Financial impact of incorporating deep learning reconstruction into magnetic resonance imaging routine
Mikael Brix, Jyri Järvinen, Michaela K. Bode, Mika Nevalainen, Marko Nikki, Jaakko Niinimäki, Eveliina Lammentausta
Abstract
PURPOSE: Artificial intelligence and deep learning solutions are increasingly utilized in healthcare and radiology. The number of studies addressing their enhancement of productivity and monetary impact is, however, still limited. Our hospital has faced a need to enhance MRI scanner throughput, and we investigate the utility of new commercial deep learning reconstruction (DLR) algorithm for this purpose. In this work, a multidisciplinary team evaluated the impact of the widespread deployment of a new commercial deep learning reconstruction (DLR) algorithm for our magnetic resonance imaging scanner fleet. METHODS: Our analysis centers on the DLR algorithm's effects on patient throughput and investment costs, contrasting these with alternative strategies for capacity expansion-namely, acquiring additional MRI scanners and increasing device utilization on weekends. We provide a framework for assessing the financial implications of new technologies in a trial phase, aiding in informed decision-making for healthcare investments. RESULTS: We demonstrate substantial reductions in total operating costs compared to other capacity-enhancing methods. Specifically, the cost of adopting the deep learning technology for our entire scanner fleet is only 11 % compared to procuring an additional scanner and 20 % compared to the weekend utilization costs of existing devices. CONCLUSIONS: Procuring DLR for our existing five-scanner fleet allows us to sustain our current MRI service levels without the need for an additional scanner, thereby achieving considerable cost savings. These reductions highlight the efficiency and economic viability of DLR in optimizing MRI service delivery.