Deep Learning Aids Diagnosis of Chronic Pancreatitis

TOPLINE:

A deep learning (DL)–based model achieved a high accuracy in pancreas segmentation for patients with chronic pancreatitis (CP) and healthy individuals, a new study finds. The model showed robust performance across diverse scanning protocols and anatomic variations, although its accuracy was affected by visceral fat area and pancreas volume.

METHODOLOGY:

• Researchers developed a DL-based tool using the neural network U-Net (nnU-Net) architecture for the automated segmentation of retrospectively collected CT scans of the pancreas of healthy individuals and of patients with CP.
• Scans were obtained from one hospital each in Aalborg (n = 373; 223 patients with CP and 150 healthy individuals) and Bergen (n = 97 patients with CP), along with an online dataset from the National Institutes of Health (NIH; n = 80 healthy individuals).
• The tool was validated and tested using internal and external datasets, and its performance was compared with manual processing done by radiologists using the Sørensen-Dice index.
• The tool’s performance was examined for potential correlation with factors including visceral fat area at the third lumbar level, pancreas volume, and CT scan parameters.

TAKEAWAY:

• The tool demonstrated strong performance with mean Sørensen-Dice scores of 0.85 for the Aalborg test dataset, 0.79 for the Bergen dataset, and 0.79 for the NIH dataset.
• Sørensen-Dice scores were positively correlated with visceral fat area across datasets (correlation coefficient [r], 0.45; P < .0001) and with pancreas volume in the Aalborg test dataset (r, 0.53; P = .0002).
• CT scan parameters had no significant effect on model performance.
• The tool maintained accuracy across diverse anatomic variations, except in cases with severe pancreatic fat infiltration.

IN PRACTICE:

“This study presents a novel AI [artificial intelligence]–based pancreas segmentation model trained on both healthy individuals and CP [chronic pancreatitis] patients, demonstrating consistent and robust performance across internal and external test datasets that vary in patient characteristics and scanner parameters. The model has the potential to significantly enhance the efficiency and accuracy of pancreas segmentation in clinical practice and research, particularly for CP patients with complex anatomical features,” the authors wrote.

SOURCE:

This study was led by Surenth Nalliah, Radiology Research Center, Department of Radiology, Aalborg University Hospital, Aalborg, Denmark. It was published online on May 14 in European Journal of Radiology.

LIMITATIONS:

Comprehensive hyperparameter optimisation was not performed due to computational constraints. Additionally, architectures beyond nnU-Net and other segmentation methods were not explored. Post hoc visualisation methods were not studied. Small sizes of datasets could have hindered model performance, and cases of severe pancreatic fat infiltration were not included. 

DISCLOSURES:

Funding information was not provided for this study. One author reported receiving financial support from Health Hub, founded by the Spar Nord Foundation.

This article was created using several editorial tools, including AI, as part of the process. Human editors reviewed this content before publication.