全部 标题 作者
关键词 摘要

OALib Journal期刊
ISSN: 2333-9721
费用:99美元

查看量下载量

相关文章

更多...

神经网络算法于消渴内障的应用的研究进展
Research Progress on the Application of Neural Network Algorithms in XiaokeNeizhang

DOI: 10.12677/hjo.2024.132004, PP. 20-26

Keywords: 消渴内障,深度学习,神经网络,糖尿病性视网膜病变
XiaokeNeizhang
, Deep Learning, Neural Network, Diabetic Retinopathy

Full-Text   Cite this paper   Add to My Lib

Abstract:

消渴内障大致相当于西医学的糖尿病性视网膜病变,为消渴病的常见并发症之一,对患者的视力损失及生活质量造成了较大的影响。除了消渴病本身的原发病因素,消渴内障自身的复杂性与难治性也为患者造成了庞大的医疗负担。因此,对于消渴内障的早筛查、早干预成为了防治的第一阵地。近年来,随着光学技术的发展与应用技术的成熟,眼底的情况从以前的“不可知”到现在的“可知”,极大地帮助了对于该疾病的早期筛查及诊断分期。而神经网络算法的加入,使得对于该病的诊断技术达到了“超前预测”的水平。但对于祖国医学而言,消渴内障的辨证尚未完全搭上这班“神经网络算法”的快车。因此,本文拟对近年来神经网络算法于消渴内障病应用的研究进展做系统综述,以启迪其在中医辨证论治的应用,丰富中医望诊的内容,以求得“治未病”之功。
Abstract: XiaokeNeizhang is roughly equivalent to diabetic retinopathy in Western medicine. It is one of the common complications of diabetes and has a great impact on patients' vision loss and quality of life. In addition to the primary causative factors of diabetes, the complexity and refractory of XiaokeNeizhang also impose a huge medical cost on patients. Therefore, early screening and early intervention for XiaokeNeizhang have become the first line of prevention and treatment. In recent years, with the development of optical technology and the maturity of application technology, the condition of the fundus has changed from “unknowable” to “knowable” now, which has greatly helped the early screening and diagnosis and staging of the disease. The addition of neural network algorithms has enabled the diagnosis technology of this disease to reach the level of “prediction”. But for Traditional Chinese Medicine, the syndrome differentiation of XiaokeNeizhang has not yet completely caught up with the express train of this “neural network algorithm”. Therefore, this article intends to conduct a systematic review of the research progress of neural network algorithms in the application of XiaokeNeizhang in recent years, in order to enlighten its application in syndrome differentiation and treatment of Traditional Chinese Medicine, enrich the content of Traditional Chinese Medicine examination, and achieve the effect of “preventing disease”.

