{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,30]],"date-time":"2026-10-30T06:04:31Z","timestamp":1793340271588,"version":"4.5.0"},"reference-count":71,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,1,22]],"date-time":"2024-01-22T00:00:00Z","timestamp":1705881600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,1,22]],"date-time":"2024-01-22T00:00:00Z","timestamp":1705881600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"National Research Foundation of Korea (NRF) funded by the Korean government","award":["2021R1A2C1007824"],"award-info":[{"award-number":["2021R1A2C1007824"]}]},{"name":"This work was also supported by the Future Medicine 20*30 Project of the Samsung Medical Center","award":["SMX1210791"],"award-info":[{"award-number":["SMX1210791"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>We propose a hybrid technique that employs artificial intelligence (AI)-based segmentation and machine learning classification using multiple features extracted from the foveal avascular zone (FAZ)\u2014a retinal biomarker for Alzheimer\u2019s disease\u2014to improve the disease diagnostic performance. Imaging data of optical coherence tomography angiography from 37 patients with Alzheimer\u2019s disease and 48 healthy controls were investigated. The presence or absence of brain amyloids was confirmed using amyloid positron emission tomography. In the superficial capillary plexus of the angiography scans, the FAZ was automatically segmented using an AI method to extract multiple biomarkers (area, solidity, compactness, roundness, and eccentricity), which were paired with clinical data (age and sex) as common correction variables. We used a light-gradient boosting machine (a light-gradient boosting machine is a machine learning algorithm based on trees utilizing gradient boosting) to diagnose Alzheimer\u2019s disease by integrating the corresponding multiple radiomic biomarkers. Fivefold cross-validation was applied for analysis, and the diagnostic performance for Alzheimer\u2019s disease was determined by the area under the curve. The proposed hybrid technique achieved an area under the curve of <jats:inline-formula><jats:alternatives><jats:tex-math>$$72.2\\pm 4.2$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                  <mml:mrow>\n                    <mml:mn>72.2<\/mml:mn>\n                    <mml:mo>\u00b1<\/mml:mo>\n                    <mml:mn>4.2<\/mml:mn>\n                  <\/mml:mrow>\n                <\/mml:math><\/jats:alternatives><\/jats:inline-formula>%, outperforming the existing single-feature (area) criteria by over 13%. Furthermore, in the holdout test set, the proposed technique exhibited a 14% improvement compared to single features, achieving an area under the curve of 72.0\u00b1 4.8%. Based on these facts, we have demonstrated the effectiveness of our technology in achieving significant performance improvements in FAZ-based Alzheimer\u2019s diagnosis research through the use of multiple radiomic biomarkers (area, solidity, compactness, roundness, and eccentricity).<\/jats:p>","DOI":"10.1038\/s41598-024-51612-8","type":"journal-article","created":{"date-parts":[[2024,1,22]],"date-time":"2024-01-22T08:03:06Z","timestamp":1705910586000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["Enhancing foveal avascular zone analysis for Alzheimer\u2019s diagnosis with AI segmentation and machine learning using multiple radiomic features"],"prefix":"10.1038","volume":"14","author":[{"given":"Je Moon","family":"Yoon","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chae Yeon","family":"Lim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hoon","family":"Noh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Seung Wan","family":"Nam","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sung Yeon","family":"Jun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Min Ji","family":"Kim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mi