Literature DB >> 33064194

Sex judgment using color fundus parameters in elementary school students.

Saki Noma1, Takehiro Yamashita2, Ryo Asaoka3,4,5,6, Hiroto Terasaki2, Naoya Yoshihara2, Naoko Kakiuchi2, Taiji Sakamoto2.   

Abstract

PURPOSES: Recently, artificial intelligence has been used to determine sex using fundus photographs alone. We had earlier reported that sex can be distinguished using known factors obtained from color fundus photography (CFP) in adult eyes. However, it is not clear when the sex difference in fundus parameters begins. Therefore, we conducted this study to investigate sex determination based on fundus parameters using binominal logistic regression in elementary school students.
METHODS: This prospective observational cross-sectional study was conducted on 119 right eyes of elementary school students (aged 8 or 9 years, 59 boys and 60 girls). Through CFP, the tessellation fundus index was calculated as R/(R + G + B) using the mean value of red-green-blue intensity in the eight locations around the optic disc. Optic disc ovality ratio, papillomacular angle, retinal artery trajectory, and retinal vessel were quantified based on our earlier reports. Regularized binomial logistic regression was applied to these variables to select the decisive factors. Furthermore, its discriminative performance was evaluated using the leave-one-out cross-validation method. Sex difference in the parameters was assessed using the Mann-Whitney U test.
RESULTS: The optimal model yielded by the Ridge binomial logistic regression suggested that the ovality ratio of girls was significantly smaller, whereas their nasal green and blue intensities were significantly higher, than those of boys. Using this approach, the area under the receiver-operating characteristic curve was 63.2%.
CONCLUSIONS: Although sex can be distinguished using CFP even in elementary school students, the discrimination accuracy was relatively low. Some sex difference in the ocular fundus may begin after the age of 10 years.

Entities:  

Keywords:  Children; Fundus photography; Sex differences; Sex identification

Year:  2020        PMID: 33064194     DOI: 10.1007/s00417-020-04969-1

Source DB:  PubMed          Journal:  Graefes Arch Clin Exp Ophthalmol        ISSN: 0721-832X            Impact factor:   3.117


  40 in total

1.  A fast learning algorithm for deep belief nets.

Authors:  Geoffrey E Hinton; Simon Osindero; Yee-Whye Teh
Journal:  Neural Comput       Date:  2006-07       Impact factor: 2.026

2.  Artificial Intelligence With Deep Learning Technology Looks Into Diabetic Retinopathy Screening.

Authors:  Tien Yin Wong; Neil M Bressler
Journal:  JAMA       Date:  2016-12-13       Impact factor: 56.272

3.  Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs.

Authors:  Varun Gulshan; Lily Peng; Marc Coram; Martin C Stumpe; Derek Wu; Arunachalam Narayanaswamy; Subhashini Venugopalan; Kasumi Widner; Tom Madams; Jorge Cuadros; Ramasamy Kim; Rajiv Raman; Philip C Nelson; Jessica L Mega; Dale R Webster
Journal:  JAMA       Date:  2016-12-13       Impact factor: 56.272

4.  Development and Validation of a Deep Learning System for Diabetic Retinopathy and Related Eye Diseases Using Retinal Images From Multiethnic Populations With Diabetes.

Authors:  Daniel Shu Wei Ting; Carol Yim-Lui Cheung; Gilbert Lim; Gavin Siew Wei Tan; Nguyen D Quang; Alfred Gan; Haslina Hamzah; Renata Garcia-Franco; Ian Yew San Yeo; Shu Yen Lee; Edmund Yick Mun Wong; Charumathi Sabanayagam; Mani Baskaran; Farah Ibrahim; Ngiap Chuan Tan; Eric A Finkelstein; Ecosse L Lamoureux; Ian Y Wong; Neil M Bressler; Sobha Sivaprasad; Rohit Varma; Jost B Jonas; Ming Guang He; Ching-Yu Cheng; Gemmy Chui Ming Cheung; Tin Aung; Wynne Hsu; Mong Li Lee; Tien Yin Wong
Journal:  JAMA       Date:  2017-12-12       Impact factor: 56.272

5.  Automated Identification of Diabetic Retinopathy Using Deep Learning.

Authors:  Rishab Gargeya; Theodore Leng
Journal:  Ophthalmology       Date:  2017-03-27       Impact factor: 12.079

6.  Improved Automated Detection of Diabetic Retinopathy on a Publicly Available Dataset Through Integration of Deep Learning.

Authors:  Michael David Abràmoff; Yiyue Lou; Ali Erginay; Warren Clarida; Ryan Amelon; James C Folk; Meindert Niemeijer
Journal:  Invest Ophthalmol Vis Sci       Date:  2016-10-01       Impact factor: 4.799

7.  Applying artificial intelligence to disease staging: Deep learning for improved staging of diabetic retinopathy.

Authors:  Hidenori Takahashi; Hironobu Tampo; Yusuke Arai; Yuji Inoue; Hidetoshi Kawashima
Journal:  PLoS One       Date:  2017-06-22       Impact factor: 3.240

8.  Feasibility and patient acceptability of a novel artificial intelligence-based screening model for diabetic retinopathy at endocrinology outpatient services: a pilot study.

Authors:  Stuart Keel; Pei Ying Lee; Jane Scheetz; Zhixi Li; Mark A Kotowicz; Richard J MacIsaac; Mingguang He
Journal:  Sci Rep       Date:  2018-03-12       Impact factor: 4.379

9.  Performance of Deep Learning Architectures and Transfer Learning for Detecting Glaucomatous Optic Neuropathy in Fundus Photographs.

Authors:  Mark Christopher; Akram Belghith; Christopher Bowd; James A Proudfoot; Michael H Goldbaum; Robert N Weinreb; Christopher A Girkin; Jeffrey M Liebmann; Linda M Zangwill
Journal:  Sci Rep       Date:  2018-11-12       Impact factor: 4.379

10.  Development of a deep residual learning algorithm to screen for glaucoma from fundus photography.

Authors:  Naoto Shibata; Masaki Tanito; Keita Mitsuhashi; Yuri Fujino; Masato Matsuura; Hiroshi Murata; Ryo Asaoka
Journal:  Sci Rep       Date:  2018-10-02       Impact factor: 4.379

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.