Artificial intelligence tool detection of intravenous contrast enhancement using spleen attenuation.

Autor: Pooler BD; Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA. bpooler@uwhealth.org., Fleming CJ; Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA., Garrett JW; Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA., Summers RM; Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Department of Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, MD, USA., Pickhardt PJ; Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA.
Jazyk: angličtina
Zdroj: Abdominal radiology (New York) [Abdom Radiol (NY)] 2023 Nov; Vol. 48 (11), pp. 3382-3390. Date of Electronic Publication: 2023 Aug 27.
DOI: 10.1007/s00261-023-04020-x
Abstrakt: Purpose: To assess the ability of an automated AI tool to detect intravenous contrast material (IVCM) in abdominal CT examinations using spleen attenuation.
Methods: A previously validated automated AI tool measuring the attenuation of the spleen was deployed on a sample of 32,994 adult (age ≥ 18) patients (mean age, 61.9 ± 14.7 years; 13,869 men, 19,125 women) undergoing 65,449 supine position CT examinations (41,020 with and 24,429 without IVCM by DICOM header) from January 1, 2000 to December 31, 2021. After exclusions, receiver operating characteristic (ROC) curve analysis was performed to determine the optimal threshold for binary classification of IVCM status (non-contrast vs IVCM enhanced), which was then applied to the sample. Discordant examinations (i.e., IVCM status determined by AI tool did not match DICOM header) were manually reviewed to establish ground truth. Repeat ROC curve and contingency table analysis were performed to assess AI tool performance.
Results: ROC analysis of the initial study sample of 61,783 CT examinations yielded AUC of 0.970 with Youden index suggesting an optimal spleen attenuation threshold of 65 Hounsfield units (HU). Manual review of 2094 discordant CT examinations revealed discordance due to DICOM header error in 1278 (61.0%) and AI tool misclassification in 410 (19.6%), with 406 (9.4%) meeting exclusion criteria. Analysis of 61,377 CT examinations in the final study sample yielded AUC of 0.999 with accuracy of 99.3% at the 65 HU threshold. Error rate for DICOM header information was 2.1% (1278/61,377) versus 0.7% (410/61,377) for the AI tool.
Conclusion: The automated spleen attenuation AI tool was highly accurate for detection of IVCM at a threshold of 65 HU.
(© 2023. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)
Databáze: MEDLINE
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