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Graefe's Archive for Clinical and Experimental Ophthalmology

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16.10.2020 | Miscellaneous

Sex judgment using color fundus parameters in elementary school students

verfasst von: Saki Noma, Takehiro Yamashita, Ryo Asaoka, Hiroto Terasaki, Naoya Yoshihara, Naoko Kakiuchi, Taiji Sakamoto

Erschienen in: Graefe's Archive for Clinical and Experimental Ophthalmology | Ausgabe 12/2020

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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.

Hinton GE, Osindero S, Teh YW (2006) A fast learning algorithm for deep belief nets. Neural Comput 18:1527–1554. https://doi.org/10.1162/neco.2006.18.7.1527CrossRefPubMed

Ting DSW, Cheung CY, Lim G, Tan GSW, Quang ND, Gan A, Hamzah H, Garcia-Franco R, San Yeo IY, Lee SY, Wong EYM, Sabanayagam C, Baskaran M, Ibrahim F, Tan NC, Finkelstein EA, Lamoureux EL, Wong IY, Bressler NM, Sivaprasad S, Varma R, Jonas JB, He MG, Cheng CY, Cheung GCM, Aung T, Hsu W, Lee ML, Wong TY (2017) Development and validation of a deep learning system for diabetic retinopathy and related eye diseases using retinal images from multiethnic populations with diabetes. JAMA 318:2211–2223. https://doi.org/10.1001/jama.2017.18152CrossRefPubMedPubMedCentral

Gulshan V, Peng L, Coram M, Stumpe MC, Wu D, Narayanaswamy A, Venugopalan S, Widner K, Madams T, Cuadros J, Kim R, Raman R, Nelson PC, Mega JL, Webster DR (2016) Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA 316:2402–2410. https://doi.org/10.1001/jama.2016.17216CrossRefPubMed

Wong TY, Bressler NM (2016) Artificial intelligence with deep learning technology looks into diabetic retinopathy screening. JAMA 316:2366–2367. https://doi.org/10.1001/jama.2016.17563CrossRefPubMed

Keel S, Lee PY, Scheetz J, Li Z, Kotowicz MA, MacIsaac RJ, He M (2018) Feasibility and patient acceptability of a novel artificial intelligence-based screening model for diabetic retinopathy at endocrinology outpatient services: a pilot study. Sci Rep 8:4330. https://doi.org/10.1038/s41598-018-22612-2CrossRefPubMedPubMedCentral

Abramoff MD, Lou Y, Erginay A, Clarida W, Amelon R, Folk JC, Niemeijer M (2016) Improved automated detection of diabetic retinopathy on a publicly available dataset through integration of deep learning. Invest Ophthalmol Vis Sci 57:5200–5206. https://doi.org/10.1167/iovs.16-19964CrossRefPubMed

Gargeya R, Leng T (2017) Automated identification of diabetic retinopathy using deep learning. Ophthalmology 124:962–969. https://doi.org/10.1016/j.ophtha.2017.02.008CrossRefPubMed

Takahashi H, Tampo H, Arai Y, Inoue Y, Kawashima H (2017) Applying artificial intelligence to disease staging: deep learning for improved staging of diabetic retinopathy. PLoS One 12:e0179790. https://doi.org/10.1371/journal.pone.0179790CrossRefPubMedPubMedCentral

Shibata N, Tanito M, Mitsuhashi K, Fujino Y, Matsuura M, Murata H, Asaoka R (2018) Development of a deep residual learning algorithm to screen for glaucoma from fundus photography. Sci Rep 8:14665. https://doi.org/10.1038/s41598-018-33013-wCrossRefPubMedPubMedCentral

10.

Asaoka R, Tanito M, Shibata N, Mitsuhashi K, Nakahara K, Fujino Y, Matsuura M, Murata H, Tokumo K, Kiuchi Y (2019) Validation of a deep learning model to screen for glaucoma using images from different fundus cameras and data augmentation. Ophthalmol Glaucom 2:224–231CrossRef

11.

Christopher M, Belghith A, Bowd C, Proudfoot JA, Goldbaum MH, Weinreb RN, Girkin CA, Liebmann JM, Zangwill LM (2018) Performance of deep learning architectures and transfer learning for detecting glaucomatous optic neuropathy in fundus photographs. Sci Rep 8:16685. https://doi.org/10.1038/s41598-018-35044-9CrossRefPubMedPubMedCentral

12.

