Artificial intelligence-based tools with automated segmentation and measurement on CT images to assist accurate and fast diagnosis in acute pancreatitis

Xuhang Pan; Kaijian Jiao; Xinyu Li; Linshuang Feng; Yige Tian; Lei Wu; Peng Zhang; Kejun Wang; Suping Chen; Bo Yang; Wen Chen

doi:10.1093/bjr/tqae091

Artificial intelligence-based tools with automated segmentation and measurement on CT images to assist accurate and fast diagnosis in acute pancreatitis

Br J Radiol. 2024 May 10:tqae091. doi: 10.1093/bjr/tqae091. Online ahead of print.

Authors

Xuhang Pan^{1

2}, Kaijian Jiao^{1

2}, Xinyu Li^{1

2}, Linshuang Feng², Yige Tian², Lei Wu^{1

2}, Peng Zhang^{1

2}, Kejun Wang¹, Suping Chen³, Bo Yang^{1

2}, Wen Chen^{1

2}

Affiliations

¹ Institute of Medical Imaging, Department of Radiology, Taihe Hospital, Hubei University of Medicine, Shiyan, China.
² School of Biomedical Engineering, Hubei University of Medicine, Shiyan, China.
³ Advanced Application Team, GE Healthcare, Shanghai, China.

PMID: 38730541
DOI: 10.1093/bjr/tqae091

Abstract

Purpose: To develop an artificial intelligence (AI) tool with automated pancreas segmentation and measurement of pancreatic morphological information on CT images to assist improved and faster diagnosis in acute pancreatitis.

Methods: This study retrospectively contained 1124 patients suspected for AP and received non-contrast and enhanced abdominal CT examination between September 2013 to September 2022. Patients were divided into training (N = 688), validation (N = 145), testing dataset (N = 291, N = 104 for normal pancreas, N = 98 for AP, N = 89 for AP complicated with PDAC (AP&PDAC)). A model based on convolutional neural network (MSAnet) was developed. The pancreas segmentation and measurement were performed via eight open-source models and MSAnet based tools, and the efficacy was evaluated using Dice similarity coefficient (DSC) and Intersection over union (IoU). The DSC and IoU for patients with different ages were also compared. The outline of tumor and edema in the AP and were segmented by clustering. The diagnostic efficacy for radiologists with or without the assistance of MSAnet tool in AP and AP&PDAC was evaluated using receiver operation curve and confusion matrix.

Results: Among all models, MSAnet based tool showed best performance on the training and validation dataset, and had high efficacy on testing dataset. The performance was age-affected. With assistance of the AI tool, the diagnosis time was significantly shortened by 26.8% and 32.7% for junior and senior radiologists, respectively. The area under curve in diagnosis of AP was improved from 0.91 to 0.96 for junior radiologist and 0.98 to 0.99 for senior radiologist. In AP&PDAC diagnosis, AUC was increased from 0.85 to 0.92 for junior and 0.97 to 0.99 for senior.

Conclusion: MSAnet based tools showed good pancreas segmentation and measurement performance, which help radiologists improve diagnosis efficacy and workflow in both AP and AP with PDAC conditions.

Advances in knowledge: This study developed an AI tool with automated pancreas segmentation and measurement and provided evidence for AI tool assistance in improving the workflow and accuracy of AP diagnosis.