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Your Partner in Advancing Science, ADN CoE

Pediatric CT & MRI Interpretation Fellowship Program 1 year

Duration

1 year

Available Institutions

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The Pediatric CT & MRI Interpretation Fellowship is an advanced training program designed for radiologists and pediatric specialists seeking to enhance their expertise in pediatric imaging. This fellowship provides in-depth exposure to cutting-edge diagnostic techniques, AI-assisted imaging, and hands-on case-based learning, focusing on the interpretation of complex pediatric CT and MRI scans.

Throughout the program, fellows will work closely with leading pediatric radiologists and multidisciplinary experts, gaining practical experience in diagnosing a wide range of pediatric conditions, including congenital heart disease, neurological disorders, pulmonary hypertension, and vascular anomalies. The fellowship emphasizes a hybrid learning approach, combining interactive online lectures, real-life case discussions, and onsite hands-on training at ADN CoE’s Istanbul Training Center.

Fellows will develop critical diagnostic skills through a structured curriculum that covers advanced imaging protocols, AI-powered analysis, and multidisciplinary case reviews. Training will include workstation-based interpretation sessions, allowing participants to refine their proficiency in 3D cardiac modeling, AI-assisted risk stratification, and post-intervention imaging assessments.

In addition to technical expertise, the program emphasizes collaboration and patient-centered care, preparing fellows to communicate effectively with pediatricians, neurologists, and surgeons in a multidisciplinary setting. By the end of the fellowship, participants will be equipped with specialized knowledge in pediatric imaging, enabling them to make accurate, timely diagnoses and contribute to improved patient outcomes.

Graduates of the Pediatric CT & MRI Interpretation Fellowship will be well-prepared to advance their careers in pediatric radiology, with the opportunity to specialize further in areas such as AI-assisted diagnostics, interventional pediatric imaging, or research in emerging imaging technologies.

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