Re-defining radiology quality assurance (QA): artificial intelligence (AI)-based QA by restricted investigation of unequal scores (AQUARIUS)

Axel Wismüller; Larry Stockmaster; M. Ali Vosoughi

doi:10.1117/12.2622234

27 May 2022 Re-defining radiology quality assurance (QA): artificial intelligence (AI)-based QA by restricted investigation of unequal scores (AQUARIUS)

Axel Wismüller, Larry Stockmaster, M. Ali Vosoughi

Author Affiliations +

Proceedings Volume 12101, Pattern Recognition and Tracking XXXIII; 121010E (2022) https://doi.org/10.1117/12.2622234
Event: SPIE Defense + Commercial Sensing, 2022, Orlando, Florida, United States

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.

Conference Presentation

Citation Download Citation

Axel Wismüller, Larry Stockmaster, and M. Ali Vosoughi "Re-defining radiology quality assurance (QA): artificial intelligence (AI)-based QA by restricted investigation of unequal scores (AQUARIUS)", Proc. SPIE 12101, Pattern Recognition and Tracking XXXIII, 121010E (27 May 2022); https://doi.org/10.1117/12.2622234

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