Yeon","family":"Song","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hyemin","family":"Jang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hee Jin","family":"Kim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sang Won","family":"Seo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Duk L.","family":"Na","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Myung Jin","family":"Chung","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Don-Il","family":"Ham","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kyungsu","family":"Kim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,1,22]]},"reference":[{"key":"51612_CR1","doi-asserted-by":"crossref","first-page":"459","DOI":"10.1016\/j.jalz.2016.03.001","volume":"12","author":"A Association","year":"2016","unstructured":"Association, A. et al. 2016 Alzheimer\u2019s disease facts and figures. Alzheimer\u2019s Dement. 12, 459\u2013509 (2016).","journal-title":"Alzheimer\u2019s Dement."},{"key":"51612_CR2","doi-asserted-by":"crossref","first-page":"1778","DOI":"10.1212\/WNL.0b013e31828726f5","volume":"80","author":"LE Hebert","year":"2013","unstructured":"Hebert, L. E., Weuve, J., Scherr, P. A. & Evans, D. A. Alzheimer disease in the united states (2010\u20132050) estimated using the 2010 census. Neurology 80, 1778\u20131783 (2013).","journal-title":"Neurology"},{"key":"51612_CR3","doi-asserted-by":"crossref","first-page":"293","DOI":"10.1007\/s13167-010-0024-3","volume":"1","author":"I Grossman","year":"2010","unstructured":"Grossman, I. et al. Alzheimer\u2019s disease: Diagnostics, prognostics and the road to prevention. EPMA J. 1, 293\u2013303 (2010).","journal-title":"EPMA J."},{"key":"51612_CR4","doi-asserted-by":"crossref","DOI":"10.1201\/b13935","volume-title":"Alzheimer\u2019s Disease and Related Disorders","author":"S Gauthier","year":"2005","unstructured":"Gauthier, S., Scheltens, P. & Cummings, J. Alzheimer\u2019s Disease and Related Disorders (CRC Press, 2005)."},{"key":"51612_CR5","doi-asserted-by":"crossref","first-page":"640","DOI":"10.1016\/j.bcp.2013.12.024","volume":"88","author":"C Reitz","year":"2014","unstructured":"Reitz, C. & Mayeux, R. Alzheimer disease: Epidemiology, diagnostic criteria, risk factors and biomarkers. Biochem. Pharmacol. 88, 640\u2013651 (2014).","journal-title":"Biochem. Pharmacol."},{"key":"51612_CR6","doi-asserted-by":"crossref","first-page":"934","DOI":"10.1016\/S1474-4422(16)30029-1","volume":"15","author":"Z Arvanitakis","year":"2016","unstructured":"Arvanitakis, Z., Capuano, A. W., Leurgans, S. E., Bennett, D. A. & Schneider, J. A. Relation of cerebral vessel disease to Alzheimer\u2019s disease dementia and cognitive function in elderly people: A cross-sectional study. Lancet Neurol. 15, 934\u2013943 (2016).","journal-title":"Lancet Neurol."},{"key":"51612_CR7","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1111\/j.1365-2990.2010.01139.x","volume":"37","author":"WR Brown","year":"2011","unstructured":"Brown, W. R. & Thore, C. R. Cerebral microvascular pathology in ageing and neurodegeneration. Neuropathol. Appl. Neurobiol. 37, 56\u201374 (2011).","journal-title":"Neuropathol. Appl. Neurobiol."},{"key":"51612_CR8","doi-asserted-by":"crossref","first-page":"2601","DOI":"10.1161\/STROKEAHA.108.536839","volume":"40","author":"EE Smith","year":"2009","unstructured":"Smith, E. E. & Greenberg, S. M. $$\\beta$$-amyloid, blood vessels, and brain function. Stroke 40, 2601\u20132606 (2009).","journal-title":"Stroke"},{"key":"51612_CR9","doi-asserted-by":"crossref","first-page":"S421","DOI":"10.3233\/JAD-141461","volume":"42","author":"DR Thal","year":"2014","unstructured":"Thal, D. R., Attems, J. & Ewers, M. Spreading of amyloid, tau, and microvascular pathology in Alzheimer\u2019s disease: Findings from neuropathological and neuroimaging studies. J. Alzheimer\u2019s Dis. 42, S421\u2013S429 (2014).","journal-title":"J. Alzheimer\u2019s Dis."