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Artificial intelligence CAD tools in trauma imaging: a scoping review from the American Society of Emergency Radiology (ASER) AI/ML Expert Panel

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Abstract

Background

AI/ML CAD tools can potentially improve outcomes in the high-stakes, high-volume model of trauma radiology. No prior scoping review has been undertaken to comprehensively assess tools in this subspecialty.

Purpose

To map the evolution and current state of trauma radiology CAD tools along key dimensions of technology readiness.

Methods

Following a search of databases, abstract screening, and full-text document review, CAD tool maturity was charted using elements of data curation, performance validation, outcomes research, explainability, user acceptance, and funding patterns. Descriptive statistics were used to illustrate key trends.

Results

A total of 4052 records were screened, and 233 full-text articles were selected for content analysis. Twenty-one papers described FDA-approved commercial tools, and 212 reported algorithm prototypes. Works ranged from foundational research to multi-reader multi-case trials with heterogeneous external data. Scalable convolutional neural network–based implementations increased steeply after 2016 and were used in all commercial products; however, options for explainability were narrow. Of FDA-approved tools, 9/10 performed detection tasks. Dataset sizes ranged from < 100 to > 500,000 patients, and commercialization coincided with public dataset availability. Cross-sectional torso datasets were uniformly small. Data curation methods with ground truth labeling by independent readers were uncommon. No papers assessed user acceptance, and no method included human–computer interaction. The USA and China had the highest research output and frequency of research funding.

Conclusions

Trauma imaging CAD tools are likely to improve patient care but are currently in an early stage of maturity, with few FDA-approved products for a limited number of uses. The scarcity of high-quality annotated data remains a major barrier.

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Funding

David Dreizin funding source: NIH K08 EB027141-01A1 (PI: David Dreizin, MD)

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Authors and Affiliations

  1. Department of Diagnostic Radiology and Nuclear Medicine, R Adams Cowley Shock Trauma Center, University of Maryland School of Medicine, Baltimore, MD, USA

    David Dreizin

  2. Cardiothoracic Imaging, Department of Radiology, Larner College of Medicine, University of Vermont, Burlington, VT, USA

    Pedro V. Staziaki

  3. Department of Radiology, University of Washington School of Medicine, Seattle, WA, USA

    Garvit D. Khatri

  4. Memorial Hermann Orthopedic & Spine Hospital, McGovern Medical School at UTHealth, Houston, TX, USA

    Nicholas M. Beckmann

  5. Epidemiology & Public Health, University of Maryland School of Medicine, Baltimore, MD, USA

    Zhaoyong Feng & Yuanyuan Liang

  6. Division of Emergency Radiology, Department of Radiology, University of Michigan, Ann Arbor, MI, USA

    Zachary S. Delproposto

  7. Sheba Medical Center, Ramat Gan, Israel

    Maximiliano Klug

  8. Department of Radiology, University of Alabama at Birmingham Heersink School of Medicine, Birmingham, AL, USA

    J. Stephen Spann

  9. University of Maryland School of Medicine, Baltimore, MD, USA

    Nathan Sarkar

  10. Health Sciences and Human Services Library, University of Maryland, Baltimore, Baltimore, MD, USA

    Yunting Fu

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  1. David Dreizin
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Correspondence to David Dreizin.

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Dreizin, D., Staziaki, P.V., Khatri, G.D. et al. Artificial intelligence CAD tools in trauma imaging: a scoping review from the American Society of Emergency Radiology (ASER) AI/ML Expert Panel. Emerg Radiol 30, 251–265 (2023). https://doi.org/10.1007/s10140-023-02120-1

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