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

A Nedelcu, B Oerther, H Engel, A Sigle, C Schmucker, IG Schoots, M Eisenblätter, M Benndorf

Research output: Contribution to journalArticleAcademicpeer-review

21 Downloads (Pure)

Abstract

Prostate magnetic resonance imaging has become the imaging standard for prostate cancer in various clinical settings, with interpretation standardized according to the Prostate Imaging Reporting and Data System (PI-RADS). Each year, hundreds of scientific studies that report on the diagnostic performance of PI-RADS are published. To keep up with this ever-increasing evidence base, systematic reviews and meta-analyses are essential. As systematic reviews are highly resource-intensive, we investigated whether a machine learning framework can reduce the manual workload and speed up the screening process (title and abstract). We used search results from a living systematic review of the diagnostic performance of PI-RADS (1585 studies, of which 482 were potentially eligible after screening). A naïve Bayesian classifier was implemented in an active learning environment for classification of the titles and abstracts. Our outcome variable was the percentage of studies that can be excluded after 95% of relevant studies have been identified by the classifier (work saved over sampling: WSS@95%). In simulation runs of the entire screening process (controlling for classifier initiation and the frequency of classifier updating), we obtained a WSS@95% value of 28% (standard error of the mean ±0.1%). Applied prospectively, our classification framework would translate into a significant reduction in manual screening effort.
Original languageEnglish
Pages (from-to)11-14
Number of pages4
JournalEuropean Urology Open Science
Volume56
DOIs
Publication statusPublished - Oct 2023

Bibliographical note

Funding/Support and role of the sponsor: The underlying living systematic review is supported by a grant from the German Federal Ministry of
Education and Research (Bundesministerium für Bildung und Forschung, project 01KG2202). The sponsor played a role in data collection and management

Fingerprint

Dive into the research topics of 'A Machine Learning Framework Reduces the Manual Workload for Systematic Reviews of the Diagnostic Performance of Prostate Magnetic Resonance Imaging'. Together they form a unique fingerprint.

Cite this