Automated analysis of medical images is not new [1–3]. Researchers in the respiratory sciences and, particularly, the field of interstitial lung diseases, have long enthused about the potential for computers to analyse medical images thereby revealing "signals" hitherto invisible to the human eye: an enthusiasm only enhanced by recent developments in machine learning and artificial intelligence [4–6]. By leveraging the central importance of computed tomography (CT) scanning for diagnosis, treatment decisions and prognostication, a key aim is to identify imaging biomarkers to more accurately phenotype disease and, in so doing, move a step closer to truly patient-centric medicine. Another goal is to apply novel imaging analyses to pathogenesis, disease "behaviour" and prognostication in the hope that this might unlock new therapeutic approaches. Given the digital nature of the data and the potentially myriad imaging patterns, frequently compounded by patient, therapeutic and disease-based factors, lung imaging is ideally suited to more sophisticated analytic approaches.

医学图像的自动分析并不新鲜 [1-3]。呼吸科学，尤其是间质性肺病领域的研究人员长期以来一直热衷于计算机分析医学图像从而揭示迄今为止人眼看不见的“信号”的潜力：机器学习的最新发展只会增强这种热情和人工智能 [4-6]。通过利用计算机断层扫描 (CT) 扫描在诊断、治疗决策和预后方面的核心重要性，一个关键目标是识别成像生物标志物以更准确地对疾病表型，从而向真正以患者为中心的医学更近一步。另一个目标是将新的成像分析应用于发病机制、疾病“行为” 和预测，希望这可能会开启新的治疗方法。考虑到数据的数字性质和潜在的无数成像模式，经常与患者、治疗和基于疾病的因素相结合，肺成像非常适合更复杂的分析方法。