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Accurate prediction of myopic progression and high myopia by machine learning

Precision Clinical Medicine 2024年第1期7卷 14-20页

作者： Jiahui Li Simiao Zeng Zhihuan Li Jie Xu Zhuo Sun Jing Zhao Meiyan Li Zixing Zou Taihua Guan Jin Zeng Zhuang Liu Wenchao Xiao Ran Wei Hanpei Miao ian ziyar Junxiong Huang Yuanxu Gao Yangfa Zeng Xing-Tao Zhou Kang Zhang Guangzhou Women and Children's Medical Center Guangzhou Medical UniversityGuangzhou 510120China Guangzhou National Laboratory Guangzhou 510o0oChina Zhuhai International Eye Center of the First Affiliated Hospital of Faculty of Medicine Macao University of Science and TechnologyZhuhai 519000China Beijing Institute of Ophthalmology Beijing Tongren Eye CenterBeijing Tongren HospitalCapital Medical UniversityBeijing 100069China Key Laboratory of Myopia Ministry of HealthShanghai Key Laboratory of Visual Impairment and RestorationDepartment of OphthalmologyEye InstituteEye and EarNoseand Throat HospitalFudan UniversityShanghai 200031China DongguanPeople's Hospital The First School of Clinical MedicinelSouthern Medical UniversityDongguan 523059China State Key Laboratory of Ophthalmology Zhongshan Ophthalmic CenterSun Yat-sen UniversityGuangzhou 510060China Wenzhou Eye Hospital and Institute for Advanced Study on Eye Health and Diseases Wenzhou Medical UniversityWenzhou 32500oChina

Background Myopia is a leading cause of visual impairment in Asia and ***,accurately predicting the progression of myopia and the high risk of myopia remains a *** study aims to develop a predictive model for the development of *** We first retrospectively gathered 612530 medical records from five independent cohorts,encompassing 227543 patients ranging from infants to young ***,we developed a multivariate linear regression algorithm model to predict the progression of myopia and the risk of high *** The model to predict the progression of myopia achieved an R^(2) value of 0.964 vs a mean absolute error(MAE)of 0.119D[95%confidence interval(CI):0.119,1.146]in the internal validation *** demonstrated strong generalizability,maintaining consistent performance across external validation sets:R^(2)=0.950 vs MAE=0.119D(95%CI:0.119,1.136)in validation study 1,R^(2)=0.950 vs MAE=0.121D(95%CI:0.121,1.144)in validation study 2,and R^(2)=0.806 vs MAE=−0.066D(95%CI:−0.066,0.569)in the Shanghai Children Myopia *** the Beijing Children Eye Study,the model achieved an R^(2) of 0.749 vs a MAE of 0.178D(95%CI:0.178,1.557).The model to predict the risk of high myopia achieved an area under the curve(AUC)of 0.99 in the internal validation set and consistently high area under the curve values of 0.99,0.99,0.96 and 0.99 in the respective external validation *** Our study demonstrates accurate prediction of myopia progression and risk of high myopia providing valuable insights for tailoring strategies to personalize and optimize the clinical management of myopia in children.

关键词： myopia progression machine learning prevention precision medicine

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Bilateral simultaneous primary acute angle-closure glaucoma

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Precision Clinical Medicine 2020年第4期3卷 297-300页

作者： Charlotte L.Zhang Wico LLai ian ziyar Laurance Lai Yuen Lau Jie Xu Beijing Institute of Ophthalmology Beijing Tongren HospitalCapital Medical UniversityBeijing 100005China University Hospital and Faculty of Medicine Macao University of Science and TechnologyMacaoChina

Dear Editor,Acute primary angle-closure glaucoma(PACG)is an important cause of blindness in East Asia.1 It is estimated that the overall prevalence of PACG will increase from 1.44%to 2.01%from 2020 to 2050.2 Acute PACG is typically related to increased high intraocular pressure(IOP),with symptoms including red eye,blurred vision,nausea,vomiting,and *** in timely IOPlowering treatment can result in permanent optic nerve damage and vision loss.