},{"key":"51612_CR10","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1038\/nrneurol.2012.227","volume":"9","author":"A London","year":"2013","unstructured":"London, A., Benhar, I. & Schwartz, M. The retina as a window to the brain-from eye research to CNS disorders. Nat. Rev. Neurol. 9, 44\u201353 (2013).","journal-title":"Nat. Rev. Neurol."},{"key":"51612_CR11","first-page":"136","volume":"1","author":"KL Thomson","year":"2015","unstructured":"Thomson, K. L., Yeo, J. M., Waddell, B., Cameron, J. R. & Pal, S. A systematic review and meta-analysis of retinal nerve fiber layer change in dementia, using optical coherence tomography. Alzheimer\u2019s Dement. Diagn. Assess. Disease Monit. 1, 136\u2013143 (2015).","journal-title":"Alzheimer\u2019s Dement. Diagn. Assess. Disease Monit."},{"key":"51612_CR12","first-page":"91","volume":"6","author":"S McGrory","year":"2017","unstructured":"McGrory, S. et al. The application of retinal fundus camera imaging in dementia: A systematic review. Alzheimer\u2019s Dement. Diagn. Assess. Disease Monit. 6, 91\u2013107 (2017).","journal-title":"Alzheimer\u2019s Dement. Diagn. Assess. Disease Monit."},{"key":"51612_CR13","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.preteyeres.2017.11.003","volume":"64","author":"RF Spaide","year":"2018","unstructured":"Spaide, R. F., Fujimoto, J. G., Waheed, N. K., Sadda, S. R. & Staurenghi, G. Optical coherence tomography angiography. Prog. Retin. Eye Res. 64, 1\u201355 (2018).","journal-title":"Prog. Retin. Eye Res."},{"key":"51612_CR14","volume":"13","author":"OM Rifai","year":"2021","unstructured":"Rifai, O. M. et al. The application of optical coherence tomography angiography in Alzheimer\u2019s disease: A systematic review. Alzheimer\u2019s Dement. Diagn. Assess. Disease Monit. 13, e12149 (2021).","journal-title":"Alzheimer\u2019s Dement. Diagn. Assess. Disease Monit."},{"key":"51612_CR15","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1016\/j.visres.2016.09.019","volume":"139","author":"BD Krawitz","year":"2017","unstructured":"Krawitz, B. D. et al. Acircularity index and axis ratio of the foveal avascular zone in diabetic eyes and healthy controls measured by optical coherence tomography angiography. Vis. Res. 139, 177\u2013186 (2017).","journal-title":"Vis. Res."},{"key":"51612_CR16","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-016-0028-x","volume":"7","author":"FY Tang","year":"2017","unstructured":"Tang, F. Y. et al. Determinants of quantitative optical coherence tomography angiography metrics in patients with diabetes. Sci. Rep. 7, 1\u201310 (2017).","journal-title":"Sci. Rep."},{"key":"51612_CR17","doi-asserted-by":"crossref","first-page":"1637","DOI":"10.1167\/iovs.16-21079","volume":"58","author":"J Kwon","year":"2017","unstructured":"Kwon, J., Choi, J., Shin, J. W., Lee, J. & Kook, M. S. Alterations of the foveal avascular zone measured by optical coherence tomography angiography in glaucoma patients with central visual field defects. Investig. Ophthalmol. Vis. Sci. 58, 1637\u20131645 (2017).","journal-title":"Investig. Ophthalmol. Vis. Sci."},{"key":"51612_CR18","first-page":"e2201","volume":"96","author":"SH Kang","year":"2021","unstructured":"Kang, S. H. et al. Amyloid positivity in the Alzheimer\/subcortical-vascular spectrum. Neurology 96, e2201\u2013e2211 (2021).","journal-title":"Neurology"},{"key":"51612_CR19","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1016\/0022-510X(96)00169-4","volume":"142","author":"F Fazekas","year":"1996","unstructured":"Fazekas, F. et al. The relation of cerebral magnetic resonance signal hyperintensities to Alzheimer\u2019s disease. J. Neurol. Sci. 142, 121\u2013125 (1996).","journal-title":"J. Neurol. Sci."},{"key":"51612_CR20","volume-title":"Seoul Neuropsychological Screening Battery (SNSB)","author":"Y Kang","year":"2012","unstructured":"Kang, Y., Jang, S. & Na, D. Seoul Neuropsychological Screening Battery (SNSB) (Human Brain Research & Consulting Co., 2012)."