[Artificial intelligence in lung imaging]. / Künstliche Intelligenz in der Bildgebung der Lunge.

CLINICAL/METHODICAL ISSUE: Artificial intelligence (AI) has the potential to improve diagnostic accuracy and management in patients with lung disease through automated detection, quantification, classification, and prediction of disease progression. STANDARD RADIOLOGICAL METHODS: Owing to unspecific symptoms, few well-defined CT disease patterns, and varying prognosis, interstitial lungs disease represents a focus of AI-based research. METHODICAL INNOVATIONS: Supervised and unsupervised machine learning can identify CT disease patterns using features which may allow the analysis of associations with specific diseases and outcomes. PERFORMANCE: Machine learning on the one hand improves computer-aided detection of pulmonary nodules. On the other hand it enables further characterization of pulmonary nodules, which may improve resource effectiveness regarding lung cancer screening programs. ACHIEVEMENTS: There are several challenges regarding AI-based CT data analysis. Besides the need for powerful algorithms, expert annotations and extensive training data sets that reflect physiologic and pathologic variability are required for effective machine learning. Comparability and reproducibility of AI research deserve consideration due to a lack of standardization in this emerging field. PRACTICAL RECOMMENDATIONS: This review article presents the state of the art and the challenges concerning AI in lung imaging with special consideration of interstitial lung disease, and detection and consideration of pulmonary nodules.

Machine learning: from radiomics to discovery and routine.

Machine learning is rapidly gaining importance in radiology. It allows for the exploitation of patterns in imaging data and in patient records for a more accurate and precise quantification, diagnosis, and prognosis. Here, we outline the basics of machine learning relevant for radiology, and review the current state of the art, the limitations, and the challenges faced as these techniques become an important building block of precision medicine. Furthermore, we discuss the roles machine learning can play in clinical routine and research and predict how it might change the field of radiology.