Prayer F, Röhrich S, Pan J, Hofmanninger J, Langs G, Prosch H
Universitätsklinik für Radiologie und Nuklearmedizin, Medizinische Universität Wien, Währinger Gürtel 18-20, 1090, Wien, Österreich.
Computational Imaging and Research Lab, Universitätsklinik für Radiologie und Nuklearmedizin, Medizinische Universität Wien, Wien, Österreich.
Radiologe. 2020 Jan;60(1):42-47. doi: 10.1007/s00117-019-00611-2.
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.
Owing to unspecific symptoms, few well-defined CT disease patterns, and varying prognosis, interstitial lungs disease represents a focus of AI-based research.
Supervised and unsupervised machine learning can identify CT disease patterns using features which may allow the analysis of associations with specific diseases and outcomes.
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.
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.
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.
临床/方法学问题：人工智能（AI）有潜力通过对疾病进展进行自动检测、量化、分类和预测，提高肺部疾病患者的诊断准确性和管理水平。
由于症状不具特异性、明确的CT疾病模式较少以及预后各异，间质性肺疾病是基于人工智能研究的重点。
有监督和无监督机器学习可利用特征识别CT疾病模式，这些特征可能有助于分析与特定疾病及结果的关联。
机器学习一方面可改善肺结节的计算机辅助检测。另一方面，它能够对肺结节进行进一步特征描述，这可能会提高肺癌筛查项目的资源利用效率。
基于人工智能的CT数据分析存在若干挑战。除了需要强大的算法外，有效的机器学习还需要专家注释以及反映生理和病理变异性的广泛训练数据集。由于这一新兴领域缺乏标准化，人工智能研究的可比性和可重复性值得关注。
本文综述了人工智能在肺部成像领域的现状和挑战，特别考虑了间质性肺疾病以及肺结节的检测与分析。
