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An Analysis of the Application of a Medical Imaging Artificial Intelligence System for a Spontaneous Fluorescence Imaging Recognition System Using Pre-Clinical Diabetic Retinopathy as an Example |
Xiaoru Xu1 , Jiawei Chen1 , Yuanxun Zhang1 , Yixia Feng1 , Daguan Ke2 |
1 The Third Affiliated Hospital of Wenzhou Medical University, Wenzhou 325100, China
2 Biomedical Engineering, Wenzhou Medical University, Wenzhou 325035, China |
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Abstract Objective: To analyze the application of a medical imaging artificial intelligence system for spontaneous fluorescence imaging recognition using pre-clinical diabetic retinopathy as an example so as to provide a technical exploration for early diagnosis and treatment. Methods: The fundus autofluorescence images of 102 patients (200 eyes) in a control group and 105 patients (200 eyes) in a study group were collected from August 2017 to May 2018. All patients were examined by a slit lamp microscope, preview lens, naked eye or corrected visual acuity and fundus autofluorescence images. The images from the control and study groups were used for analysis. The medical image extraction and recognition system is based on a two-dimensional lattice complexity measurement and was used to analyze the discernible differences between the fundus autofluorescence image of pre-clinical diabetic retinopathy and the normal retinal autofluorescence image. Results: Twenty-five features with comparative significance were extracted. The single and multiple features were tested by 10-fold and 5-fold cross tests for 25 features, and the accuracy rate was 82.47%. Conclusions: Complex analysis of a medical imaging artificial intelligence system can be used to identify the spontaneous fluorescence changes on the fundus of pre-clinical diabetic retinopathy with high accuracy.
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Received: 11 February 2019
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Fund: Wenzhou Commonweal Social Development (Medical and Health Care) Technology Project (Y20170791) |
Corresponding Authors:
Jiawei Chen, Biomedical Engineering, Wenzhou Medical University, Wenzhou 325035, China (Email: chenjiaweicat@163.com)
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[1] |
Frampton GK, Kalita N, Payne L, et al. Fundus autofluorescence imaging: systematic review of test accuracy for the diagnosis and monitoring of retinal conditions. Eye (Lond), 2017, 31(7): 995-1007. DOI: 10.1038/eye.2017.19.
|
[2] |
Katz G, Levkovitch-Verbin H, Treister G, et al. Mesopic foveal contrast sensitivity is impaired in diabetic patients without retinopathy. Graefes Arch Clin Exp Ophthalmol, 2010, 248(12): 1699-1703. DOI: 10.1007/s00417-010-1413-y.
|
[3] |
von Rtickmann A, Fitzke FW, Bird AC. Distribution of funduaautofluorescence with a scanning Iaser ophthalmoscope. Br JOphthalmol, 1995, 79: 407-412.
|
[4] |
Hajeb Mohammad Alipour S, Rabbani H, Akhlaghi MR. Diabetic retinopathy grading by digital curvelet transform. Comput Math Methods Med, 2012, 2012: 761901. DOI: 10.
|
11 |
55/2012/761901.
|
[5] |
Ozmert E, Batio?lu F. Fundus autofluorescence before and after photodynamic therapy for chronic central serous chorioretinopathy. Ophthalmologica, 2009, 223(4): 263-268.
|
|
DOI: 10.1159/000210386.
|
[6] |
Saleh MG, Campbell JP, Yang P, et al. Ultra-Wide-Field Fundus Autofluorescence and Spectral-Domain Optical Coherence Tomography Findings in Syphilitic Outer Retinitis. Ophthalmic Surg Lasers Imaging Retina, 2017, 48(3): 208-215. DOI: 10. 3928/23258160-20170301-03.
|
[7] |
周芸芸, 陈长征, 文峰. 眼底自发荧光在视网膜疾病中应用新进展. 中国实用眼科杂志, 2007, 25(12): 1272-1274. DOI: 10. 3760/cma.j.issn.1006-4443.2007.12.002.
|
[8] |
Dysli C, Berger L, Wolf S, et al. Fundus autofluorescence lifetimes and central serous chorioretinopathy. Retina, 2017, 37(11): 2151-2161. DOI: 10.1097/IAE.0000000000001452.
|
[9] |
Lodwick GS. Computer-aided diagnosis in radiology. A research plan. Invest Radiol, 1966, 1(1): 72-80.
|
[10] |
彭锡嘉, 苏兰萍. 正常人后极部自发荧光分布定量研究.中华眼底病杂志, 2011, 27(2): 114-118.
|
[11] |
Bellmann C, Rubin GS, Kabanarou SA, et al. Fundus autofluorescence imaging compared with different confocal scanning laser ophthalmoscopes. Br J Ophthalmol, 2003, 87(11): 1381-1386.
|
[12] |
基于二维格子复杂性质量的医学图像特征提取和识别系统. 中国, 专利号: CN20130753643.9.
|
[13] |
董卫军, 周明全, 耿国华, 等. 基于内容的图像检索技术研究. 计算机工程, 2005(10): 162-163.
|
[14] |
柯大观, 张宏, 童勤业. 格子复杂性和符号序列的细粒化 . 物理学报, 2005(2): 534-542.
|
[15] |
昊芝芝. 复杂性度量在胃镜图像上对胃肿瘤良恶性鉴别的应 用. 温州: 温州医科大学, 2017.
|
[16] |
武瑞霞, 张子瑞, 陈宇彬, 等. 利用二维格子复杂性挖掘肝癌CT图像预后信息. 温州医科大学学报, 2018, 48(6): 396-400. DOI: 10.3969/j.issn.2095-9400.2018.06.002.
|
[17] |
张子瑞. 复杂性度量在医学图像分析中的应用. 温州: 温州医科大学, 2016.
|
[18] |
刘军, 邹倩, 柯大观, 等. 基于脑电格子复杂性分析的麻醉深度监测研究. 传感技术学报, 2015, 28(12): 1747-1753.
|
|
|
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