Hahn Lewis D, Hall Kent, Alebdi Thamer, Kligerman Seth J, Hsiao Albert
Department of Radiology, University of California San Diego School of Medicine, 9300 Campus Point Dr, MC 0841, La Jolla, CA 92037-0841 (L.D.H., T.A., S.J.K., A.H.); and Naval Hospital Camp Pendleton, Oceanside, Calif (K.H.).
Radiol Artif Intell. 2022 Feb 23;4(2):e210162. doi: 10.1148/ryai.210162. eCollection 2022 Mar.
CT pulmonary angiography (CTPA) is the first-line imaging test for evaluation of acute pulmonary emboli. However, diagnostic quality is heterogeneous across institutions and is frequently limited by suboptimal pulmonary artery (PA) contrast enhancement. In this retrospective study, a deep learning algorithm for measuring enhancement of the central PAs was developed and assessed for feasibility of its use in quality improvement of CTPA. In a convenience sample of 450 patients, automated measurement of CTPA enhancement showed high agreement with manual radiologist measurement ( = 0.996). Using a threshold of less than 250 HU for suboptimal enhancement, the sensitivity and specificity of the automated classification were 100% and 99.5%, respectively. The algorithm was further evaluated in a random sampling of 3195 CTPA examinations from January 2019 through May 2021. Beginning in January 2021, the scanning protocol was transitioned from bolus tracking to a timing bolus strategy. Automated analysis of these examinations showed that most suboptimal examinations following the change in protocol were performed using one scanner, highlighting the potential value of deep learning algorithms for quality improvement in the radiology department. CT Angiography, Pulmonary Arteries © RSNA, 2022.
CT肺动脉造影（CTPA）是评估急性肺栓塞的一线成像检查。然而，各机构之间的诊断质量参差不齐，且常常受到肺动脉（PA）对比增强效果欠佳的限制。在这项回顾性研究中，开发了一种用于测量中央肺动脉强化程度的深度学习算法，并评估了其在改善CTPA质量方面应用的可行性。在450例患者的便利样本中，CTPA强化程度的自动测量结果与放射科医生的手动测量结果高度一致（ = 0.996）。对于强化效果欠佳的情况，使用低于250 HU的阈值，自动分类的灵敏度和特异度分别为100%和99.5%。该算法在2019年1月至2021年5月期间随机抽取的3195例CTPA检查中得到进一步评估。从2021年1月开始，扫描方案从团注追踪转变为定时团注策略。对这些检查的自动分析表明，方案改变后大多数强化效果欠佳的检查是使用一台扫描仪进行的，这凸显了深度学习算法在放射科质量改进方面的潜在价值。CT血管造影，肺动脉 © RSNA，2022。
