A Machine Learning Framework Reduces the Manual Workload for Systematic Reviews of the Diagnostic Performance of Prostate Magnetic Resonance Imaging.

Andrea Nedelcu,Christine Schmucker,Ivo G Schoots,August Sigle,Matthias Benndorf,Benedict Oerther,Michel Eisenblätter,Hannes Engel

doi:10.1016/j.euros.2023.07.005

A Machine Learning Framework Reduces the Manual Workload for Systematic Reviews of the Diagnostic Performance of Prostate Magnetic Resonance Imaging.

Andrea Nedelcu, Christine Schmucker + Show 6 more

Open Access

https://doi.org/10.1016/j.euros.2023.07.005

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Journal: European Urology Open Science	Publication Date: Oct 1, 2023
License type: cc-by-nc-nd

Affiliation: University of Freiburg, Erasmus MC, Klinikum Lippe, Bielefeld University

#Performance Of Imaging #Prostate Magnetic Resonance Imaging + Show 8 more

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Abstract

Systematic reviews of scientific evidence are labor-intensive and take a lot of time. For example, many studies on prostate cancer diagnosis via MRI (magnetic resonance imaging) are published every year. We describe the use of machine learning to reduce the manual workload in screening search results. For a review of MRI for prostate cancer diagnosis, this approach reduced the screening workload by about 28%.

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