Ahmad Hassan K, Milne Michael R, Buchlak Quinlan D, Ektas Nalan, Sanderson Georgina, Chamtie Hadi, Karunasena Sajith, Chiang Jason, Holt Xavier, Tang Cyril H M, Seah Jarrel C Y, Bottrell Georgina, Esmaili Nazanin, Brotchie Peter, Jones Catherine
Annalise.ai, Sydney, NSW 2000, Australia.
Department of Emergency Medicine, Royal North Shore Hospital, Sydney, NSW 2065, Australia.
Diagnostics (Basel). 2023 Feb 15;13(4):743. doi: 10.3390/diagnostics13040743.
Limitations of the chest X-ray (CXR) have resulted in attempts to create machine learning systems to assist clinicians and improve interpretation accuracy. An understanding of the capabilities and limitations of modern machine learning systems is necessary for clinicians as these tools begin to permeate practice. This systematic review aimed to provide an overview of machine learning applications designed to facilitate CXR interpretation. A systematic search strategy was executed to identify research into machine learning algorithms capable of detecting >2 radiographic findings on CXRs published between January 2020 and September 2022. Model details and study characteristics, including risk of bias and quality, were summarized. Initially, 2248 articles were retrieved, with 46 included in the final review. Published models demonstrated strong standalone performance and were typically as accurate, or more accurate, than radiologists or non-radiologist clinicians. Multiple studies demonstrated an improvement in the clinical finding classification performance of clinicians when models acted as a diagnostic assistance device. Device performance was compared with that of clinicians in 30% of studies, while effects on clinical perception and diagnosis were evaluated in 19%. Only one study was prospectively run. On average, 128,662 images were used to train and validate models. Most classified less than eight clinical findings, while the three most comprehensive models classified 54, 72, and 124 findings. This review suggests that machine learning devices designed to facilitate CXR interpretation perform strongly, improve the detection performance of clinicians, and improve the efficiency of radiology workflow. Several limitations were identified, and clinician involvement and expertise will be key to driving the safe implementation of quality CXR machine learning systems.
胸部X光（CXR）的局限性促使人们尝试创建机器学习系统，以协助临床医生并提高解读准确性。随着这些工具开始融入临床实践，临床医生有必要了解现代机器学习系统的能力和局限性。本系统综述旨在概述旨在促进CXR解读的机器学习应用。执行了系统的检索策略，以识别2020年1月至2022年9月期间发表的、能够检测胸部X光片上超过2种影像学表现的机器学习算法的研究。总结了模型细节和研究特征，包括偏倚风险和质量。最初检索到2248篇文章，最终纳入综述的有46篇。已发表的模型表现出强大的独立性能，通常与放射科医生或非放射科临床医生一样准确，甚至更准确。多项研究表明，当模型作为诊断辅助工具时，临床医生对临床发现的分类表现有所改善。在30%的研究中，将设备性能与临床医生的性能进行了比较，而在19%的研究中评估了对临床认知和诊断的影响。只有一项研究是前瞻性进行的。平均而言，使用128,662张图像来训练和验证模型。大多数模型分类的临床发现少于8种，而最全面的三个模型分类了54、72和124种发现。本综述表明，旨在促进CXR解读的机器学习设备表现出色，提高了临床医生的检测性能，并提高了放射学工作流程的效率。同时也发现了一些局限性，临床医生的参与和专业知识将是推动高质量CXR机器学习系统安全实施的关键。
