AI with volumetric thresholds facilitates opportunistic screening for

Leesburg, VA, June 30, 2023—according to the accepted manuscript published by ARRS itself American Journal of Roentgenology (AIR), using automated deep learning AI tools, as well as weight-based volumetric thresholds, can provide large-scale evaluations for splenomegaly on CT examinations performed for any indication.

Noting that, historically, standard linear spleen measurements used as a surrogate for spleen volume resulted in suboptimal performance in detecting volume-based splenomegaly, “a weight-based volumetric threshold indicates the presence of splenomegaly in the majority of patients undergoing pre-liver CT transplant,” explain the authors who in accordance Perry J. PickhardtMD, of the radiology department at the University of Wisconsin School of Medicine & Public Health.

Pickhardt and colleagues AIR Manuscripts received included a screening sample of 8,901 patients (4,235 men, 4,666 women; mean age, 56 years) who underwent CT colonoscopy (n = 7736) or CT donor kidney (n = 1165) from April 2004 to January 2017. Cohorts of 104 patients (62 males, 42 females; mean age, 56 years) with end-stage liver disease underwent pre-liver transplant CT from January 2011 to May 2013. The deep learning algorithm of Pickhardt et al.—previously developed, trained, and tested at the National Institutes of Health Clinical Center—used for segmentation of the spleen, to help determine spleen volume, with two radiologists independently reviewing part of the segmentation.

Ultimately, this automated deep learning AI tool was used to calculate spleen volume from CT examinations in 8,853 patients from the primary outpatient population. In addition, spleen volume was most strongly associated with body weight, among patient factors.

“To our knowledge,” it is AIR the authors concluded, “this study represents the largest sample of patients reported to have undergone splenic volumetric segmentation.”

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