Diaz-Pinto A, Morales S, Naranjo V, Kohler T, Mossi JM, Navea A (2019) CNNs for automatic glaucoma assessment using fundus images: an extensive validation. Biomed Eng Online 18:29. https://doi.org/10.1186/s12938-019-0649-yCrossRefPubMedPubMedCentral

13.

Gomez-Valverde JJ, Anton A, Fatti G, Liefers B, Herranz A, Santos A, Sanchez CI, Ledesma-Carbayo MJ (2019) Automatic glaucoma classification using color fundus images based on convolutional neural networks and transfer learning. Biomed Opt Express 10:892–913. https://doi.org/10.1364/BOE.10.000892CrossRefPubMedPubMedCentral

14.

Li Z, He Y, Keel S, Meng W, Chang RT, He M (2018) Efficacy of a deep learning system for detecting glaucomatous optic neuropathy based on color fundus photographs. Ophthalmology 125:1199–1206. https://doi.org/10.1016/j.ophtha.2018.01.023CrossRefPubMed

15.

Liu S, Graham SL, Schulz A, Kalloniatis M, Zangerl B, Cai W (2018) A deep learning-based algorithm identifies glaucomatous discs using monoscopic fundus photographs. Ophthalmol Glaucoma 2018:1

16.

Son J, Shin JY, Kim HD, Jung KH, Park KH, Park SJ (2020) Development and validation of deep learning models for screening multiple abnormal findings in retinal fundus images. Ophthalmology 127:85–94. https://doi.org/10.1016/j.ophtha.2019.05.029CrossRefPubMed

17.

Poplin R, Varadarajan AV, Blumer K, Liu Y, McConnell MV, Corrado GS, Peng L, Webster DR (2018) Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning. Nat Biomed Eng 2:158–164. https://doi.org/10.1038/s41551-018-0195-0CrossRefPubMed

18.

Yamashita T, Asaoka R, Tanaka M, Kii Y, Yamashita T, Nakao K, Sakamoto T (2013) Relationship between position of peak retinal nerve fiber layer thickness and retinal arteries on sectoral retinal nerve fiber layer thickness. Invest Ophthalmol Vis Sci 54:5481–5488. https://doi.org/10.1167/iovs.12-11008CrossRefPubMed

19.

Yamashita T, Asaoka R, Kii Y, Terasaki H, Murata H, Sakamoto T (2017) Structural parameters associated with location of peaks of peripapillary retinal nerve fiber layer thickness in young healthy eyes. PLoS One 12:e0177247. https://doi.org/10.1371/journal.pone.0177247CrossRefPubMedPubMedCentral

20.

Yamashita T, Sakamoto T, Terasaki H, Tanaka M, Kii Y, Nakao K (2014) Quantification of retinal nerve fiber and retinal artery trajectories using second-order polynomial equation and its association with axial length. Invest Ophthalmol Vis Sci 55:5176–5182. https://doi.org/10.1167/iovs.14-14105CrossRefPubMed

21.

Yamashita T, Terasaki H, Yoshihara N, Kii Y, Uchino E, Sakamoto T (2018) Relationship between retinal artery trajectory and axial length in Japanese school students. Jpn J Ophthalmol 62:315–320. https://doi.org/10.1007/s10384-018-0572-yCrossRefPubMed

22.

Yamashita T, Nitta K, Sonoda S, Sugiyama K, Sakamoto T (2015) Relationship between location of retinal nerve fiber layer defect and curvature of retinal artery trajectory in eyes with normal tension glaucoma. Invest Ophthalmol Vis Sci 56:6190–6195. https://doi.org/10.1167/iovs.15-17119CrossRefPubMed

23.

Fujino Y, Yamashita T, Murata H, Asaoka R (2016) Adjusting circumpapillary retinal nerve fiber layer profile using retinal artery position improves the structure-function relationship in glaucoma. Invest Ophthalmol Vis Sci 57:3152–3158. https://doi.org/10.1167/iovs.16-19461CrossRefPubMed

24.

Garway-Heath DF, Poinoosawmy D, Fitzke FW, Hitchings RA (2000) Mapping the visual field to the optic disc in normal tension glaucoma eyes. Ophthalmology 107:1809–1815. https://doi.org/10.1016/s0161-6420(00)00284-0CrossRefPubMed

25.

