Automated data extraction and report analysis in computer-aided radiology audit: practice implications from post-mortem paediatric imaging.

Affiliation

Department of Radiology, Great Ormond Street Hospital for Children NHS Foundation Trust, London, UK; UCL Great Ormond Street Institute of Child Health, London, UK. Electronic address: [Email]

Abstract

OBJECTIVE : To determine local departmental adherence to the paediatric post-mortem magnetic resonance imaging (MRI) protocols, using a customised automated computational approach.
METHODS : A retrospective review of 460 whole-body post-mortem MRI examinations performed at Great Ormond Street Hospital for Children over a 5.5-year period was assessed for adherence to a full or abbreviated imaging sequence protocol. A simple computer program was developed to batch process DICOM (digital imaging and communications in medicine) files, extracting imaging sequence details, followed by natural language processing (NLP) of authorised reports to automate information extraction of diagnostic image quality.
RESULTS : The program was able to extract study parameters from the entire dataset (approximately 80 GB of data) in a few hours, and retrieve information on diagnostic image quality using NLP with an overall diagnostic accuracy for data extraction of 96.7% (445/460, 95% confidence interval [CI]: 94.7-98%). The full imaging protocol was adhered to in 305/460 (66.3%) cases, and an abbreviated protocol in 140/460 (30.4%) cases. Overall, 423/460 (91.9%) of studies were of diagnostic quality. These included 298/305 (97.7%) of the full protocol, 111/140 (79.3%) of the abbreviated protocol. In only five cases were the examinations non-diagnostic for all body systems, all of whom weighed <100 g (24.7-72 g) and imaged using the abbreviated protocol.
CONCLUSIONS : The present study demonstrated a successful application of an automated approach for data collection for audit and quality assessment purposes using paediatric post-mortem imaging as a specific example. Re-audit of these data following change implementation will be straightforward now that the automated workflow is clearly established.

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