Imaging Pearls ❯ Spleen ❯ Artificial Intelligence
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- BACKGROUND. Splenomegaly historically has been assessed on imaging by use of potentially inaccurate linear measurements. Prior work tested a deep learning artificial intelligence (AI) tool that automatically segments the spleen to determine splenic volume.
OBJECTIVE. The purpose of this study is to apply the deep learning AI tool in a large screening population to establish volume-based splenomegaly thresholds.
METHODS. This retrospective study included a primary (screening) sample of 8901 patients (4235 men, 4666 women; mean age, 56 Å} 10 [SD] years) who underwent CT colonoscopy (n = 7736) or renal donor CT (n = 1165) from April 2004 to January 2017 and a secondary sample of 104 patients (62 men, 42 women; mean age, 56 Å} 8 years) with endstage liver disease who underwent contrast-enhanced CT performed as part of evaluation for potential liver transplant from January 2011 to May 2013. The automated deep learning AI tool was used for spleen segmentation, to determine splenic volumes.
Automated Deep Learning Artificial Intelligence Tool for Spleen Segmentation on CT: Defining Volume-Based Thresholds for Splenomegaly
Alberto A. Perez, Victoria Noe-Kim, Meghan G. Lubner, et al.
AJR 2023; 221:1–9 - RESULTS. In 8853 patients included in analysis of splenic volumes (i.e., excluding a value of 0 mL or error values), the mean automated splenicvolume was 216 Å} 100 [SD] mL. The weight-based volumetric threshold (expressed in milliliters) for splenomegaly was calculated as (3.01 Å~ weight [expressed as kilograms]) + 127; for weight greater than 125 kg, the splenomegaly threshold was constant (503 mL). Sensitivity and specificity for volume-defined splenomegaly were 13% and 100%, respectively, at a true craniocaudal length of 13 cm, and 78% and 88% for a maximum 3D length of 13 cm. In the secondary sample, both observers identified segmentation failure in one patient. The mean automated splenic volume in the 103 remaining patients was 796 Å} 457 mL; 84% (87/103) of patients met the weight-based volume-defined splenomegalythreshold. CONCLUSION. We derived a weight-based volumetric threshold for splenomegaly using an automated AI-based tool. CLINICAL IMPACT. The AI tool could facilitate large-scale opportunistic screening for splenomegaly.
Automated Deep Learning Artificial Intelligence Tool for Spleen Segmentation on CT: Defining Volume-Based Thresholds for Splenomegaly
Alberto A. Perez, Victoria Noe-Kim, Meghan G. Lubner, et al.
AJR 2023; 221:1–9 - Key Finding
„ A previously tested automated deep learning AI tool was used to calculate splenic volumes from the CT examinations of 8853 patients from an outpatient screening population. Splenic volume was most strongly associated with weight among a range of patient factors, and a weight-based volume-defined threshold for splenomegaly was derived.
Importance
„ Use of the automated deep learning AI tool and weightbased volumetric thresholds could allow large-scale evaluation for splenomegaly on CT examinations performed for any indication.
Automated Deep Learning Artificial Intelligence Tool for Spleen Segmentation on CT: Defining Volume-Based Thresholds for Splenomegaly
Alberto A. Perez, Victoria Noe-Kim, Meghan G. Lubner, et al.
AJR 2023; 221:1–9 - “In conclusion, we derived a simple weight-based volumetric threshold for determining the presence of splenomegaly using an automated AI-based tool for determining splenic volume from CT examinations. Standard linear splenic measurements (which historically have been used as a surrogate for splenic volume) had suboptimal performance in detecting volume-based splenomegaly, and the weight-based volumetric thresholds indicated the presence of splenomegaly in most patients who underwent pre–liver transplant CT. The AI tool could be applied for more robust evaluation for splenomegaly in comparison with linear measurements as well as for large-scale opportunistic screening for splenomegaly.”
Automated Deep Learning Artificial Intelligence Tool for Spleen Segmentation on CT: Defining Volume-Based Thresholds for Splenomegaly
Alberto A. Perez, Victoria Noe-Kim, Meghan G. Lubner, et al.
AJR 2023; 221:1–9