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International Collaboration Explores AI-Based Prediction in Pediatric Hip Imaging
The Analytics and Informatics for Child Health group, led by Prof. Dr. Ece Özkan Elsen, has launched a new scientific collaboration with the Swiss Mongolian Pediatric Project (SMOPP) to develop multimodal AI models for pediatric hip imaging.
The partnership combines SMOPP’s clinical expertise in Mongolia with the AICH group’s experience in machine learning. Since 2009, SMOPP has been working to improve the early detection and treatment of developmental dysplasia of the hip (DDH), one of the most common congenital musculoskeletal disorders in children. The project also includes training local physicians, improving diagnostic quality, and supporting the provision of ultrasound equipment.
Over the years, this work has resulted in a unique longitudinal dataset of pediatric patients, including neonatal hip ultrasound images, follow-up pelvic radiographs, and structured clinical data. Using this resource, the team aims to develop multimodal AI models capable of supporting clinical decision-making. A key objective of the project is to investigate whether information from early ultrasound examinations can predict long-term hip development and potentially reduce the need for follow-up radiographic imaging.
Learn about the Swiss Mongolian Pediatric Project (SMOPP)
More about AICH Research Group