[1]栾庆贤,虞靖彬,王敏.深度学习在肥胖相关影像分析中的应用[J].中国医学物理学杂志,2026,43(9):1283-1289.[doi:DOI:10.3969/j.issn.1005-202X.2026.09.017]
　LUAN Qingxian? YU Jingbin,WANG Min?.Application of deep learning in obesity-related image analysis[J].Chinese Journal of Medical Physics,2026,43(9):1283-1289.[doi:DOI:10.3969/j.issn.1005-202X.2026.09.017]
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深度学习在肥胖相关影像分析中的应用()

《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:
1283-1289
栏目:
医学人工智能
出版日期:
2026-09-30

文章信息/Info

Title:
Application of deep learning in obesity-related image analysis
文章编号:
1005-202X（2026）09-1283-07
作者:
栾庆贤1; 虞靖彬2; 王敏3
1.山东中医药大学医学信息工程学院， 山东 济南 250118；2.山东中医药大学医学信息工程学院， 山东 济南 250355；3.山东中医药大学实验中心， 山东 济南 250355
Author(s):
LUAN Qingxian? YU Jingbin2;  WANG Min3?
1. School of Medical Informational Engineering, Shandong University of Traditional Chinese Medicine, Jinan 250118, China 2. School of Medical Informational Engineering, Shandong University of Traditional Chinese Medicine, Jinan 250355, China 3. Experimental Center of Shandong University of Traditional Chinese Medicine, Jinan 250355, China
关键词:
深度学习; 肥胖; 影像分析; 脂肪分割; 体成分量化; 临床应用; 综述
Keywords:
Keywords: deep learning obesity image analysis obesity segmentation body composition quantification clinical application review
分类号:
R318；TP391.41
DOI:
DOI:10.3969/j.issn.1005-202X.2026.09.017
文献标志码:
A
摘要:
为系统梳理深度学习在肥胖相关影像分析中的应用现状与临床价值，本文检索CNKI、PubMed、Web of Science等数据库，按“算法类型-影像模态-分析目标-临床应用”框架，重点对比不同算法的核心性能与落地效果。结果显示：深度学习在肥胖影像领域的核心应用涵盖脂肪组织（皮下/内脏脂肪）分割与量化、体成分精准评估、肥胖相关并发症（非酒精性脂肪肝、心血管疾病、骨关节病变）影像诊断3大方向。临床应用中，深度学习较传统人工测量平均节省60%~80%时间，脂肪量化准确率提升10%~20%，为肥胖精准防控、并发症早期诊断及治疗效果监测提供标准化量化工具。本文进一步剖析当前技术层面（模型可解释性、数据标准化）与临床层面（个性化适配、诊疗流程融合）的核心挑战，最后展望轻量化算法研发、多模态数据融合及基层医疗适配等未来发展方向，为深度学习在肥胖精准诊疗中的规范化应用提供参考。
Abstract:
Abstract: To systematically sort out the application status and clinical value of deep learning in obesity-related image analysis, this study retrieves databases including CNKI, PubMed, and Web of Science, and focuses on comparing the core performance and practical implementation outcomes of different algorithms in accordance with the framework of "algorithm type-imaging modality-analysis objective-clinical application". Results show that the applications of deep learning in obesity imaging cover 3 major directions: segmentation and quantification of adipose tissue (subcutaneous/visceral fat), accurate assessment of body composition, and imaging diagnosis of obesity-related complications (non-alcoholic fatty liver disease, cardiovascular diseases, osteoarticular lesions). In clinical applications, compared with traditional manual measurement, deep learning achieves an average reduction of 60% to 80% in time cost and improves the accuracy of fat quantification by 10% to 20%, providing a standardized quantitative tool for the precise prevention and control of obesity, early diagnosis of complications, and monitoring of therapeutic efficacy. This study further analyzes the core challenges at the current technical level (model interpretability, data standardization) and clinical level (personalized adaptation, integration with diagnosis and treatment processes). Finally, it outlines future development directions such as the research and development of lightweight algorithms, multi-modal data fusion, and adaptation for primary medical care, so as to provide a reference for the standardized application of deep learning in the precise diagnosis and treatment of obesity.

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备注/Memo

备注/Memo:
【收稿日期】2026-06-20 【基金项目】山东省中医药科技发展计划（M20241741）；济南市市校融合发展战略工程项目（JNSX2025014, JNSX2024020, JNSX2021043） 【作者简介】栾庆贤，硕士研究生，研究方向：医学图像处理与深度学习，E-mail: 254817235@qq.com 【通信作者】虞靖彬，硕士，副教授，研究方向：生物医学工程，E-mail: 15866700768@163.com
更新日期/Last Update: 2026-09-30