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Prediction of Causative Genes in Inherited Retinal Disorders from Spectral-Domain Optical Coherence Tomography Utilizing Deep Learning Techniques
Joint Authors
Yoshitake, Kazutoshi
Fujinami-Yokokawa, Yu
Pontikos, Nikolas
Yang, Lizhu
Tsunoda, Kazushige
Miyata, Hiroaki
Fujinami, Kaoru
Japan Eye Genetics Consortium, on behalf of
Iwata, Takeshi
Source
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-7, 7 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-04-09
Country of Publication
Egypt
No. of Pages
7
Main Subjects
Abstract EN
Purpose.
To illustrate a data-driven deep learning approach to predicting the gene responsible for the inherited retinal disorder (IRD) in macular dystrophy caused by ABCA4 and RP1L1 gene aberration in comparison with retinitis pigmentosa caused by EYS gene aberration and normal subjects.
Methods.
Seventy-five subjects with IRD or no ocular diseases have been ascertained from the database of Japan Eye Genetics Consortium; 10 ABCA4 retinopathy, 20 RP1L1 retinopathy, 28 EYS retinopathy, and 17 normal patients/subjects.
Horizontal/vertical cross-sectional scans of optical coherence tomography (SD-OCT) at the central fovea were cropped/adjusted to a resolution of 400 pixels/inch with a size of 750 × 500 pix2 for learning.
Subjects were randomly split following a 3 : 1 ratio into training and test sets.
The commercially available learning tool, Medic mind was applied to this four-class classification program.
The classification accuracy, sensitivity, and specificity were calculated during the learning process.
This process was repeated four times with random assignment to training and test sets to control for selection bias.
For each training/testing process, the classification accuracy was calculated per gene category.
Results.
A total of 178 images from 75 subjects were included in this study.
The mean training accuracy was 98.5%, ranging from 90.6 to 100.0.
The mean overall test accuracy was 90.9% (82.0–97.6).
The mean test accuracy per gene category was 100% for ABCA4, 78.0% for RP1L1, 89.8% for EYS, and 93.4% for Normal.
Test accuracy of RP1L1 and EYS was not high relative to the training accuracy which suggests overfitting.
Conclusion.
This study highlighted a novel application of deep neural networks in the prediction of the causative gene in IRD retinopathies from SD-OCT, with a high prediction accuracy.
It is anticipated that deep neural networks will be integrated into general screening to support clinical/genetic diagnosis, as well as enrich the clinical education.
American Psychological Association (APA)
Fujinami-Yokokawa, Yu& Pontikos, Nikolas& Yang, Lizhu& Tsunoda, Kazushige& Yoshitake, Kazutoshi& Iwata, Takeshi…[et al.]. 2019. Prediction of Causative Genes in Inherited Retinal Disorders from Spectral-Domain Optical Coherence Tomography Utilizing Deep Learning Techniques. Journal of Ophthalmology،Vol. 2019, no. 2019, pp.1-7.
https://search.emarefa.net/detail/BIM-1185128
Modern Language Association (MLA)
Fujinami-Yokokawa, Yu…[et al.]. Prediction of Causative Genes in Inherited Retinal Disorders from Spectral-Domain Optical Coherence Tomography Utilizing Deep Learning Techniques. Journal of Ophthalmology No. 2019 (2019), pp.1-7.
https://search.emarefa.net/detail/BIM-1185128
American Medical Association (AMA)
Fujinami-Yokokawa, Yu& Pontikos, Nikolas& Yang, Lizhu& Tsunoda, Kazushige& Yoshitake, Kazutoshi& Iwata, Takeshi…[et al.]. Prediction of Causative Genes in Inherited Retinal Disorders from Spectral-Domain Optical Coherence Tomography Utilizing Deep Learning Techniques. Journal of Ophthalmology. 2019. Vol. 2019, no. 2019, pp.1-7.
https://search.emarefa.net/detail/BIM-1185128
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1185128