Accident research and prevention

In close collaboration with the Division of Pediatric Radiology, led by Sebastian Tschauner, we are investigating the use of artificial intelligence (AI) and machine learning methods to support image interpretation. The goal of our research is to facilitate and improve the evaluation of native radiological and sonographic image data through innovative analytical methods. By employing modern radiomics approaches and AI-based algorithms, we aim to identify hidden image information that can increase diagnostic accuracy and support clinical decision-making. In this regard, we have already published several articles in high-impact journals. For example, we were able to demonstrate that AI can effectively distinguish between cranial sutures and fractures in infancy using sonographic images (Hankel et al, Sci Rep).

The use of artificial intelligence also holds great promise for the future of surgery, particularly in minimally invasive procedures. In our research, we are investigating the extent to which AI-based methods can be used to assist in laparoscopic surgeries. The goal is to support surgical procedures through intelligent analysis of image and video data, to identify relevant structures more reliably, and thereby further improve the safety and precision of minimally invasive procedures. We have already published an experimental study on the laparoscopic Nissen procedure. (Till et al, Artificial intelligence based surgical support for experimental laparoscopic Nissen fundoplication, Front Ped)

The use of artificial intelligence in everyday medical practice is still perceived by many practicing physicians as a kind of “black box.” Despite the great potential of these technologies, there is often uncertainty regarding how they work, their reliability, and their practical integration into the clinical workflow. For this reason, structured surveys and studies are of great importance in assessing how physicians perceive AI and to what extent there is a willingness to implement it in clinical practice. In this regard, we have already published articles examining the AI competence of practicing and trainee colleagues, including in the European context 

• Till et al, Artificial Intelligence Competencies and Educational Needs Among ERNICA Members: Results of a Multinational Survey,  Eur J Ped Surg
• Till et al, Enhancing AI Readiness in Pediatric Surgery: Impact of a Targeted Workshop on Knowledge and Competencies, Eur J Ped Surg
• Till et al, Artificial Intelligence (AI) Competency and Educational Needs: Results of an AI Survey of Members of the European Society of Pediatric Endoscopic Surgeons (ESPES)