},{"key":"51612_CR21","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1001\/archneur.56.3.303","volume":"56","author":"RC Petersen","year":"1999","unstructured":"Petersen, R. C. et al. Mild cognitive impairment: clinical characterization and outcome. Arch. Neurol. 56, 303\u2013308 (1999).","journal-title":"Arch. Neurol."},{"key":"51612_CR22","doi-asserted-by":"crossref","first-page":"1228","DOI":"10.1176\/ajp.152.8.1228","volume":"152","author":"SB Guze","year":"1995","unstructured":"Guze, S. B. Diagnostic and statistical manual of mental disorders, (dsm-iv). Am. J. Psychiatry 152, 1228\u20131228 (1995).","journal-title":"Am. J. Psychiatry"},{"key":"51612_CR23","doi-asserted-by":"crossref","first-page":"922","DOI":"10.1111\/j.1532-5415.1992.tb01992.x","volume":"40","author":"TN Tombaugh","year":"1992","unstructured":"Tombaugh, T. N. & McIntyre, N. J. The mini-mental state examination: A comprehensive review. J. Am. Geriatr. Soc. 40, 922\u2013935 (1992).","journal-title":"J. Am. Geriatr. Soc."},{"key":"51612_CR24","doi-asserted-by":"crossref","first-page":"1091","DOI":"10.1001\/archneur.65.8.1091","volume":"65","author":"SE O\u2019Bryant","year":"2008","unstructured":"O\u2019Bryant, S. E. et al. Staging dementia using clinical dementia rating scale sum of boxes scores: A Texas Alzheimer\u2019s research consortium study. Arch. Neurol. 65, 1091\u20131095 (2008).","journal-title":"Arch. Neurol."},{"key":"51612_CR25","doi-asserted-by":"crossref","first-page":"1178","DOI":"10.1126\/science.1957169","volume":"254","author":"D Huang","year":"1991","unstructured":"Huang, D. et al. Optical coherence tomography. Science 254, 1178\u20131181 (1991).","journal-title":"Science"},{"key":"51612_CR26","doi-asserted-by":"crossref","first-page":"440","DOI":"10.3928\/23258160-20180601-09","volume":"49","author":"DS Grewal","year":"2018","unstructured":"Grewal, D. S., Polascik, B. W., Hoffmeyer, G. C. & Fekrat, S. Assessment of differences in retinal microvasculature using OCT angiography in Alzheimer\u2019s disease: A twin discordance report. Ophthalmic Surg. Lasers Imaging Retin. 49, 440\u2013444 (2018).","journal-title":"Ophthalmic Surg. Lasers Imaging Retin."},{"key":"51612_CR27","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1136\/bjophthalmol-2017-310476","volume":"102","author":"M Bulut","year":"2018","unstructured":"Bulut, M. et al. Evaluation of optical coherence tomography angiographic findings in Alzheimer\u2019s type dementia. Br. J. Ophthalmol. 102, 233\u2013237 (2018).","journal-title":"Br. J. Ophthalmol."},{"key":"51612_CR28","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0210505","volume":"14","author":"C Enders","year":"2019","unstructured":"Enders, C. et al. Quantity and quality of image artifacts in optical coherence tomography angiography. PloS ONE 14, e0210505 (2019).","journal-title":"PloS ONE"},{"key":"51612_CR29","doi-asserted-by":"crossref","first-page":"388","DOI":"10.1136\/bjophthalmol-2020-317659","volume":"106","author":"CE Wisely","year":"2022","unstructured":"Wisely, C. E. et al. Convolutional neural network to identify symptomatic Alzheimer\u2019s disease using multimodal retinal imaging. Br. J. Ophthalmol. 106, 388\u2013395 (2022).","journal-title":"Br. J. Ophthalmol."},{"key":"51612_CR30","unstructured":"O\u2019Shea, K. & Nash, R. An introduction to convolutional neural networks. arXiv:1511.08458 (2015)."},{"key":"51612_CR31","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S. & Sun, J. Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 770\u2013778 (2016).","DOI":"10.1109\/CVPR.2016.90"},{"key":"51612_CR32","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der\u00a0Maaten, L. & Weinberger, K.\u00a0Q. Densely connected convolutional networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 4700\u20134708 (2017).","DOI":"10.1109\/CVPR.2017.243"},{"key":"51612_CR33","unstructured":"Tan, M. & Le, Q. Efficientnet: Rethinking model scaling for convolutional neural networks. In International Conference on Machine Learning, 6105\u20136114 (PMLR, 2019)."},{"key":"51612_CR34","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Ioffe, S., Vanhoucke, V. & Alemi, A.