References

[1]  周丹, 刘利哲, 张桐楠. 基于“开导之后宜补论”治疗消渴内障的经验探析[J]. 中国中医眼科杂志, 2016, 26(1): 61-63.
[2]  单祎. 糖尿病性视网膜病变的视力损伤负担及其危险因素分析[D]: [博士学位论文]. 杭州: 浙江大学, 2021.
[3]  Yau, J.W.Y., Rogers, S.L., Kawasaki, R., Lamoureux, E.L., Kowalski, J.W., Bek, T., Chen, S.-J., Dekker, J.M., Fletcher, A., Grauslund, J., Haffner, S., Hamman, R.F., Ikram, M.K., Kayama, T., Klein, B.E.K., Klein, R., Krishnaiah, S., Mayurasakorn, K., O’Hare, J.P., Orchard, T.J., Porta, M., Rema, M., Roy, M.S., Sharma, T., Shaw, J., Taylor, H., Tielsch, J.M., Varma, R., Wang, J.J., Wang, N., West, S., Xu, L., Yasuda, M., Zhang, X., Mitchell, P. and Wong, T.Y. (2012) Global Prevalence and Major Risk Factors of Diabetic Retinopathy. Diabetes Care, 35, 556-564.
https://doi.org/10.2337/dc11-1909
[4]  中华医学会糖尿病学分会视网膜病变学组. 糖尿病视网膜病变防治专家共识[J]. 中华糖尿病杂志, 2018, 10(4): 241-247.
[5]  Magliano, D.J. and Boyko, E.J. (2021) IDF Diabetes Atlas. 10th Edition, International Diabetes Federation, Brussels.
[6]  Yang, J., Wang, X. and Jiang, S. (2023) Development and Validation of a Nomogram Model for Individualized Prediction of Hypertension Risk in Patients with Type 2 Diabetes Mellitus. Scientific Reports, 13, Article No. 1298.
https://doi.org/10.1038/s41598-023-28059-4
[7]  Klein, R., Klein, B.E.K., Moss, S.E., et al. (1995) The Wisconsin Epidemiologic Study of Diabetic Retinopathy. Archives of Ophthalmology, 113, 702-703.
https://doi.org/10.1001/archopht.1995.01100060024016
[8]  Yang, Y., Cai, Z., Qiu, S. and Xu, P. (2024) Vision Transformer with Masked Autoencoders for Referable Diabetic Retinopathy Classification Based on Large-Size Retina Image. PLOS ONE, 19, e0299265.
https://doi.org/10.1371/journal.pone.0299265
[9]  Atc?, ?.Y., Güne?, A., Zontul, M. and Arslan, Z. (2024) Identifying Diabetic Retinopathy in the Human Eye: A Hybrid Approach Based on a Computer-Aided Diagnosis System Combined with Deep Learning. Tomography, 10, 215-230.
https://doi.org/10.3390/tomography10020017
[10]  Li, A., Cheng, J., Wong, D. and Jiang, L. (2016) Integrating Holistic and Local Deep Features for Glaucoma Classification. 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Orlando, 16-20 August 2016, 1328-1331.
https://doi.org/10.1109/EMBC.2016.7590952
[11]  Demir, F. and Ta?c?, B. (2021) An Effective and Robust Approach Based on R-CNN+LSTM Model and NCAR Feature Selection for Ophthalmological Disease Detection from Fundus Images. Journal of Personalized Medicine, 11, Article 1276.
https://doi.org/10.3390/jpm11121276
[12]  Ezhei, M., Plonka, G. and Rabbani, H. (2022) Retinal Optical Coherence Tomography Image Analysis by a Restricted Boltzmann Machine. Biomedical Optics Express, 13, 4539-4558.
https://doi.org/10.1364/BOE.458753
[13]  Foysal, M., Hossain, A., Yassine, A. and Hossain, M.S. (2023) Detection of COVID-19 Case from Chest CT Images Using Deformable Deep Convolutional Neural Network. Journal of Healthcare Engineering, 2023, Article ID: 4301745.
https://doi.org/10.1155/2023/4301745
[14]  舒军, 杨露, 陈义红, 杨莉, 邓芳. 基于小数据集的改进LeNet图像分类模型研究[J]. 中南民族大学学报(自然科学版), 2019, 38(4): 605-612.
[15]  肖小梅, 杨红云, 易文龙, 万颖, 黄琼, 罗建军. 改进的Alexnet模型在水稻害虫图像识别中的应用[J]. 科学技术与工程, 2021, 21(22): 9447-9454.
[16]  伍思雨, 冯骥. 基于改进VGGNet卷积神经网络的鲜花识别[J]. 重庆师范大学学报(自然科学版), 2020, 37(4): 124-131.
[17]  张烽, 翁英健, 苏家明, 潘航露, 李馨, 郑尚知, 陈伟斌. 基于TV模型与GoogLeNet的甲状腺结节图像分类[J]. 计算机应用研究, 2020, 37(S1): 421-422, 417.
[18]  邱云飞, 张家欣, 兰海, 宗佳旭. 融合张量合成注意力的改进ResNet图像分类模型[J]. 激光与光电子学进展, 2023, 60(6): 87-96.
[19]  李赵旭, 宋涛, 葛梦飞, 刘嘉欣, 王宏伟, 王佳. 基于改进Inception模型的乳腺癌病理学图像分类[J]. 激光与光电子学进展, 2021, 58(8): 388-394.
[20]  Li, J.-P.O., Liu, H., Ting, D.S.J., Jeon, S., Chan, R.V.P., Kim, J.E., Sim, D.A., Thomas, P.B.M., Lin, H., Chen, Y., Sakomoto, T., Loewenstein, A., Lam, D.S.C., Pasquale, L.R., Wong, T.Y., Lam, L.A. and Ting, D.S.W. (2021) Digital Technology, Tele-Medicine and Artificial Intelligence in Ophthalmology: A Global Perspective. Progress in Retinal and Eye Research, 82, Article 100900.