Tay E, Seah SK, Chan SP, Lim AT, Chew SJ, Foster PJ, Aung T (2005) Optic disk ovality as an index of tilt and its relationship to myopia and perimetry. Am J Ophthalmol 139:247–252. https://doi.org/10.1016/j.ajo.2004.08.076CrossRefPubMed

26.

Yoshihara N, Yamashita T, Ohno-Matsui K, Sakamoto T (2014) Objective analyses of tessellated fundi and significant correlation between degree of tessellation and choroidal thickness in healthy eyes. PLoS One 9:e103586. https://doi.org/10.1371/journal.pone.0103586CrossRefPubMedPubMedCentral

27.

Yamashita T, Terasaki H, Tanaka M, Nakao K, Sakamoto T (2020) Relationship between peripapillary choroidal thickness and degree of tessellation in young healthy eyes. Graefes Arch Clin Exp Ophthalmol. https://doi.org/10.1007/s00417-020-04644-5

28.

Suzuki S (1999) Quantitative evaluation of “sunset glow” fundus in Vogt-Koyanagi-Harada disease. Jpn J Ophthalmol 43:327–333. https://doi.org/10.1016/s0021-5155(99)00016-7CrossRefPubMed

29.

Neelam K, Chew RY, Kwan MH, Yip CC, Au Eong KG (2012) Quantitative analysis of myopic chorioretinal degeneration using a novel computer software program. Int Ophthalmol 32:203–209. https://doi.org/10.1007/s10792-012-9542-4CrossRefPubMed

30.

Yan YN, Wang YX, Xu L, Xu J, Wei WB, Jonas JB (2015) Fundus tessellation: prevalence and associated factors: The Beijing Eye Study 2011. Ophthalmology 122:1873–1880. https://doi.org/10.1016/j.ophtha.2015.05.031CrossRefPubMed

31.

Terasaki H, Yamashita T, Yoshihara N, Kii Y, Tanaka M, Nakao K, Sakamoto T (2016) Location of tessellations in ocular fundus and their associations with optic disc tilt, optic disc area, and axial length in young healthy eyes. PLoS One 11:e0156842. https://doi.org/10.1371/journal.pone.0156842CrossRefPubMedPubMedCentral

32.

Yamashita T, Iwase A, Kii Y, Sakai H, Terasaki H, Sakamoto T, Araie M (2018) Location of ocular tessellations in Japanese: population-based Kumejima Study. Invest Ophthalmol Vis Sci 59:4963–4967. https://doi.org/10.1167/iovs.18-25007CrossRefPubMed

33.

Tibshirani R (1996) Regression shrinkage and selection via the lasso. J R Stat Soc Ser B 58:267–288

34.

Friedman J, Hastie T, Tibshirani R (2010) Regularization paths for generalized linear models via coordinate descent. J Stat Softw 33:1–22CrossRef

35.

Yamashita T, Asaoka R, Terasaki H, Murata H, Tanaka M, Nakao K, Sakamoto T (2020) Factors in color fundus photographs that can be used by humans to determine sex of individuals. Trans Vis Sci Tech 9(2):4CrossRef

36.

Hines M, Constantinescu M, Spencer D (2015) Early androgen exposure and human gender development. Biol Sex Differ 6:3. https://doi.org/10.1186/s13293-015-0022-1CrossRefPubMedPubMedCentral

37.

Sawyer SM, Azzopardi PS, Wickremarathne D, Patton GC (2018) The age of adolescence...and young adulthood – authors’ reply. Lancet Child Adolesc Health 2:e7. https://doi.org/10.1016/S2352-4642(18)30075-0CrossRefPubMed

38.

Barbosa MS, Bubna-Litic A, Maddess T (2013) Locally countable properties and the perceptual salience of textures. J Opt Soc Am A Opt Image Sci Vis 30:1687–1697. https://doi.org/10.1364/JOSAA.30.001687CrossRefPubMed

39.

Akutekwe A, Seker H (2014) A hybrid dynamic Bayesian network approach for modelling temporal associations of gene expressions for hypertension diagnosis. Conf Proc IEEE Eng Med Biol Soc 2014:804–807. https://doi.org/10.1109/EMBC.2014.6943713CrossRef

40.

