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Artificial intelligence tool detection of intravenous contrast enhancement using spleen attenuation

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Abstract

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.

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Funding

This research was supported in part by the Intramural Research Program of the National Institutes of Health Clinical Center.

Author information

Author notes

  1. Ronald M. Summers and Perry J. Pickhardt serve as joint senior authors of this manuscript.

Authors and Affiliations

  1. Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA

    B. Dustin Pooler, Cullen J. Fleming, John W. Garrett & Perry J. Pickhardt

  2. Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Department of Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, MD, USA

    Ronald M. Summers

Authors
  1. B. Dustin Pooler
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  2. Cullen J. Fleming
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Contributions

All authors (BDP, CJF, JWG, RMS, PJP) participated in and contributed materially to the design of this study, as well as manuscript writing, production, and editing. The decision to publish and final submitted manuscript was approved by all authors. Data collection was conducted by JWG, CJF, and BDP. Data and statistical analysis were conducted principally by BDP and JWG. BDP and PJP serve as joint guarantors for the study and accept full responsibility for the finished work.

Corresponding author

Correspondence to B. Dustin Pooler.

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Conflict of interest

There are no competing interests or conflicts of interest for any author. The authors wish to disclose the following non-competing interests: BDP, CJF, and JWG: nothing to disclose; PJP: advisor to Bracco, GE Healthcare, and Nano-X. RMS receives royalties from iCAD, ScanMed, PingAn, Philips, and Translation Holdings. His lab received research funding through a Cooperative Research and Development Agreement with PingAn.

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Pooler, B.D., Fleming, C.J., Garrett, J.W. et al. Artificial intelligence tool detection of intravenous contrast enhancement using spleen attenuation. Abdom Radiol 48, 3382–3390 (2023). https://doi.org/10.1007/s00261-023-04020-x

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  • DOI: https://doi.org/10.1007/s00261-023-04020-x

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