https://doi.org/10.1016/j.preteyeres.2020.100900
[21]  Bogunovi?, H., Montuoro, A., Baratsits, M., Karantonis, M.G., Waldstein, S.M., Schlanitz, F. and Schmidt-Erfurth, U. (2017) Machine Learning of the Progression of Intermediate Age-Related Macular Degeneration Based on OCT Imaging. Investigative Ophthalmology & Visual Science, 58, BIO141-BIO150.
https://doi.org/10.1167/iovs.17-21789
[22]  Grzybowski, A., Rao, D.P., Brona, P., Negiloni, K., Krzywicki, T. and Savoy, F.M. (2023) Diagnostic Accuracy of Automated Diabetic Retinopathy Image Assessment Softwares: IDx-DR and Medios Artificial Intelligence. Ophthalmic Research, 66, 1286-1292.
https://doi.org/10.1159/000534098
[23]  郭潇雅. 嵩岳机器人惊艳亮相[J]. 中国医院院长, 2018(14): 28-29.
[24]  Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K. and Yuille, A.L. (2017) Deeplab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40, 834-848.
https://doi.org/10.1109/TPAMI.2017.2699184
[25]  Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V. and Rabinovich, A. (2015) Going Deeper with Convolutions. 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, 7-12 June 2015, 1-9.
https://doi.org/10.1109/CVPR.2015.7298594
[26]  Caicho, J., Chuya-Sumba, C., Jara, N., Salum, G.M., Tirado-Espín, A., Villalba-Meneses, G., Alvarado-Cando, O., Cadena-Morejón, C. and Almeida-Galárraga, D.A. (2022) Diabetic Retinopathy: Detection and Classification Using AlexNet, GoogleNet and ResNet50 Convolutional Neural Networks. In: Narváez, F.R., Proa?o, J., Morillo, P., Vallejo, D., González Montoya, D. and Díaz, G.M., Eds., Smart Technologies, Systems and Applications, Springer, Cham, 259-271.
https://doi.org/10.1007/978-3-030-99170-8_19
[27]  Sarwinda, D., Paradisa, R.H., Bustamam, A. and Anggia, P. (2021) Deep Learning in Image Classification Using Residual Network (ResNet) Variants for Detection of Colorectal Cancer. Procedia Computer Science, 179, 423-431.
https://doi.org/10.1016/j.procs.2021.01.025
[28]  Duan, J., Shi, T., Zhou, H., Xuan, J. and Wang, S. (2020) A Novel ResNet-Based Model Structure and Its Applications in Machine Health Monitoring. Journal of Vibration and Control, 27, 1036-1050.
https://doi.org/10.1177/1077546320936506
[29]  Zeng, L.Z., Cui, J., Jiang, T., Tu, L.P., Liu, H.D., Gong, Y.B., Xu, L. and Xu, J.T. (2023) Study on the Difference and Regularity of Tongue Images in 309 Patients with Different Pathological Stages of Non-Small Cell Lung Cancer. Technology and Health Care, 32, 1403-1420.
https://doi.org/10.3233/THC-230372
[30]  Sreenivasu, S.V.N., Santosh Kumar Patra, P., Midasala, V., Murthy, G.S.N., Janapati, K.C., Swarup Kumar, J. and Kumar, P.M. (2023) ODQN-Net: Optimized Deep Q Neural Networks for Disease Prediction through Tongue Image Analysis Using Remora Optimization Algorithm. Big Data, 11, 452-465.
https://doi.org/10.1089/big.2023.0014
[31]  Zhu, X., Wang, F., Mao, J., Huang, Y., Zhou, P. and Luo, J. (2023) A Protocol for Digitalized Collection of Traditional Chinese Medicine (TCM) Pulse Information Using Bionic Pulse Diagnosis Equipment. Phenomics, 3, 519-534.
https://doi.org/10.1007/s43657-023-00104-2
[32]  Feng, Y., Hu, C., Cui, K., Fan, M., Xiang, W., Ye, D., Shi, Y., Ye, H., Bai, X., Wei, Y., Xu, Y. and Huang, J. (2023) GSK840 Alleviates Retinal Neuronal Injury by Inhibiting RIPK3/MLKL-Mediated RGC Necroptosis after Ischemia/Reperfusion. Investigative Ophthalmology & Visual Science, 64, Article 42.
https://doi.org/10.1167/iovs.64.14.42

Full-Text

Contact Us

service@oalib.com

QQ:3279437679

WhatsApp +8615387084133