Lew Dana, Klang Eyal, Soffer Shelly, Morgenthau Adam S
Division of Internal Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Department of Diagnostic Imaging, Sheba Medical Center, Ramat Gan, Israel.
Lung. 2023 Oct;201(5):445-454. doi: 10.1007/s00408-023-00641-7. Epub 2023 Sep 20.
Sarcoidosis is a complex disease which can affect nearly every organ system with manifestations ranging from asymptomatic imaging findings to sudden cardiac death. As such, diagnosis and prognostication are topics of continued investigation. Recent technological advancements have introduced multiple modalities of artificial intelligence (AI) to the study of sarcoidosis. Machine learning, deep learning, and radiomics have predominantly been used to study sarcoidosis.
Articles were collected by searching online databases using keywords such as sarcoid, machine learning, artificial intelligence, radiomics, and deep learning. Article titles and abstracts were reviewed for relevance by a single reviewer. Articles written in languages other than English were excluded.
Machine learning may be used to help diagnose pulmonary sarcoidosis and prognosticate in cardiac sarcoidosis. Deep learning is most comprehensively studied for diagnosis of pulmonary sarcoidosis and has less frequently been applied to prognostication in cardiac sarcoidosis. Radiomics has primarily been used to differentiate sarcoidosis from malignancy. To date, the use of AI in sarcoidosis is limited by the rarity of this disease, leading to small, suboptimal training sets. Nevertheless, there are applications of AI that have been used to study other systemic diseases, which may be adapted for use in sarcoidosis. These applications include discovery of new disease phenotypes, discovery of biomarkers of disease onset and activity, and treatment optimization.
结节病是一种复杂的疾病，几乎可累及每个器官系统，其表现从无症状的影像学发现到心源性猝死不等。因此，诊断和预后评估一直是持续研究的课题。最近的技术进步将多种人工智能（AI）模式引入了结节病的研究。机器学习、深度学习和放射组学已主要用于研究结节病。
通过使用“结节病”“机器学习”“人工智能”“放射组学”和“深度学习”等关键词搜索在线数据库来收集文章。由一名审稿人对文章标题和摘要进行相关性审查。排除非英文撰写的文章。
机器学习可用于帮助诊断肺部结节病和对心脏结节病进行预后评估。深度学习在肺部结节病诊断方面的研究最为全面，而在心脏结节病预后评估中的应用较少。放射组学主要用于区分结节病和恶性肿瘤。迄今为止，AI在结节病中的应用受到该疾病罕见性的限制，导致训练集规模小且不够理想。然而，已有AI应用于研究其他全身性疾病，这些应用可能适用于结节病。这些应用包括发现新的疾病表型、发现疾病发病和活动的生物标志物以及优化治疗。