关键词： glaucoma acute primary angle closure

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DNA methylation markers in the diagnosis and prognosis of common leukemias

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Signal Transduction and Targeted Therapy 2020年第1期5卷 2468-2477页

作者： Hua Jiang Zhiying Ou Yingyi He Meixing Yu Shaoqing Wu Gen Li Jie Zhu Ru Zhang Jiayi Wang Lianghong Zheng Xiaohong Zhang Wenge Hao Liya He Xiaoqiong Gu Qingli Quan Edward Zhang Huiyan Luo Wei Wei Zhihuan Li Guangxi Zang Charlotte Zhang Tina Poon Daniel Zhang ian ziyar Run-ze Zhang Oulan Li Linhai Cheng Taylor Shimizu Xinping Cui Jian-kang Zhu Xin Sun Kang Zhang Guangzhou Women and Children’s Medical Center Guangzhou Medical UniversityGuangzhou 510623China Guangzhou Regenerative Medicine and Health Guangdong Laboratory Guangzhou 510005China State Key Laboratory of Oncology Sun Yat-sen University Cancer CenterGuangzhou 510060China Department of Statistics and Institute for Integrative Genome Biology University of California RiversideRiversideCA 92521USA Shanghai Center for Plant Stress Biology Shanghai Institute for Biological SciencesChinese Academy of SciencesShanghai 210602China Faculty of Medicine Macao University of Science and TechnologyTaipaMacaoChina

The ability to identify a specific type of leukemia using minimally invasive biopsies holds great promise to improve the diagnosis,treatment selection,and prognosis prediction of *** genome-wide methylation profiling and machine learning methods,we investigated the utility of CpG methylation status to differentiate blood from patients with acute lymphocytic leukemia(ALL)or acute myelogenous leukemia(AML)from normal *** established a CpG methylation panel that can distinguish ALL and AML blood from normal blood as well as ALL blood from AML blood with high sensitivity and *** then developed a methylation-based survival classifier with 23 CpGs for ALL and 20 CpGs for AML that could successfully divide patients into high-risk and low-risk groups,with significant differences in clinical outcome in each leukemia ***,these findings demonstrate that methylation profiles can be highly sensitive and specific in the accurate diagnosis of ALL and AML,with implications for the prediction of prognosis and treatment selection.

关键词： diagnosis prognosis acute

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一个基于深度学习的眼病和肺炎两大类疾病的AI诊断系统

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科学新闻 2019年第2期 33-33页

作者： Daniel S.Kermany Michael Goldbaum Wenjia Cai Carolina C.S.Valentim Huiying Liang Sally L.Baxter Alex McKeown Ge Yang Xiaokang Wu Fangbing Yan Justin Dong Made K.Prasadha Jacqueline Pei Magdalene Y.L.Ting Jie Zhu Christina Li Sierra Hewett Jason Dong ian ziyar Alexander Shi Runze Zhang Rui Hou William Shi Xin Fu Yaou Duan Viet A.N. Huu Cindy Wen Edward D. Zhang Charlotte L. Zhang Oulan Li Xiaobo Wang Michael A. Singer Xiaodong Sun Jie Xu Ali Tafreshi M. Anthony Lewis Huimin Xia 张康美国加州大学圣地亚哥分校人类基因组医学研究所

文章简介本研究开发了一个基于深度学习框架能诊断致盲性视网膜疾病的人工智能(AI)诊断工具。而且,该AI诊断工具还可用于诊断小儿肺炎。这是一项将AI应用于医疗领域的重磅研究成果,有望高精确诊断多种可治疗性疾病。在眼科治疗中,视网膜... 详细信息

文章简介本研究开发了一个基于深度学习框架能诊断致盲性视网膜疾病的人工智能(AI)诊断工具。而且,该AI诊断工具还可用于诊断小儿肺炎。这是一项将AI应用于医疗领域的重磅研究成果,有望高精确诊断多种可治疗性疾病。在眼科治疗中,视网膜OCT(光学相干断层扫描)

关键词： AI 深度学习

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