机器学习在腰椎间盘突出症诊治中的优势和应用策略

Advantages and application strategies of machine learning in diagnosis and treatment of lumbar disc herniation

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。方法:计算机检索PubMed、Web of Science、EMBASE、中国知网、万方数据、维普及中国生物医学数据库中与机器学习在腰椎间盘突出症诊治中的相关应用文献,按入组标准筛选后最终纳入96篇文献进行综述。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。

BACKGROUND:Based on different algorithms of machine learning,how to carry out clinical research on lumbar disc herniation with the help of various algorithmic models has become a trend and hot spot in the development of intelligent medicine at present.OBJECTIVE:To review the characteristics of different algorithmic models of machine learning in the diagnosis and treatment of lumbar disc herniation,and summarize the respective advantages and application strategies of algorithmic models for the same purpose.METHODS:The computer searched PubMed,Web of Science,EMBASE,CNKI,WanFang,VIP and China Biomedical(CBM)databases to extract the relevant articles on machine learning in the diagnosis and treatment of lumbar disc herniation.Finally,96 articles were included for analysis.RESULTS AND CONCLUSION:(1)Different algorithm models of machine learning provide intelligent and accurate application strategies for clinical diagnosis and treatment of lumbar disc herniation.(2)Traditional statistical methods and decision trees in supervised learning are simple and efficient in exploring risk factors and establishing diagnostic and prognostic models.Support vector machine is suitable for small data sets with high-dimensional features.As a nonlinear classifier,it can be applied to the recognition,segmentation and classification of normal or degenerative intervertebral discs,and to establish diagnostic and prognostic models.Ensemble learning can make up for the shortcomings of a single model.It has the ability to deal with high-dimensional data and improve the precision and accuracy of clinical prediction models.Artificial neural network improves the learning ability of the model,and can be applied to intervertebral disc recognition,classification and making clinical prediction models.On the basis of the above uses,deep learning can also optimize images and assist surgical operations.It is the most widely used model with the best performance in the diagnosis and treatment of lumbar disc herniation.The clustering algorithm in unsupervised learning is mainly used for disc segmentation and classification of different herniated segments.However,the clinical application of semi-supervised learning is relatively less.(3)At present,machine learning has certain clinical advantages in the identification and segmentation of lumbar intervertebral discs,classification and grading of the degenerative intervertebral discs,automatic clinical diagnosis and classification,construction of the clinical predictive model and auxiliary operation.(4)In recent years,the research strategy of machine learning has changed to the neural network and deep learning,and the deep learning algorithm with stronger learning ability will be the key to realizing intelligent medical treatment in the future.