Institute of Medical Imaging, Department of Radiology, Taihe Hospital, Hubei University of Medicine, Shiyan 442000, China.
School of Biomedical Engineering, Hubei University of Medicine, Shiyan 442000, China.
Br J Radiol. 2024 Jun 18;97(1159):1268-1277. doi: 10.1093/bjr/tqae091.
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.
This study retrospectively contained 1124 patients suspected for AP and received non-contrast and enhanced abdominal CT examination between September 2013 and 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 tumour and oedema 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.
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 (AUC) 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.
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.
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.
开发一种人工智能（AI）工具，用于对 CT 图像进行胰腺自动分割和测量，以协助急性胰腺炎的快速、准确诊断。
本研究回顾性纳入了 2013 年 9 月至 2022 年 9 月间 1124 例疑似 AP 患者的非增强和增强腹部 CT 检查资料。患者被分为训练集（N=688）、验证集（N=145）和测试集[291 例；N=104 例为正常胰腺，N=98 例为 AP，N=89 例为 AP 合并 PDAC（AP&PDAC）]。基于卷积神经网络（MSAnet）建立模型。使用 8 个开源模型和 MSAnet 工具进行胰腺分割和测量，并通过 Dice 相似系数（DSC）和交并比（IoU）评估效能。比较了不同年龄患者的 DSC 和 IoU。通过聚类对 AP 中的肿瘤和水肿进行分割。使用受试者工作特征曲线和混淆矩阵评估 AI 工具辅助前后，初级和高级放射科医师对 AP 和 AP&PDAC 的诊断效能。
在所有模型中，MSAnet 工具在训练集和验证集上的表现最佳，在测试集上也具有较高的效能。其效能受到年龄的影响。在 AI 工具的辅助下，初级和高级放射科医师的诊断时间分别缩短了 26.8%和 32.7%。初级放射科医师对 AP 的诊断 AUC 从 0.91 提高到 0.96，高级放射科医师从 0.98 提高到 0.99。在 AP&PDAC 诊断中，初级放射科医师的 AUC 从 0.85 提高到 0.92，高级放射科医师从 0.97 提高到 0.99。
MSAnet 工具具有良好的胰腺分割和测量性能，有助于放射科医师提高 AP 及 AP&PDAC 诊断的效能和工作流程。
本研究开发了一种自动胰腺分割和测量的 AI 工具，为 AI 工具辅助提高 AP 诊断的工作流程和准确性提供了证据。