Asaoka R (2013) Measuring visual field progression in the central 10 degrees using additional information from central 24 degrees visual fields and ‘lasso regression’. PLoS One 8:e72199. https://doi.org/10.1371/journal.pone.0072199CrossRefPubMedPubMedCentral

41.

Fujino Y, Murata H, Mayama C, Asaoka R (2015) Applying “Lasso” regression to predict future visual field progression in glaucoma patients. Invest Ophthalmol Vis Sci 56:2334–2339. https://doi.org/10.1167/iovs.15-16445CrossRefPubMed

42.

Japkowicz N (2011) Evaluating learning algorithms: a classification perspective. Cambridge University Press, CambridgeCrossRef

43.

Airaksinen PJ, Nieminen H, Mustonen E (1982) Retinal nerve fibre layer photography with a wide angle fundus camera. Acta Ophthalmol 60:362–368. https://doi.org/10.1111/j.1755-3768.1982.tb03026.xCrossRef

44.

Terasaki H, Sonoda S, Kakiuchi N, Shiihara H, Yamashita T, Sakamoto T (2018) Ability of MultiColor scanning laser ophthalmoscope to detect non-glaucomatous retinal nerve fiber layer defects in eyes with retinal diseases. BMC Ophthalmol 18:324. https://doi.org/10.1186/s12886-018-0995-8CrossRefPubMedPubMedCentral

45.

Wang CY, Zheng YF, Liu B, Meng ZW, Hong F, Wang XX, Wang XJ, Du L, Wang IY, Zhu D, Tao Y, You QS, Jonas JB (2018) Retinal nerve fiber layer thickness in children: the Gobi Desert Children Eye Study. Invest Ophthalmol Vis Sci 59:5285–5291. https://doi.org/10.1167/iovs.18-25418CrossRefPubMed

46.

Atchison DA, Jones CE, Schmid KL, Pritchard N, Pope JM, Strugnell WE, Riley RA (2004) Eye shape in emmetropia and myopia. Invest Ophthalmol Vis Sci 45:3380–3386. https://doi.org/10.1167/iovs.04-0292CrossRefPubMed

47.

Pope JM, Verkicharla PK, Sepehrband F, Suheimat M, Schmid KL, Atchison DA (2017) Three-dimensional MRI study of the relationship between eye dimensions, retinal shape and myopia. Biomed Opt Express 8:2386–2395. https://doi.org/10.1364/BOE.8.002386CrossRefPubMedPubMedCentral

48.

Zhang JM, Wu JF, Chen JH, Wang L, Lu TL, Sun W, Hu YY, Jiang WJ, Guo DD, Wang XR, Bi HS, Jonas JB (2015) Macular choroidal thickness in children: The Shandong Children Eye Study. Invest Ophthalmol Vis Sci 56:7646–7652. https://doi.org/10.1167/iovs.15-17137CrossRefPubMed

49.

Dieck S, Ibarra M, Moghul I, Yeung MW, Pantel JT, Thiele S, Pfau M, Fleckenstein M, Pontikos N, Krawitz PM (2020) Factors in color fundus photographs that can be used by humans to determine sex of individuals. Transl Vis Sci Technol 9:8. https://doi.org/10.1167/tvst.9.7.8CrossRefPubMedPubMedCentral

Titel: Sex judgment using color fundus parameters in elementary school students
verfasst von: Saki Noma
Takehiro Yamashita
Ryo Asaoka
Hiroto Terasaki
Naoya Yoshihara
Naoko Kakiuchi
Taiji Sakamoto
Publikationsdatum: 16.10.2020
Verlag: Springer Berlin Heidelberg
Erschienen in: Graefe's Archive for Clinical and Experimental Ophthalmology / Ausgabe 12/2020
Print ISSN: 0721-832X
Elektronische ISSN: 1435-702X
DOI: https://doi.org/10.1007/s00417-020-04969-1

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Springer Medizin

Sex judgment using color fundus parameters in elementary school students

Abstract

Purposes

Methods

Results

Conclusions

Neu im Fachgebiet Augenheilkunde

Ophthalmika in der Schwangerschaft

Operative Therapie und Keimnachweis bei endogener Endophthalmitis

Bakterielle endogene Endophthalmitis

So erreichen Sie eine bestmögliche Wundheilung der Kornea

Update Augenheilkunde

Springer Medizin

Abstract

Purposes

Methods

Results

Conclusions

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