\u00a0A. Inception-v4, inception-resnet and the impact of residual connections on learning. In Thirty-first AAAI Conference on Artificial Intelligence (2017).","DOI":"10.1609\/aaai.v31i1.11231"},{"key":"51612_CR35","doi-asserted-by":"crossref","first-page":"1242","DOI":"10.1001\/jamaophthalmol.2018.3556","volume":"136","author":"BE O\u2019Bryhim","year":"2018","unstructured":"O\u2019Bryhim, B. E., Apte, R. S., Kung, N., Coble, D. & Van Stavern, G. P. Association of preclinical Alzheimer disease with optical coherence tomographic angiography findings. JAMA Ophthalmol. 136, 1242\u20131248 (2018).","journal-title":"JAMA Ophthalmol."},{"key":"51612_CR36","doi-asserted-by":"crossref","first-page":"488","DOI":"10.2967\/jnumed.118.222893","volume":"61","author":"ME Mayerhoefer","year":"2020","unstructured":"Mayerhoefer, M. E. et al. Introduction to radiomics. J. Nucl. Med 61, 488\u2013495 (2020).","journal-title":"J. Nucl. Med"},{"key":"51612_CR37","doi-asserted-by":"crossref","unstructured":"Chen, T. & Guestrin, C. Xgboost: A scalable tree boosting system. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 785\u2013794 (2016).","DOI":"10.1145\/2939672.2939785"},{"key":"51612_CR38","first-page":"18","volume":"2","author":"A Liaw","year":"2002","unstructured":"Liaw, A. et al. Classification and regression by randomforest. R News 2, 18\u201322 (2002).","journal-title":"R News"},{"key":"51612_CR39","unstructured":"Ke, G. et\u00a0al. Lightgbm: A highly efficient gradient boosting decision tree. Advances in Neural Information Processing Systems 30 (2017)."},{"key":"51612_CR40","unstructured":"Wirth, M.\u00a0A. Shape analysis and measurement. University of Guelph. CIS 6320 (2001)."},{"key":"51612_CR41","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1007\/s10479-005-5724-z","volume":"134","author":"P-T De Boer","year":"2005","unstructured":"De Boer, P.-T., Kroese, D. P., Mannor, S. & Rubinstein, R. Y. A tutorial on the cross-entropy method. Ann. Oper. Res. 134, 19\u201367 (2005).","journal-title":"Ann. Oper. Res."},{"key":"51612_CR42","unstructured":"Wang, Y. et\u00a0al. OCTA image dataset with pixel-level mask annotation for FAZ segmentation (2021)."},{"key":"51612_CR43","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1038\/s41592-020-01008-z","volume":"18","author":"F Isensee","year":"2021","unstructured":"Isensee, F., Jaeger, P. F., Kohl, S. A., Petersen, J. & Maier-Hein, K. H. nnU-Net: A self-configuring method for deep learning-based biomedical image segmentation. Nat. Methods 18, 203\u2013211 (2021).","journal-title":"Nat. Methods"},{"key":"51612_CR44","unstructured":"Kingma, D.\u00a0P. & Ba, J. Adam: A method for stochastic optimization. arXiv:1412.6980 (2014)."},{"key":"51612_CR45","volume":"16","author":"J Hui","year":"2021","unstructured":"Hui, J. et al. Detection of retinal changes with optical coherence tomography angiography in mild cognitive impairment and Alzheimer\u2019s disease patients: A meta-analysis. PloS ONE 16, e0255362 (2021).","journal-title":"PloS ONE"},{"key":"51612_CR46","doi-asserted-by":"crossref","first-page":"1419","DOI":"10.1038\/s41433-021-01648-1","volume":"36","author":"A Katsimpris","year":"2022","unstructured":"Katsimpris, A. et al. Optical coherence tomography angiography in Alzheimer\u2019s disease: A systematic review and meta-analysis. Eye 36, 1419\u20131426 (2022).","journal-title":"Eye"},{"key":"51612_CR47","unstructured":"Musik, I. & Bili\u0144ski, S. Reaction of 1-acyl-4 (o-tolyl)-thiosemicarbazide with alpha-halogenketones. i. cyclization with chloroacetone. In Annales Universitatis Mariae Curie-Sklodowska. Sectio D: Medicina, vol.\u00a043, 83\u201390 (1988)."},{"key":"51612_CR48","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1167\/tvst.7.5.20","volume":"7","author":"RE Linderman","year":"2018","unstructured":"Linderman, R. E. et al. Variability of foveal avascular zone metrics derived from optical coherence tomography angiography images. Transl. Vis. Sci. Technol. 