Automated Deep Learning Analysis for Quality Improvement of CT Pulmonary Angiography

• Published in: Radiology. Artificial intelligence 2022-03, Vol.4 (2), p.e210162-e210162
• Main Authors: Hahn, Lewis D, Hall, Kent, Alebdi, Thamer, Kligerman, Seth J, Hsiao, Albert
• Format: Article
• Language: English
• Subjects: AI in Brief
• ISSN: 2638-6100; 2638-6100
• DOI: 10.1148/RYAI.210162

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.