Yi Xin, Adams Scott J, Henderson Robert D E, Babyn Paul
Department of Medical Imaging (X.Y., S.J.A., P.B.) and College of Medicine (R.D.E.H.), University of Saskatchewan, 103 Hospital Drive, Saskatoon, SK, Canada S7N 0W8.
Radiol Artif Intell. 2020 Jan 29;2(1):e190082. doi: 10.1148/ryai.2020190082. eCollection 2020 Jan.
Catheters are the second most common abnormal finding on radiographs. The position of catheters must be assessed on all radiographs because serious complications can arise if catheters are malpositioned. However, due to the large number of radiographs obtained each day, there can be substantial delays between the time a radiograph is obtained and when it is interpreted by a radiologist. Computer-aided approaches hold the potential to assist in prioritizing radiographs with potentially malpositioned catheters for interpretation and automatically insert text indicating the placement of catheters in radiology reports, thereby improving radiologists' efficiency. After 50 years of research in computer-aided diagnosis, there is still a paucity of study in this area. With the development of deep learning approaches, the problem of catheter assessment is far more solvable. This review provides an overview of current algorithms and identifies key challenges in building a reliable computer-aided diagnosis system for assessment of catheters on radiographs. This review may serve to further the development of machine learning approaches for this important use case. © RSNA, 2020.
导管是X光片上第二常见的异常表现。必须在所有X光片上评估导管的位置，因为如果导管位置不当，可能会引发严重并发症。然而，由于每天获取的X光片数量众多，从获取X光片到放射科医生解读之间可能会有相当长的延迟。计算机辅助方法有潜力协助对可能存在导管位置不当的X光片进行优先解读，并在放射学报告中自动插入说明导管位置的文本，从而提高放射科医生的效率。经过50年的计算机辅助诊断研究，该领域的研究仍然匮乏。随着深度学习方法的发展，导管评估问题更易于解决。本综述概述了当前算法，并确定了构建用于X光片导管评估的可靠计算机辅助诊断系统的关键挑战。本综述可能有助于推动针对这一重要用例的机器学习方法的发展。© RSNA, 2020.
