahmoudi, T., Riazi-Esfahani, H., Faghihi, H., Mirshahi, A., Ghassemi, F., Khodabande, A., Kafieh, R., & Khalili Pour, E. (2023). Automatic Choroid Vascularity Index Calculation in Optical Coherence Tomography Images with Low-Contrast Sclerochoroidal Junction Using Deep Learning. Photonics, 10(3), Article 234. https://doi.org/10.3390/photonics10030234
• A new convolutional neural network based on combination of circlets and wavelets for macular OCT classification
  
  Arian, R., Vard, A., Kafieh, R., Plonka, G., & Rabbani, H. (2023). A new convolutional neural network based on combination of circlets and wavelets for macular OCT classification. Scientific Reports, 13(1), Article 22582. https://doi.org/10.1038/s41598-023-50164-7
• COVID-19 in Iran: Forecasting Pandemic Using Deep Learning
  
  Kafieh, R., Arian, R., Saeedizadeh, N., Amini, Z., Serej, N. D., Minaee, S., Yadav, S. K., Vaezi, A., Rezaei, N., & Javanmard, S. H. (2021). COVID-19 in Iran: Forecasting Pandemic Using Deep Learning. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE, 2021, Article 6927985. https://doi.org/10.1155/2021/6927985
• Isfahan and Covid-19: Deep spatiotemporal representation
  
  Kafieh, R., Saeedizadeh, N., Arian, R., Amini, Z., Serej, N. D., Vaezi, A., & Javanmard, S. H. (2020). Isfahan and Covid-19: Deep spatiotemporal representation. CHAOS SOLITONS \& FRACTALS, 141, Article 110339. https://doi.org/10.1016/j.chaos.2020.110339
• Study of CXCR4 chemokine receptor inhibitors using QSPR and molecular docking methodologies
  
  Mostashari-Rad, T., Arian, R., Sadri, H., Mehridehnavi, A., Mokhtari, M., Ghasemi, F., & Fassihi, A. (2019). Study of CXCR4 chemokine receptor inhibitors using QSPR and molecular docking methodologies. Journal of Theoretical and Computational Chemistry, 18(04), Article 1950018. https://doi.org/10.1142/s0219633619500184