7, 20\u201320 (2018).","journal-title":"Transl. Vis. Sci. Technol."},{"key":"51612_CR49","doi-asserted-by":"crossref","first-page":"1233","DOI":"10.3389\/fphys.2018.01233","volume":"9","author":"M Kostic","year":"2018","unstructured":"Kostic, M. et al. Investigating the fractal dimension of the foveal microvasculature in relation to the morphology of the foveal avascular zone and to the macular circulation in patients with type 2 diabetes mellitus. Front. Physiol. 9, 1233 (2018).","journal-title":"Front. Physiol."},{"key":"51612_CR50","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-020-79139-8","volume":"11","author":"R Mirshahi","year":"2021","unstructured":"Mirshahi, R. et al. Foveal avascular zone segmentation in optical coherence tomography angiography images using a deep learning approach. Sci. Rep. 11, 1\u20138 (2021).","journal-title":"Sci. Rep."},{"key":"51612_CR51","doi-asserted-by":"crossref","first-page":"37203","DOI":"10.1007\/s11042-022-13540-5","volume":"81","author":"LK Singh","year":"2022","unstructured":"Singh, L. K., Pooja, Garg, H. & Khanna, M. An IoT based predictive modeling for glaucoma detection in optical coherence tomography images using hybrid genetic algorithm. Multimed. Tools Appl. 81, 37203\u201337242 (2022).","journal-title":"Multimed. Tools Appl."},{"key":"51612_CR52","volume":"39","author":"LK Singh","year":"2022","unstructured":"Singh, L. K., Khanna, M. & Thawkar, S. A novel hybrid robust architecture for automatic screening of glaucoma using fundus photos, built on feature selection and machine learning-nature driven computing. Expert Syst. 39, e13069 (2022).","journal-title":"Expert Syst."},{"key":"51612_CR53","doi-asserted-by":"crossref","DOI":"10.1016\/j.advengsoft.2022.103283","volume":"173","author":"LK Singh","year":"2022","unstructured":"Singh, L. K., Khanna, M., Thawkar, S. & Singh, R. Collaboration of features optimization techniques for the effective diagnosis of glaucoma in retinal fundus images. Adv. Eng. Softw. 173, 103283 (2022).","journal-title":"Adv. Eng. Softw."},{"key":"51612_CR54","first-page":"S72","volume":"9","author":"KA Johnson","year":"2013","unstructured":"Johnson, K. A. et al. Florbetapir (F18-AV-45) PET to assess amyloid burden in Alzheimer\u2019s disease dementia, mild cognitive impairment, and normal aging. Alzheimer\u2019s Dement. 9, S72\u2013S83 (2013).","journal-title":"Alzheimer\u2019s Dement."},{"key":"51612_CR55","doi-asserted-by":"crossref","first-page":"593","DOI":"10.1016\/j.nbd.2015.08.014","volume":"82","author":"LY Di Marco","year":"2015","unstructured":"Di Marco, L. Y. et al. Vascular dysfunction in the pathogenesis of Alzheimer\u2019s disease-a review of endothelium-mediated mechanisms and ensuing vicious circles. Neurobiol. Dis. 82, 593\u2013606 (2015).","journal-title":"Neurobiol. Dis."},{"key":"51612_CR56","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1016\/j.pscychresns.2011.12.007","volume":"203","author":"KW Park","year":"2012","unstructured":"Park, K. W. et al. Regional cerebral blood flow differences in patients with mild cognitive impairment between those who did and did not develop Alzheimer\u2019s disease. Psychiatry Res. Neuroimaging 203, 201\u2013206 (2012).","journal-title":"Psychiatry Res. Neuroimaging"},{"key":"51612_CR57","doi-asserted-by":"crossref","first-page":"1698","DOI":"10.1007\/s00415-007-0631-7","volume":"254","author":"A Caroli","year":"2007","unstructured":"Caroli, A. et al. Cerebral perfusion correlates of conversion to Alzheimer\u2019s disease in amnestic mild cognitive impairment. J. Neurol. 254, 1698\u20131707 (2007).","journal-title":"J. Neurol."