Skip to main content

Automatic diagnosis of pathological myopia from heterogeneous biomedical data.

Publication ,  Journal Article
Zhang, Z; Xu, Y; Liu, J; Wong, DWK; Kwoh, CK; Saw, S-M; Wong, TY
Published in: PLoS One
2013

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.

Duke Scholars

Altmetric Attention Stats
Dimensions Citation Stats

Published In

PLoS One

DOI

EISSN

1932-6203

Publication Date

2013

Volume

8

Issue

6

Start / End Page

e65736

Location

United States

Related Subject Headings

  • Support Vector Machine
  • Software
  • ROC Curve
  • Polymorphism, Single Nucleotide
  • Myopia, Degenerative
  • Middle Aged
  • Medical Informatics
  • Male
  • Knowledge Bases
  • Image Interpretation, Computer-Assisted
 

Citation

APA
Chicago
ICMJE
MLA
NLM
Zhang, Z., Xu, Y., Liu, J., Wong, D. W. K., Kwoh, C. K., Saw, S.-M., & Wong, T. Y. (2013). Automatic diagnosis of pathological myopia from heterogeneous biomedical data. PLoS One, 8(6), e65736. https://doi.org/10.1371/journal.pone.0065736
Zhang, Zhuo, Yanwu Xu, Jiang Liu, Damon Wing Kee Wong, Chee Keong Kwoh, Seang-Mei Saw, and Tien Yin Wong. “Automatic diagnosis of pathological myopia from heterogeneous biomedical data.PLoS One 8, no. 6 (2013): e65736. https://doi.org/10.1371/journal.pone.0065736.
Zhang Z, Xu Y, Liu J, Wong DWK, Kwoh CK, Saw S-M, et al. Automatic diagnosis of pathological myopia from heterogeneous biomedical data. PLoS One. 2013;8(6):e65736.
Zhang, Zhuo, et al. “Automatic diagnosis of pathological myopia from heterogeneous biomedical data.PLoS One, vol. 8, no. 6, 2013, p. e65736. Pubmed, doi:10.1371/journal.pone.0065736.
Zhang Z, Xu Y, Liu J, Wong DWK, Kwoh CK, Saw S-M, Wong TY. Automatic diagnosis of pathological myopia from heterogeneous biomedical data. PLoS One. 2013;8(6):e65736.

Published In

PLoS One

DOI

EISSN

1932-6203

Publication Date

2013

Volume

8

Issue

6

Start / End Page

e65736

Location

United States

Related Subject Headings

  • Support Vector Machine
  • Software
  • ROC Curve
  • Polymorphism, Single Nucleotide
  • Myopia, Degenerative
  • Middle Aged
  • Medical Informatics
  • Male
  • Knowledge Bases
  • Image Interpretation, Computer-Assisted