Artificial intelligence applications for thoracic imaging. Issue 123 (February 2020)

• Record Type: Journal Article
• Authors: Chassagnon, Guillaume; Vakalopoulou, Maria; Paragios, Nikos; Revel, Marie-Pierre
• Is Part Of: European journal of radiology. Issue 123(2020)
• Publication Date: 2020-02
• Subjects: Artificial intelligence -- Deep learning -- Machine learning -- Thoracic imaging; Medical radiology -- Periodicals; Radiology -- Periodicals; Radiologie médicale -- Périodiques; Medical radiology; Periodicals; 616.075705
• DOI: 10.1016/j.ejrad.2019.108774
• Languages: English
• ISSNs: 0720-048X

Abstract

Highlights: Thoracic imaging benefits from the development of artificial intelligence algorithm. AI equals or exceeds radiologists' performance for chest X-ray single anomalies. Radiologists' performance increases when assisted by AI algorithms. Prospective clinical validation of AI algorithms is still lacking. Abstract: Artificial intelligence is a hot topic in medical imaging. The development of deep learning methods and in particular the use of convolutional neural networks (CNNs), have led to substantial performance gain over the classic machine learning techniques. Multiple usages are currently being evaluated, especially for thoracic imaging, such as such as lung nodule evaluation, tuberculosis or pneumonia detection or quantification of diffuse lung diseases. Chest radiography is a near perfect domain for the development of deep learning algorithms for automatic interpretation, requiring large annotated datasets, in view of the high number of procedures and increasing data availability. Current algorithms are able to detect up to 14 common anomalies, when present as isolated findings. Chest computed tomography is another major field of application for artificial intelligence, especially in the perspective of large scale lung cancer screening. It is important for radiologists to apprehend, contribute actively and lead this new era of radiology powered by artificial intelligence. Such a perspective requires understanding new terms and concepts associated with machine learning. The objective of this paper is to provide useful definitions for understanding the methods used and their possibilities, and report current and future developments for thoracic imaging. Prospective validation of AI tools will be required before reaching routine clinical implementation.