PhD Defense: Machine Learning in Cardiothoracic Radiology - from Medical Data Curation to Clinical Application

The thesis is organized into 8 chapters. Its main theme is the creation and investigation of machine learning (ML) algorithms for a broad spectrum of tasks in the field of cardiothoracic radiology.

This includes data curation, a fundamental step at the beginning of most scientific projects, exemplified by an algorithm for radiology report classification (Chapter 2). Furthermore, image recognition algorithms for detection (Chapters 3 & 4) and segmentation (Chapter 5) of cardiothoracic findings in cross-sectional imaging (pulmonary embolism, rib fractures, and lung cancer).  Finally, a model using cardiothoracic CT imaging biomarkers extracted with machine learning to predict the clinical course of patients infected with SARS-CoV-2 is presented (Chapter 6). Thereby, the chapters of the thesis follow a chronologic sequence of tasks, beginning with data curation to finding detection to finding segmentation to application of extracted information for clinical decision support. Finally, Chapter 7 summarizes and discusses methodological insights gained during the abovementioned ML projects and provides a framework for creation and evaluation of machine learning algorithms in clinical practice.

The thesis is framed by an introduction (Chapter 1), which includes a brief introduction to ML, discusses the relevance of ML to radiology, and provides examples of current ML applications in radiology with a focus on cardiothoracic imaging. And by a concluding chapter (Chapter 8) that summarizes the main findings and discusses future directions of machine learning in radiology with a focus on cardiothoracic imaging.