机器学习在脊柱疾病智能诊治中的应用综述

doi:10.11896/jsjkx.201100006

摘要/Abstract

摘要： 脊柱疾病是现代社会中常见疾病之一,目前其诊断与治疗主要依赖于医生的专业水平和临床经验,这样不仅给医生带来沉重负担,而且效率低下。以神经网络为代表的机器学习算法能够自动提取脊柱数据集中的特征信息,辅助医生快速定位病灶区域,实现精准治疗。文中从实验数据、特征选择、算法模型和性能评估指标等方面,对机器学习技术在脊柱疾病应用中的研究现状进行了系统总结。首先从机器学习算法角度出发,阐述典型算法在疾病诊治中的用途;其次围绕实际应用,从危险因素分析和疾病预测、疾病识别和分类、脊柱图像的特征提取和分割3方面,结合具体实验对比机器学习模型的性能;最后总结目前应用中存在的局限性并提出展望。

关键词: 机器学习, 脊柱疾病, 神经网络, 研究综述, 智慧医疗

Abstract: Spine diseases are prevalent in modern society.The diagnosis and treatment mainly depend on doctors' professional knowledge and clinical experience.More and more patients and conventional treatments resulted in heavy overload and inefficient diagnosis.Machine learning algorithms can automatically extract useful information from datasets and images,assisting doctors to locate the lesion and carry out the accurate treatment.This paper focuses on the applications of machine learning in the field of spine disease and summarizes the relevant research from aspects of datasets,feature selection,model,evaluation metrics,and so on.Firstly,in terms of machine learning algorithms,the utility of some typical algorithms in disease diagnosis and treatment is described.Moreover,in terms of the actual applications of disease diagnosis and treatment(risk factor analysis and disease prediction,disease recognition and classification,feature extraction of spine image and image segmentation),the performances of several important models are compared in some specific experiments.Accuracy,specificity,sensitivity,AUC,and other evaluation indexes are involved.Finally,the major limitations and corresponding issues in current applications are summarized.

Key words: Machine learning, Neural network, Review, Smart health-care, Spine disease

中图分类号:

TP399

刘彤彤, 杨环, 西永明, 郭建伟, 潘振宽, 黄宝香. 机器学习在脊柱疾病智能诊治中的应用综述[J]. 计算机科学, 2021, 48(11A): 597-607. https://doi.org/10.11896/jsjkx.201100006

LIU Tong-tong, YANG Huan, XI Yong-ming, GUO Jian-wei, PAN Zhen-kuan, HUANG Bao-xiang. Review on Intelligent Diagnosis of Spine Disease Based on Machine Learning[J]. Computer Science, 2021, 48(11A): 597-607. https://doi.org/10.11896/jsjkx.201100006

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