数据与计算发展前沿 ›› 2024, Vol. 6 ›› Issue (2): 117-133.
CSTR: 32002.14.jfdc.CN10-1649/TP.2024.02.011
doi: 10.11871/jfdc.issn.2096-742X.2024.02.011
叶旭1(),杜一1,崔文娟1,沈俊杰2,谢靖2,王露笛1,*(
)
收稿日期:
2023-04-06
出版日期:
2024-04-20
发布日期:
2024-04-26
通讯作者:
*王露笛(E-mail: 作者简介:
叶旭,中国科学院计算机网络信息中心,硕士研究生,主要研究方向为机器学习、数据挖掘等。基金资助:
YE Xu1(),DU Yi1,CUI Wenjuan1,SHEN Junjie2,XIE Jing2,WANG Ludi1,*(
)
Received:
2023-04-06
Online:
2024-04-20
Published:
2024-04-26
摘要:
【应用背景】随着数据的爆炸式增长、算法的不断改进以及计算能力的快速发展,机器学习在教育、金融、制造和医疗等领域均得到了广泛应用。在眼健康领域,机器学习也已经在疾病诊断、疾病分级、医学检查和疾病早期筛查等许多任务上实现了初步应用。【方法】本文通过对眼健康领域国内外相关文献的调研,从眼科疾病类别、就诊阶段、数据类型及技术类型4个不同维度对领域应用进行了梳理与分析,并对接下来的研究做出相应的展望。【结果】基于调研分析的结果可以看出,在眼健康领域中,机器学习技术主要以图像数据为主,围绕疾病诊断与分级展开。同时在疾病早期筛查和疾病风险预测等处于疾病发展早期阶段的任务上也取得了不错的表现。【结论】通过将机器学习技术应用到眼科诊疗过程的各个阶段,有望降低眼科医生负担、提升眼科医生工作效率、帮助控制患者病情发展、提升患者生活质量并降低患者治疗的经济成本和时间成本。
叶旭, 杜一, 崔文娟, 沈俊杰, 谢靖, 王露笛. 机器学习技术在眼健康领域的应用[J]. 数据与计算发展前沿, 2024, 6(2): 117-133.
YE Xu, DU Yi, CUI Wenjuan, SHEN Junjie, XIE Jing, WANG Ludi. Application of Machine Learning Technology in the Field of Eye Health[J]. Frontiers of Data and Computing, 2024, 6(2): 117-133, https://cstr.cn/32002.14.jfdc.CN10-1649/TP.2024.02.011.
表1
从针对的疾病角度对研究的总结"
作者 | 年份 | 疾病 | 模型 | 数据 |
---|---|---|---|---|
Gao等[ | 2015 | 白内障 | CNN | 裂隙灯图像 |
Zhang等[ | 2017 | 白内障 | CNN | 眼底图像 |
Pratap等[ | 2019 | 白内障 | CNN(AlexNet) | 眼底图像 |
Fraccaro等[ | 2015 | 年龄相关性 黄斑变性 | SVM/随机森 林等 | 电子病历 |
Lee等[ | 2017 | 年龄相关性 黄斑变性 | CNN | OCT图像 |
Burlina等[ | 2018 | 年龄相关性 黄斑变性 | CNN | 眼底图像 |
Oh等[ | 2013 | 糖尿病视网 膜病变 | LASSO等 | 电子健康 记录 |
翁铭等[ | 2018 | 糖尿病视网 膜病变 | CNN | 眼底图像 |
马菲妍等[ | 2022 | 糖尿病视网 膜病变 | CNN | 眼底图像 |
Brown等[ | 2018 | 早产儿视网 膜病变 | CNN | 视网膜图像 |
Gupta等[ | 2019 | 早产儿视网 膜病变 | CNN | 视网膜图像 |
Thompson等[ | 2020 | 青光眼 | CNN | SD-OCT 图像 |
Christopher等[ | 2018 | 青光眼 | 多种CNN | 眼底图像 |
Liu等[ | 2019 | 青光眼 | CNN | 眼底图像 |
Apostolova等[ | 2017 | 开放性眼球 损伤 | TF-IDF/ SVM等 | 临床文本 记录 |
Elsawy等[ | 2021 | 角膜疾病 | CNN (VGG-19) | ASOCT 图像 |
表2
从任务所处的阶段对研究的总结"
作者 | 年份 | 任务 | 疾病 | 就诊阶段 | 模型 | 数据 |
---|---|---|---|---|---|---|
Baxter等[ | 2019 | 疾病干预预测 | 青光眼 | 就诊前 | 随机森林等 | 电子健康记录 |
Saleh等[ | 2018 | 疾病风险预测 | 糖尿病视网膜病变 | 就诊前 | 模糊随机森林等 | 电子健康记录 |
Abràmoff等[ | 2018 | 疾病早期筛查 | 糖尿病视网膜病变 | 就诊前 | CNN | 视网膜图像 |
De Fauwd等[ | 2018 | 疾病诊断 | 多种视网膜疾病 | 就诊中 | 三维U-Net | 三维OCT图像 |
Ting等[ | 2019 | 疾病诊断 | 白内障 | 就诊中 | CNN(ResNet) | 裂隙灯图像 |
Burlina 等[ | 2017 | 疾病诊断 | 年龄相关性黄斑变性 | 就诊中 | CNN | 眼底图像 |
Taylor等[ | 2019 | 疾病分级 | 早产儿视网膜病变 | 就诊中 | CNN | 后视网膜图像 |
Kanagasingam等[ | 2018 | 疾病分级 | 糖尿病性视网膜病变 | 就诊中 | CNN | 视网膜图像 |
Li等[ | 2020 | 医学检查 | 视力威胁危险因素 | 就诊中 | CNN (InceptionResnetV2) | OCT图像 |
Loo等[ | 2022 | 医学检查 | 视盘异常 | 就诊中 | CNN | 眼底图像 |
表3
知名眼科图像数据库"
数据库名称 | 疾病种类 | 采集设备 | 是否OA |
---|---|---|---|
Messidor-2 | 糖尿病视网膜病变 | TRC-NW6 non-mydriatic fundus camera (Topcon) | 是 |
DRIVE | 糖尿病视网膜病变 | CR5 non-mydriatic 3CCD camera (Canon) | 是 |
EyePACS | 糖尿病视网膜病变 | Centervue DRS (Centervue, 意大利), Optovue iCam (Optovue, 美国), Canon CR1/DGi/CR2 (Canon),和Topcon NW (Topcon) | 是 |
E-ophtha | 糖尿病视网膜病变 | 未知 | 是 |
ACRIMA | 青光眼 | TRC retina camera (Topcon, 日本) | 是 |
Corneal Endothelial Cell | 角膜病 | SP-3000 specular microscope (Topcon) | 是 |
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