Lifespan Pancreas Morphology for Control vs Type 2 Diabetes using AI on Largescale Clinical Imaging
Lucas W. Remedios, Chloe Cho, Trent M. Schwartz, Dingjie Su, Gaurav Rudravaram, Chenyu Gao, Aravind R. Krishnan, Adam M. Saunders, Michael E. Kim, Shunxing Bao, Thomas A. Lasko, Alvin C. Powers, Bennett A. Landman, John Virostko

TL;DR
This study uses AI to analyze large-scale clinical imaging data, establishing normative pancreatic aging trends and identifying morphological deviations associated with type 2 diabetes across the lifespan.
Contribution
It introduces a robust AI-based method for measuring pancreas morphology from CT and MRI, and provides the first comprehensive lifespan morphological reference including deviations linked to type 2 diabetes.
Findings
Pancreas size and shape differ significantly in type 2 diabetes.
MRI and CT measurements yield different pancreas morphological data.
The pancreas is generally smaller in individuals with type 2 diabetes.
Abstract
Purpose: Understanding how the pancreas changes is critical for detecting deviations in type 2 diabetes and other pancreatic disease. We measure pancreas size and shape using morphological measurements from ages 0 to 90. Our goals are to 1) identify reliable clinical imaging modalities for AI-based pancreas measurement, 2) establish normative morphological aging trends, and 3) detect potential deviations in type 2 diabetes. Approach: We analyzed a clinically acquired dataset of 2533 patients imaged with abdominal CT or MRI. We resampled the scans to 3mm isotropic resolution, segmented the pancreas using automated methods, and extracted 13 morphological pancreas features across the lifespan. First, we assessed CT and MRI measurements to determine which modalities provide consistent lifespan trends. Second, we characterized distributions of normative morphological patterns stratified by…
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Taxonomy
TopicsArtificial Intelligence in Healthcare · COVID-19 diagnosis using AI
