An Extra Set of Intelligent Eyes: Application of Artificial Intelligence in Imaging of Abdominopelvic Pathologies in Emergency Radiology

Overview

Journal Diagnostics (Basel)

Specialty Radiology

Date 2022 Jun 24

PMID 35741161

Authors

Jeffrey Liu

Bino Varghese

Farzaneh Taravat

Liesl S Eibschutz

Ali Gholamrezanezhad

Affiliations

Soon will be listed here.

Abstract

Imaging in the emergent setting carries high stakes. With increased demand for dedicated on-site service, emergency radiologists face increasingly large image volumes that require rapid turnaround times. However, novel artificial intelligence (AI) algorithms may assist trauma and emergency radiologists with efficient and accurate medical image analysis, providing an opportunity to augment human decision making, including outcome prediction and treatment planning. While traditional radiology practice involves visual assessment of medical images for detection and characterization of pathologies, AI algorithms can automatically identify subtle disease states and provide quantitative characterization of disease severity based on morphologic image details, such as geometry and fluid flow. Taken together, the benefits provided by implementing AI in radiology have the potential to improve workflow efficiency, engender faster turnaround results for complex cases, and reduce heavy workloads. Although analysis of AI applications within abdominopelvic imaging has primarily focused on oncologic detection, localization, and treatment response, several promising algorithms have been developed for use in the emergency setting. This article aims to establish a general understanding of the AI algorithms used in emergent image-based tasks and to discuss the challenges associated with the implementation of AI into the clinical workflow.

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References

Dreizin D, Zhou Y, Fu S, Wang Y, Li G, Champ K . A Multiscale Deep Learning Method for Quantitative Visualization of Traumatic Hemoperitoneum at CT: Assessment of Feasibility and Comparison with Subjective Categorical Estimation. Radiol Artif Intell. 2020; 2(6):e190220. PMC: 7706875. DOI: 10.1148/ryai.2020190220. View

Henes F, Nuchtern J, Groth M, Habermann C, Regier M, Rueger J . Comparison of diagnostic accuracy of Magnetic Resonance Imaging and Multidetector Computed Tomography in the detection of pelvic fractures. Eur J Radiol. 2011; 81(9):2337-42. DOI: 10.1016/j.ejrad.2011.07.012. View

Davuluri P, Wu J, Tang Y, Cockrell C, Ward K, Najarian K . Hemorrhage detection and segmentation in traumatic pelvic injuries. Comput Math Methods Med. 2012; 2012:898430. PMC: 3418697. DOI: 10.1155/2012/898430. View

Park J, Kim K, Nam Y, Choi M, Choi S, Rhie J . Convolutional-neural-network-based diagnosis of appendicitis via CT scans in patients with acute abdominal pain presenting in the emergency department. Sci Rep. 2020; 10(1):9556. PMC: 7293232. DOI: 10.1038/s41598-020-66674-7. View

Demetriades D, Karaiskakis M, Toutouzas K, Alo K, Velmahos G, Chan L . Pelvic fractures: epidemiology and predictors of associated abdominal injuries and outcomes. J Am Coll Surg. 2002; 195(1):1-10. DOI: 10.1016/s1072-7515(02)01197-3. View

Rubin D . Artificial Intelligence in Imaging: The Radiologist's Role. J Am Coll Radiol. 2019; 16(9 Pt B):1309-1317. PMC: 6733578. DOI: 10.1016/j.jacr.2019.05.036. View

De Bruijne M, van Ginneken B, Viergever M, Niessen W . Interactive segmentation of abdominal aortic aneurysms in CTA images. Med Image Anal. 2004; 8(2):127-38. DOI: 10.1016/j.media.2004.01.001. View

Krogue J, Cheng K, Hwang K, Toogood P, Meinberg E, Geiger E . Automatic Hip Fracture Identification and Functional Subclassification with Deep Learning. Radiol Artif Intell. 2021; 2(2):e190023. PMC: 8017394. DOI: 10.1148/ryai.2020190023. View

Moore C, Todd W, OBrien E, Lin H . Free fluid in Morison's pouch on bedside ultrasound predicts need for operative intervention in suspected ectopic pregnancy. Acad Emerg Med. 2007; 14(8):755-8. DOI: 10.1197/j.aem.2007.04.010. View

10.

Hom J, Kaplan C, Fowler S, Messina C, Chandran L, Kunkov S . Evidence-Based Diagnostic Test Accuracy of History, Physical Examination, and Imaging for Intussusception: A Systematic Review and Meta-analysis. Pediatr Emerg Care. 2020; 38(1):e225-e230. DOI: 10.1097/PEC.0000000000002224. View

11.

Giger M . Machine Learning in Medical Imaging. J Am Coll Radiol. 2018; 15(3 Pt B):512-520. DOI: 10.1016/j.jacr.2017.12.028. View

12.

Sakalihasan N, Limet R, Defawe O . Abdominal aortic aneurysm. Lancet. 2005; 365(9470):1577-89. DOI: 10.1016/S0140-6736(05)66459-8. View

13.

Ali S, Mohammad H, Sara S . Evaluation of the relationship between pelvic fracture and abdominal compartment syndrome in traumatic patients. J Emerg Trauma Shock. 2013; 6(3):176-9. PMC: 3746438. DOI: 10.4103/0974-2700.115330. View

14.

Benjamens S, Dhunnoo P, Mesko B . The state of artificial intelligence-based FDA-approved medical devices and algorithms: an online database. NPJ Digit Med. 2020; 3:118. PMC: 7486909. DOI: 10.1038/s41746-020-00324-0. View

15.

Subasic M, Loncaric S, Sorantin E . 3-D image analysis of abdominal aortic aneurysm. Stud Health Technol Inform. 2001; 77:1195-200. View

16.

Thompson W, Kilani R, Smith B, Thomas J, Jaffe T, DeLong D . Accuracy of abdominal radiography in acute small-bowel obstruction: does reviewer experience matter?. AJR Am J Roentgenol. 2007; 188(3):W233-8. DOI: 10.2214/AJR.06.0817. View

17.

Hallas P, Ellingsen T . Errors in fracture diagnoses in the emergency department--characteristics of patients and diurnal variation. BMC Emerg Med. 2006; 6:4. PMC: 1386703. DOI: 10.1186/1471-227X-6-4. View

18.

Ukai K, Rahman R, Yagi N, Hayashi K, Maruo A, Muratsu H . Detecting pelvic fracture on 3D-CT using deep convolutional neural networks with multi-orientated slab images. Sci Rep. 2021; 11(1):11716. PMC: 8175387. DOI: 10.1038/s41598-021-91144-z. View

19.

Recht M, Dewey M, Dreyer K, Langlotz C, Niessen W, Prainsack B . Integrating artificial intelligence into the clinical practice of radiology: challenges and recommendations. Eur Radiol. 2020; 30(6):3576-3584. DOI: 10.1007/s00330-020-06672-5. View

20.

Behzadi-Khormouji H, Rostami H, Salehi S, Derakhshande-Rishehri T, Masoumi M, Salemi S . Deep learning, reusable and problem-based architectures for detection of consolidation on chest X-ray images. Comput Methods Programs Biomed. 2019; 185:105162. DOI: 10.1016/j.cmpb.2019.105162. View