Dr. Hosameldin O. A. Ahmed, PhD, MIET, MIEEE
Research Fellow | Expert in Multimodal Medical Imaging, AI, XR Technologies, and Data Analytics

Dr. Hosameldin O. A. Ahmed is a leading researcher with expertise in multimodal medical imaging, artificial intelligence (AI), extended reality (XR), and data analytics. His interdisciplinary approach combines cutting-edge machine learning algorithms, XR-based technologies, and advanced signal and image processing to address challenges in healthcare, industrial systems, and cultural heritage preservation.

Research and Professional Experience

1. Multimodal AI-Powered Healthcare Diagnostics
   Dr. Ahmed has developed novel AI frameworks for lung and breast cancer detection using multimodal data, including X-ray, CT, and Ultrasound images. His two-step AI-based methods incorporate image analytics, feature generation, and deep learning models to deliver highly accurate disease classification and detection tools.
2. Integration of AI with Electronic Health Records (EHR)
   During his collaboration with Imperial College London, Dr. Ahmed developed a software tool seamlessly integrated with the NHS TPP SystmOne. This tool enables efficient retrieval, analysis, and visualisation of patient demographics and medical information, enhancing clinical decision-making.
3. XR and Metaverse-Based Healthcare Innovations
   Dr. Ahmed is a pioneer in XR technologies (AR, VR, MR), developing immersive applications for real-time data visualisation and medical training. Leveraging tools such as Unity 3D, Leap Motion Controllers, and machine learning, he has created platforms that enable virtual simulations, intuitive gesture-based interactions, and immersive user experiences.
4. Cultural Heritage Preservation with 3D Technologies
   As part of the CEPROQHA Project at Brunel University, Dr. Ahmed co-developed a cost-effective 3D visualisation framework for cultural heritage preservation. His work involved:

• • Photogrammetry for 3D content acquisition
  • Machine learning methods (e.g., super-resolution GANs) for image enhancement
  • Real-time visualisation using Looking Glass 3D screens
  • XR-based interaction tools for virtual museum experiences.
•  

     5. Predictive Analytics and Industrial Fault Diagnosis

Dr. Ahmed’s research has advanced machine condition monitoring techniques, focusing on compressive sampling and machine learning for fault diagnosis in rotating machinery. His proposed methods reduce computational costs while improving diagnostic precision.

Research Impact

Dr. Ahmed’s impactful contributions are recognised through:

• 47+ publications, including a book, journal articles, book chapters, and conference papers
• 758+ citations
• h-index: 12
• i10-index: 17

Technical Expertise

• Programming and AI Frameworks: Python, MATLAB, TensorFlow, PyTorch
• Image Processing Libraries: OpenCV, Scikit-Image, MATLAB Image Toolbox
• XR Platforms: Unity 3D, Unreal Engine
• Medical Imaging: X-ray, CT, Ultrasound, and mammography analysis
• Machine Learning: Deep learning, feature extraction, dimensionality reduction, CNNs, transfer learning

Vision and Future Goals

Dr. Ahmed is committed to advancing AI-powered, data-driven, and XR-based solutions to address critical challenges in healthcare, industry, and cultural preservation. His vision focuses on creating innovative tools that improve diagnostic accuracy, enhance clinical workflows, and enable immersive virtual environments for education, training, and data analysis.