Fahad Alshagathrah, PhD

I currently work for the Saudi Food and Drug Authority and I am focused on the establishment of a department concerned with scientific research for medical devices. Medical devices are currently one of the fastest growing industries due to innovation and the rapid advancement of technologies. The wide range of medical devices plays a major role in various aspects of healthcare services: diagnosis, treatment, prevention and rehabilitation. However, many countries lack access to high-quality devices and equipment appropriate for their specific epidemiological needs. Likewise, medical device registries and post-market medical device surveillance systems remain emergent in Saudi Arabia and MENA region in general. Complete and balanced postmarket surveillance program become necessary with these conditions. Because the premarket clinical data are limited or even absent, postmarket surveillance is needed as a “Safety Net” to assure the safety and the effectiveness of the marketed products. My colleagues and I are working seriously on the vigilance part and my concern here, with my team, is about the “Postmarket clinical follow-up” and issues related to it. This led me to establish the “Implantable Medical Devices National Registry”. My team is working seriously to start Networks of large registries designed to grasp and apprehend an important clinical/ non-clinical data/ information, which are particularly significant for proactive surveillance and post-market evaluation. Although the healthcare system in the kingdom of Saudi Arabia; as a large country; is wide and complicated, yet systems dedicated to collect, facilitate, and utilize the implantable medical devices related safety and effectiveness data and information are negligibly ineffective.

Performing and managing the different multi-tasks of lab workshop and dialysis work shop such as: 1- Repair 2- PPM 3- equipment inspection, validation, and acceptance 4- equipments installation 5- training 6- equipment disposal extra to that the task of managing the asset enterprise system (MP2), managing the recalls, preparing reports and statistics, being involved in the committees, participate with some research project, and many other tasks are part of the daily work.

Conducted advanced research in artificial intelligence for medical imaging, focusing on deep learning approaches to detect and quantify hepatic steatosis (fatty liver disease) from B-mode ultrasound images. Developed and validated AI models (CNNs and hybrid architectures) to perform multiclass classification of liver steatosis under domain shift challenges. Designed a large annotated ultrasound dataset and performed comparative analyses between AI models and expert radiologists. Published several peer-reviewed journal articles and presented findings at international conferences. Key Highlights: - Developed an AI-assisted diagnostic pipeline for NAFLD detection. - Designed multicenter studies using retrospective and prospective ultrasound data. - Integrated domain adaptation and machine learning techniques for robust performance. - Published 4+ papers in Q1 journals and created a national liver ultrasound dataset.