Artificial intelligence for abdominopelvic trauma imaging: trends, gaps, and future directions.
Abdominopelvic trauma is a major cause of morbidity and mortality, typically resulting from high-energy mechanisms such as motor vehicle collisions and penetrating injuries. Admission abdominopelvic trauma CT, performed either selectively or as part of a whole-body CT protocol, has become the workho...
| Publicado en: | Abdominal Radiology Vol. 50; no. 10; pp. 5028 - 5044 |
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| Autores principales: | , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Oct2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=188150356&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 188150356 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 2366004X JT14 jtl: Abdominal Radiology issn: 2366004X maglogo: N pubinfo: dt: Oct2025 vid: 50 iid: 10 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 188150356 183890155 10.1007/s00261-025-04816-z 188150356 ppf: 5028 ppct: 16 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Artificial intelligence for abdominopelvic trauma imaging: trends, gaps, and future directions. aug: au: Dreizin, David Cheng, Chi-Tung Liao, Chien-Hung Jindal, Ankush Colak, Errol affil: https://ror.org/055yg0521 University of Maryland Trauma Radiology AI Laboratory (TRAIL), University of Maryland School of Medicine, Baltimore, USA sug: ab: Abdominopelvic trauma is a major cause of morbidity and mortality, typically resulting from high-energy mechanisms such as motor vehicle collisions and penetrating injuries. Admission abdominopelvic trauma CT, performed either selectively or as part of a whole-body CT protocol, has become the workhorse screening and surgical planning modality due to improvements in speed and image quality. Radiography remains an essential element of the secondary trauma survey, and Focused Assessment with Sonography for Trauma (FAST) scanning has added value for quick assessment of non-compressible hemorrhage in hemodynamically unstable patients. Complex and severe polytrauma cases often delay radiology report turnaround times, which can potentially impede urgent clinical decision-making. Artificial intelligence (AI) computer-aided detection and diagnosis (CAD) offers promising solutions for enhanced diagnostic efficiency and accuracy in abdominopelvic trauma imaging. Although commercial AI tools for abdominopelvic trauma are currently available for only a few use cases, the literature reveals robust research and development (R&D) of prototype tools. Multiscale convolutional neural networks (CNNs) and transformer-based models are capable of detecting and quantifying solid organ injuries, fractures, and hemorrhage with a high degree of precision. Further, generalist foundation models such as multimodal vision-language models (VLMs) can be adapted and fine-tuned using imaging, clinical, and text data for a range of tasks, including detection, quantitative visualization, prognostication, and report auto-generation. Despite their promise, for most use cases in abdominopelvic trauma, AI CAD tools remain in the pilot stages of technology readiness, with persistent challenges related to data availability; the need for open-access PACS compatible software pipelines for pre-clinical shadow-testing; lack of well-designed multi-institutional validation studies; and regulatory hurdles. This narrative review provides a snapshot of the current state of AI in abdominopelvic trauma, examining existing commercial tools; research and development throughout the technology readiness pipeline; and future directions in this domain. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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