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Abstract #4891

Economical and Diagnostic Quality Aspects Relevant for a Radiology Department Considering Implementation of Deep Learning – Augmented MRI

Roland Bammer1, Justin Warner2, Jonathan Richer3, Callum Blackmore3, Daniel Staeb3, Jin Jin3, Roman Blagin2, Douglas Anderson2, Daniel Saddik2, Alto Stemmer3, Dominik Nickel3, Kieran O'Brien3, and Shalini A. Amukotuwa1
1Radiology and Radiological Sciences, Monash University, Clayton, Australia, 2Monash Imaging, Monash Health, Clayton, Australia, 3Siemens Healthineers, Erlangen, Germany

Synopsis

Keywords: Multiple Sclerosis, MR Value, Neuro everything, Radiology Business, MRI throughput, Value for Money

Motivation: Patient throughput, while maintaining image quality, remains a major concern of radiology departments, especially with ever-increasing referral numbers/waitlists.

Goal(s): To comparatively assess the diagnostic quality of neuroimaging studies with/without commercial deep learning MRI and its economic impact.

Approach: 20 most-frequent MRI neuroimaging-protocols underwent comparative assessments of patient scan quality and speed with/without 2D/3D Deep Resolve and assessed its economic impact due to freed-up time.

Results: Average scan time was reduced by almost 34%, enabling us to shorten scan slots from 30 to 20min while improving quality. At current software costs and reimbursement levels, initial expenses can be earned back within <5 months.

Impact: We identified the most time-consuming scan protocols by weighting the frequency of utilization by protocol duration. Leveraging 2D+3D DL reconstruction, these protocols were dramatically shortened, freeing up approximately 34% of MRI time, allowing increased throughput while improving diagnostic quality.

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Keywords