Project descpription:
Bronchiectasis is a chronic lung condition where the airways become abnormally widened, often due to previous lung infections, immune system disorders, or genetic conditions. This widening leads to persistent coughing, excess mucus production, and frequent respiratory infections like pneumonia. In addition to pneumonia, patients with bronchiectasis are prone to
recurrent exacerbations, where symptoms suddenly worsen, increasing the risk of lung damage and hospitalization. Identifying patients at high risk for pneumonia and exacerbations is crucial for timely intervention and better outcomes. Despite advances in medical imaging, current methods do not accurately predict which patients are most likely to develop pneumonia or experience exacerbations. This project aims to address this gap by developing artificial intelligence (AI) tools to predict these risks in bronchiectasis patients. By analyzing clinical data—including patient histories, comorbidities, and microbiological profiles—alongside detailed chest CT scans, the AI will identify patterns that indicate a higher likelihood of complications.
About Tician Schnitzler:
Dr. Tician Schnitzler is a radiology resident and postdoctoral researcher at Kantonsspital Aarau, specializing in thoracic imaging and artificial intelligence (AI) applications in medical imaging. His expertise has been shaped by a research fellowship at the University of California, San Francisco (UCSF), where he focused on integrating AI into clinical radiology. He holds an MD from RWTH Aachen University, Germany, and is currently pursuing a Master’s in Biomedical Informatics and Data Science at the University of Mannheim to further enhance his skills in data-driven medical research.
Dr. Schnitzler will lead the project "Predicting Recurrent Pneumonias and Exacerbations in Bronchiectasis: Clinical and Imaging Phenotypes for Risk Stratification and Algorithm-Assisted Management." In this role, he applies his specialized skills to develop predictive models that integrate clinical data with chest CT imaging, aiming to identify bronchiectasis patients at high risk for pneumonia and exacerbations. This project represents a unique collaboration between Kantonsspital Aarau and the Paul Scherrer Institute (PSI) with the goal to advance AI-driven tools that enhance patient management—particularly in the context of Switzerland’s upcoming national lung cancer screening program.
Involved institutions:
