A Iwase

A Rakotomamonjy

AM Solouki

AW Foong

C Cortes

Chee Keong Kwoh

CS Carlson

D Lowe

D Stambolian

Damon Wing Kee Wong

Dana C. Crawford

GR Lanckriet

H Buch

HG Krumpaszky

J Liu

J Wigginton

Jiang Liu

JS Green

K Mikolajczyk

Q Fan

RE Fan

Seang-Mei Saw

SM Saw

Tien Yin Wong

TIH Consortium

TL Young

Yanwu Xu

YF Shih

YL Li

Z Li

Zhuo Zhang

PLoS ONE

PubMed

Zhang Zhuo

Xu Yanwu

Liu Jiang

Wong Damon Wing Kee

Kwoh Chee Keong

Saw Seang-Mei

Wong Tien Yin

Public Library of Science (PLOS)

Automatic Diagnosis of Pathological Myopia from Heterogeneous Biomedical Data

Pathological myopia is one of the leading causes of blindness worldwide. The condition is particularly prevalent in Asia. Unlike myopia, pathological myopia is accompanied by degenerative changes in the retina, which if left untreated can lead to irrecoverable vision loss. The accurate diagnosis of pathological myopia will enable timely intervention and facilitate better disease management to slow down the progression of the disease. Current methods of assessment typically consider only one type of data, such as that from retinal imaging. However, different kinds of data, including that of genetic, demographic and clinical information, may contain different and independent information, which can provide different perspectives on the visually observable, genetic or environmental mechanisms for the disease. The combination of these potentially complementary pieces of information can enhance the understanding of the disease, providing a holistic appreciation of the multiple risks factors as well as improving the detection outcomes. In this study, we propose a computer-aided diagnosis framework for Pathological Myopia diagnosis through Biomedical and Image Informatics(PM-BMII). Through the use of multiple kernel learning (MKL) methods, PM-BMII intelligently fuses heterogeneous biomedical information to improve the accuracy of disease diagnosis. Data from 2,258 subjects of a population-based study, in which demographic and clinical information, retinal fundus imaging data and genotyping data were collected, are used to evaluate the proposed framework. The experimental results show that PM-BMII achieves an AUC of 0.888, outperforming the detection results from the use of demographic and clinical information 0.607 (increase 46.3%,p<0:005), genotyping data 0.774 (increase 14.7%, p<0.005) or imaging data 0.852 (increase 4.2%, p=0.19) alone. The accuracy of the results obtained demonstrates the feasibility of using heterogeneous data for improved disease diagnosis through our proposed PM-BMII framework.Published versio

Zhang, Zhuo

Xu, Yanwu

Liu, Jiang

Wong, Damon Wing Kee

Kwoh, Chee Keong

Saw, Seang-Mei

Wong, Tien Yin

Digital Repository - Nanyang Technological University

Automatic diagnosis of pathological myopia from heterogeneous biomedical data

Crossref

Pathological myopia is one of the leading causes of blindness worldwide. The condition is particularly prevalent in Asia. Unlike myopia, pathological myopia is accompanied by degenerative changes in the retina, which if left untreated can lead to irrecoverable vision loss. The accurate diagnosis of pathological myopia will enable timely intervention and facilitate better disease management to slow down the progression of the disease. Current methods of assessment typically consider only one type of data, such as that from retinal imaging. However, different kinds of data, including that of genetic, demographic and clinical information, may contain different and independent information, which can provide different perspectives on the visually observable, genetic or environmental mechanisms for the disease. The combination of these potentially complementary pieces of information can enhance the understanding of the disease, providing a holistic appreciation of the multiple risks factors as well as improving the detection outcomes. In this study, we propose a computer-aided diagnosis framework for Pathological Myopia diagnosis through Biomedical and Image Informatics(PM-BMII). Through the use of multiple kernel learning (MKL) methods, PM-BMII intelligently fuses heterogeneous biomedical information to improve the accuracy of disease diagnosis. Data from 2,258 subjects of a population-based study, in which demographic and clinical information, retinal fundus imaging data and genotyping data were collected, are used to evaluate the proposed framework. The experimental results show that PM-BMII achieves an AUC of 0.888, outperforming the detection results from the use of demographic and clinical information 0.607 (increase 46.3%, p<0.005), genotyping data 0.774 (increase 14.7%, P<0.005) or imaging data 0.852 (increase 4.2%, p=0.19) alone. The accuracy of the results obtained demonstrates the feasibility of using heterogeneous data for improved disease diagnosis through our proposed PM-BMII framework

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Automatic diagnosis of pathological myopia from heterogeneous biomedical data.

DR-NTU (Digital Repository of NTU)

http://scholarbank.nus.edu.sg/bitstream/10635/108876/1/2013_Automatic_Diagnosis_of_Pathological_Myopia.PDF

Automatic Diagnosis of Pathological Myopia from Heterogeneous Biomedical Data

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Public Library of Science (PLOS)

Digital Repository - Nanyang Technological University

Crossref

Directory of Open Access Journals

DR-NTU (Digital Repository of NTU)