中国组织工程研究 ›› 2024, Vol. 28 ›› Issue (9): 1426-1435.doi: 10.12307/2023.904
• 骨与关节综述 bone and joint review • 上一篇 下一篇
余伟杰1,2,3,刘爱峰1,2,陈继鑫1,2,3,郭天赐1,2,3,贾易臻1,2,3,冯汇川1,2,3,杨家麟1,2,3
收稿日期:
2022-11-25
接受日期:
2023-01-18
出版日期:
2024-03-28
发布日期:
2023-07-26
通讯作者:
刘爱峰,博士,主任医师,博士生导师,天津中医药大学第一附属医院,天津市 300381;国家中医针灸临床医学研究中心,天津市 300381
作者简介:
余伟杰,男,1995年生,云南省楚雄彝族自治州人,彝族,天津中医药大学在读博士,主要从事骨与关节疾病的中西医结合临床研究。
基金资助:
Yu Weijie1, 2, 3, Liu Aifeng1, 2, Chen Jixin1, 2, 3, Guo Tianci1, 2, 3, Jia Yizhen1, 2, 3, Feng Huichuan1, 2, 3, Yang Jialin1, 2, 3
Received:
2022-11-25
Accepted:
2023-01-18
Online:
2024-03-28
Published:
2023-07-26
Contact:
Liu Aifeng, MD, Chief physician, Doctoral supervisor, First Teaching Hospital of Tianjin University of Traditional Chinese Medicine, Tianjin 300381, China; National Clinical Research Center of Chinese Medicine Acupuncture and Moxibustion, Tianjin 300381, China
About author:
Yu Weijie, Doctoral candidate, First Teaching Hospital of Tianjin University of Traditional Chinese Medicine, Tianjin 300381, China; National Clinical Research Center of Chinese Medicine Acupuncture and Moxibustion, Tianjin 300381, China; Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China
Supported by:
摘要:
文题释义:
机器学习:作为人工智能领域的一个分支,机器学习涉及概率论、统计学和复杂算法理论等多门学科,通过学习和分析数据,预测并建立精确的数据模型。
背景:基于机器学习的不同算法,如何借助各种算法模型开展腰椎间盘突出症的临床研究已成为目前智能化医学发展的趋势和热点。
目的:综述机器学习不同算法模型在腰椎间盘突出症诊治中的特点,归纳相同用途的算法模型各自优势和应用策略。结果与结论:①机器学习的不同算法模型为腰椎间盘突出症的临床诊治提供了智能化、精准化的应用策略。②监督学习中的传统统计学方法和决策树在探究危险因素,制定诊断、预后模型方面简单高效;支持向量机适用于高维特征的小数据集,作为非线性分类器可应用于正常或退变椎间盘的识别、分割、分类,制定诊断、预后模型;集成学习可相互弥补单一模型的不足,具有处理高维数据的能力,提高临床预测模型的精度和准确性;人工神经网络提高了模型的学习能力,可应用于椎间盘识别和分类,制作临床预测模型;深度学习在具有以上用途的基础上,还能优化图像,辅助手术操作,是目前腰椎间盘突出症诊治中应用最广泛、性能最佳的模型;无监督学习中的聚类算法主要用于椎间盘分割和不同突出节段的分类;而半监督学习方式临床应用相对较少。③目前,机器学习在腰椎间盘的识别、分割,退变椎间盘的分类和分级,自动化临床诊断和分类,构建临床预测模型以及辅助术中操作方面具有一定临床优势。④近年来,机器学习的研究策略已向神经网络和深度学习方向转变,具有更强学习能力的深度学习算法将会是未来实现智能化医疗的关键。
https://orcid.org/0000-0002-5977-4268 (余伟杰);https://orcid.org/0000-0001-7318-1277 (刘爱峰)
中国组织工程研究杂志出版内容重点:人工关节;骨植入物;脊柱;骨折;内固定;数字化骨科;组织工程
中图分类号:
余伟杰, 刘爱峰, 陈继鑫, 郭天赐, 贾易臻, 冯汇川, 杨家麟. 机器学习在腰椎间盘突出症诊治中的优势和应用策略[J]. 中国组织工程研究, 2024, 28(9): 1426-1435.
Yu Weijie, Liu Aifeng, Chen Jixin, Guo Tianci, Jia Yizhen, Feng Huichuan, Yang Jialin. Advantages and application strategies of machine learning in diagnosis and treatment of lumbar disc herniation[J]. Chinese Journal of Tissue Engineering Research, 2024, 28(9): 1426-1435.
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1.1.7 检索策略 以PubMed和中国知网数据库检索策略为例,见图1。
1.1.8 检索文献量 初步检索得到文献2 869篇,包括英文文献 1 807篇,中文文献1 062篇。其中PubMed数据库674篇、Web of Science数据库673篇、EMBASE数据库460篇、中国知网数据库411篇、万方数据库524篇、维普数据库75篇、中国生物医学数据库52篇。
1.3 文献质量评价和数据提取 共检索到2 869篇文献,初筛剔除1 512篇重复文献后,对剩余1 357篇文献的标题、摘要进行筛选,无法判别时阅读全文,选取与主题相关的文献,再通过纳排标准进行文献筛选,最终纳入文献96篇,包括英文文献84篇,中文文献12篇,其中PubMed数据库78篇、Web of Science 数据库6篇、中国知网数据库12篇,文献筛选流程见图2。
文题释义:
机器学习:作为人工智能领域的一个分支,机器学习涉及概率论、统计学和复杂算法理论等多门学科,通过学习和分析数据,预测并建立精确的数据模型。机器学习是人工智能的重要研究领域,可应用于腰椎间盘突出症的诊断、治疗和预后研究中,是目前智能化医学发展的趋势和热点。文章通过分析不同机器学习算法的应用特点和优势,可根据临床应用需求设计相应的算法模型,进一步提高诊疗效果。文章总结和分析机器学习的不同算法模型及其在腰椎间盘突出症诊治中的应用进展。
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