[1]郑小旺,李勇,张红宾.AI在急性胰腺炎的CT成像全流程中的应用与挑战：从图像采集、质量优化到精准诊断[J].中国医学物理学杂志,2026,43(9):1290-1298.[doi:DOI:10.3969/j.issn.1005-202X.2026.09.018]
　ZHENG Xiaowang? LI Yong? ZHANG Hongbin?/html>.Application and challenges of artificial intelligence in the entire CT imaging process for acute pancreatitis: from image acquisition, quality optimization to precision diagnosis[J].Chinese Journal of Medical Physics,2026,43(9):1290-1298.[doi:DOI:10.3969/j.issn.1005-202X.2026.09.018]
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AI在急性胰腺炎的CT成像全流程中的应用与挑战：从图像采集、质量优化到精准诊断()

《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]

卷:
43卷
期数:
2026年第9期
页码:
1290-1298
栏目:
医学人工智能
出版日期:
2026-09-30

文章信息/Info

Title:
Application and challenges of artificial intelligence in the entire CT imaging process for acute pancreatitis: from image acquisition, quality optimization to precision diagnosis
文章编号:
1005-202X（2026）09-1290-09
作者:
郑小旺1; 李勇1; 张红宾2
1.川北医学院附属医院放射科， 四川 南充 637000； 2.南充卫生职业学院附属医院临床营养科， 四川 南充 637000
Author(s):
ZHENG Xiaowang? LI Yong? ZHANG Hongbin?/html>
1. Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, China 2. Department of Clinical Nutrition, Affiliated Hospital of Nanchong Health Vocational College, Nanchong 637000, China
关键词:
急性胰腺炎; 计算机断层扫描; 人工智能; 图像采集; 质量优化; 综述
Keywords:
Keywords: acute pancreatitis computed tomography artificial intelligence image acquisition quality optimization review
分类号:
R318；R657.51；R814.42
DOI:
DOI:10.3969/j.issn.1005-202X.2026.09.018
文献标志码:
A
摘要:
急性胰腺炎（AP）作为一种常见且危重的腹部急症，早期准确诊断和有效管理对改善患者预后具有重要意义。计算机断层扫描（CT）因其较高的诊断敏感性和特异性，成为AP诊断和评估的关键影像学手段。然而，传统CT成像在图像质量、辐射剂量控制以及诊断结果的一致性方面存在诸多限制，影响其临床应用效果。近年来，人工智能（AI）技术的快速发展为CT成像全流程带来革命性变革，涵盖扫描方案制定、图像采集与重建、图像质量优化、后期处理、定量分析以及严重程度评估与预后预测等多个环节。AI不仅提升图像质量和诊断精度，还实现流程自动化和个体化医疗的初步探索。本文系统综述AI在AP的CT成像各环节的最新研究进展，分析目前技术应用中存在的算法泛化能力不足、数据隐私保护、临床验证缺乏等挑战，并展望未来多模态融合、深度学习模型优化及临床转化的研究方向，旨在为相关领域的科研和临床实践提供理论依据和参考。
Abstract:
Abstract: Acute pancreatitis (AP) is a common and critical abdominal emergency, and its early and accurate diagnosis and effective management are of great significance for improving patients prognosis. Computed tomography (CT) has become a critical imaging modality for the diagnosis and assessment of AP owing to its high diagnostic sensitivity and specificity. However, the application performance of traditional CT imaging is limited by drawbacks in image quality, radiation dose control, and consistency of diagnostic results. In recent years, the rapid development of artificial intelligence (AI) technology has brought revolutionary changes to the entire CT imaging process, covering multiple stages including scan protocol formulation, image acquisition and reconstruction, image quality optimization, post-processing, quantitative analysis, severity assessment, and prognosis prediction. AI not only enhances image quality and diagnostic accuracy, but also enables preliminary exploration of process automation and personalized medicine. This review systematically summarizes the research advances of AI in various stages of CT imaging for AP. Furthermore, it analyzes the current challenges in technical application, including insufficient algorithm generalization ability, unresolved data privacy issues and inadequate clinical validation, and discusses future research directions, such as multimodal fusion, deep learning model optimization and clinical translation. The findings are expected to provide a theoretical basis and reference for scientific research and clinical practice in related fields.

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备注/Memo

备注/Memo:
【收稿日期】2026-03-07 【基金项目】四川省科技计划项目（2023NSFSC1535）；川北医学院医学影像研究医学影像四川省重点实验室项目（MIKL202310） 【作者简介】郑小旺，技师，研究方向：医学影像技术，E-mail: 905121331@qq.com 【通信作者】张红宾，主治医师，研究方向：临床营养学，E-mail: zhblp99@sina.com
更新日期/Last Update: 2026-09-30