Re-defining Radiology Quality Assurance (QA) -- Artificial Intelligence (AI)-Based QA by Restricted Investigation of Unequal Scores (AQUARIUS)

TL;DR

This paper introduces AQUARIUS, an AI-based radiology QA system that significantly reduces human effort by automatically identifying cases needing expert review, demonstrated through intracranial hemorrhage detection in head CT scans.

Contribution

AQUARIUS combines AI image analysis with NLP report analysis to streamline radiology QA, reducing human review effort by over 98% in a clinical study.

Findings

Reduced QA human effort by 98.5%

Detected 6 missed ICH cases in 1936 scans

Maintained low missed detection rates of 0.52% and 2.5%

Abstract

There is an urgent need for streamlining radiology Quality Assurance (QA) programs to make them better and faster. Here, we present a novel approach, Artificial Intelligence (AI)-Based QUality Assurance by Restricted Investigation of Unequal Scores (AQUARIUS), for re-defining radiology QA, which reduces human effort by up to several orders of magnitude over existing approaches. AQUARIUS typically includes automatic comparison of AI-based image analysis with natural language processing (NLP) on radiology reports. Only the usually small subset of cases with discordant reads is subsequently reviewed by human experts. To demonstrate the clinical applicability of AQUARIUS, we performed a clinical QA study on Intracranial Hemorrhage (ICH) detection in 1936 head CT scans from a large academic hospital. Immediately following image acquisition, scans were automatically analyzed for ICH using a commercially available software (Aidoc, Tel Aviv, Israel). Cases rated positive for ICH by AI (ICH-AI+) were automatically flagged in radiologists' reading worklists, where flagging was randomly switched off with probability 50%. Using AQUARIUS with NLP on final radiology reports and targeted expert neuroradiology review of only 29 discordantly classified cases reduced the human QA effort by 98.5%, where we found a total of six non-reported true ICH+ cases, with radiologists' missed ICH detection rates of 0.52% and 2.5% for flagged and non-flagged cases, respectively. We conclude that AQUARIUS, by combining AI-based image analysis with NLP-based pre-selection of cases for targeted human expert review, can efficiently identify missed findings in radiology studies and significantly expedite radiology QA programs in a hybrid human-machine interoperability approach.