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

Autor: Dreizin D; Department of Diagnostic Radiology and Nuclear Medicine, R Adams Cowley Shock Trauma Center, University of Maryland School of Medicine, Baltimore, MD, USA. daviddreizin@gmail.com., Staziaki PV; Cardiothoracic Imaging, Department of Radiology, Larner College of Medicine, University of Vermont, Burlington, VT, USA., Khatri GD; Department of Radiology, University of Washington School of Medicine, Seattle, WA, USA., Beckmann NM; Memorial Hermann Orthopedic & Spine Hospital, McGovern Medical School at UTHealth, Houston, TX, USA., Feng Z; Epidemiology & Public Health, University of Maryland School of Medicine, Baltimore, MD, USA., Liang Y; Epidemiology & Public Health, University of Maryland School of Medicine, Baltimore, MD, USA., Delproposto ZS; Division of Emergency Radiology, Department of Radiology, University of Michigan, Ann Arbor, MI, USA., Klug M; Sheba Medical Center, Ramat Gan, Israel., Spann JS; Department of Radiology, University of Alabama at Birmingham Heersink School of Medicine, Birmingham, AL, USA., Sarkar N; University of Maryland School of Medicine, Baltimore, MD, USA., Fu Y; Health Sciences and Human Services Library, University of Maryland, Baltimore, Baltimore, MD, USA.
Jazyk: angličtina
Zdroj: Emergency radiology [Emerg Radiol] 2023 Jun; Vol. 30 (3), pp. 251-265. Date of Electronic Publication: 2023 Mar 14.
DOI: 10.1007/s10140-023-02120-1
Abstrakt: 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.
(© 2023. The Author(s), under exclusive licence to American Society of Emergency Radiology (ASER).)
Databáze: MEDLINE
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