论文标题
射线照片上的导管和试管的计算机辅助评估：人工智能评估的好处？
Computer-Aided Assessment of Catheters and Tubes on Radiographs: How Good is Artificial Intelligence for Assessment?
论文作者
论文摘要
导管是X光片上第二常见的异常发现。必须在所有X光片上评估导管的位置，因为如果导管不正确，可能会出现严重的并发症。但是，由于每天进行的X光片数量大量，在执行X光片和放射线医生解释时可能会有很大的延迟。计算机辅助方法具有潜在的潜力，可以用潜在的故障导管来确定X光片的优先级，以解释并自动插入文本，指示导管在放射学报告中的放置，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，该领域的研究仍然很少。随着深度学习方法的发展，导管评估的问题可以解决得多。因此，我们已经对当前算法进行了综述，并确定了建立可靠的计算机辅助诊断系统以评估X光片的导管的关键挑战。这项审查可能有助于进一步开发这种重要用例的机器学习方法。
Catheters are the second most common abnormal finding on radiographs. The position of catheters must be assessed on all radiographs, as serious complications can arise if catheters are malpositioned. However, due to the large number of radiographs performed each day, there can be substantial delays between the time a radiograph is performed 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. Therefore, we have performed a review of current algorithms and identified 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.