},{"key":"51612_CR58","first-page":"S133","volume":"34","author":"F Nobili","year":"2007","unstructured":"Nobili, F. et al. Brain SPECT in subtypes of mild cognitive impairment. Findings from the DESCRIPA multicenter study. Eur. J. Nucl. Med. Mol. Imaging 34, S133\u2013S133 (2007).","journal-title":"Eur. J. Nucl. Med. Mol. Imaging"},{"key":"51612_CR59","doi-asserted-by":"crossref","first-page":"349","DOI":"10.1016\/0006-8993(95)01250-8","volume":"705","author":"RN Kalaria","year":"1995","unstructured":"Kalaria, R. N. & Pax, A. B. Increased collagen content of cerebral microvessels in Alzheimer\u2019s disease. Brain Res. 705, 349\u2013352 (1995).","journal-title":"Brain Res."},{"key":"51612_CR60","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1111\/j.1749-6632.1997.tb48461.x","volume":"826","author":"G De Jong","year":"1997","unstructured":"De Jong, G., De Vos, R., Steur, E. J. & Luiten, P. Cerebrovascular hypoperfusion: A risk factor for Alzheimer\u2019s disease? animal model and postmortem human studies. Ann. N. Y. Acad. Sci. 826, 56\u201374 (1997).","journal-title":"Ann. N. Y. Acad. Sci."},{"key":"51612_CR61","doi-asserted-by":"crossref","first-page":"392","DOI":"10.2174\/156720508785132334","volume":"5","author":"A Christov","year":"2008","unstructured":"Christov, A., Ottman, J., Hamdheydari, L. & Grammas, P. Structural changes in Alzheimer\u2019s disease brain microvessels. Curr. Alzheimer Res. 5, 392\u2013395 (2008).","journal-title":"Curr. Alzheimer Res."},{"key":"51612_CR62","doi-asserted-by":"crossref","first-page":"395","DOI":"10.1007\/s004010000195","volume":"100","author":"E Farkas","year":"2000","unstructured":"Farkas, E., De Jong, G. I., de Vos, R. A., Jansen Steur, E. N. & Luiten, P. G. Pathological features of cerebral cortical capillaries are doubled in Alzheimer\u2019s disease and Parkinson\u2019s disease. Acta Neuropathol. 100, 395\u2013402 (2000).","journal-title":"Acta Neuropathol."},{"key":"51612_CR63","doi-asserted-by":"crossref","first-page":"S204","DOI":"10.1016\/j.neuroimage.2010.06.020","volume":"54","author":"M Koronyo-Hamaoui","year":"2011","unstructured":"Koronyo-Hamaoui, M. et al. Identification of amyloid plaques in retinas from Alzheimer\u2019s patients and noninvasive in vivo optical imaging of retinal plaques in a mouse model. Neuroimage 54, S204\u2013S217 (2011).","journal-title":"Neuroimage"},{"key":"51612_CR64","doi-asserted-by":"crossref","first-page":"e93621","DOI":"10.1172\/jci.insight.93621","volume":"2","author":"Y Koronyo","year":"2017","unstructured":"Koronyo, Y. et al. Retinal amyloid pathology and proof-of-concept imaging trial in Alzheimer\u2019s disease. JCI Insight 2, e93621 (2017).","journal-title":"JCI Insight"},{"key":"51612_CR65","doi-asserted-by":"crossref","first-page":"523","DOI":"10.1167\/iovs.13-12888","volume":"55","author":"Y Tsai","year":"2014","unstructured":"Tsai, Y. et al. Ocular changes in TgF344-AD rat model of Alzheimer\u2019s disease. Investig. Ophthalmol. Vis. Sci. 55, 523\u2013534 (2014).","journal-title":"Investig. Ophthalmol. Vis. Sci."},{"key":"51612_CR66","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1002\/ana.24548","volume":"79","author":"C La Morgia","year":"2016","unstructured":"La Morgia, C. et al. Melanopsin retinal ganglion cell loss in Alzheimer disease. Ann. Neurol. 79, 90\u2013109 (2016).","journal-title":"Ann. Neurol."},{"key":"51612_CR67","doi-asserted-by":"crossref","unstructured":"Nguyen, Q.\u00a0N., Nguyen, V. T.-Q., Hsu, T., Vajzovic, L. & Ngo, H.\u00a0T. Automatic foveal avascular zone segmentation using hessian-based filter and U-Net deep learning network. In International Conference on the Development of Biomedical Engineering in Vietnam, 835\u2013842 (Springer, 2020).","DOI":"10.1007\/978-3-030-75506-5_68"},{"key":"51612_CR68","first-page":"827","volume":"58","author":"F Chan","year":"2017","unstructured":"Chan, F. et al. Machine learning-assisted automated quantification of foveal avascular zone parameters and perifoveal capillary density of prototype and commercial optical coherence tomography angiography (OCT-A) platforms in healthy and diabetic eyes. Investig. Ophthalmol. Vis. Sci. 58, 827\u2013827 (2017).","journal-title":"Investig. Ophthalmol. Vis. Sci."},{"key":"51612_CR69","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-018-28530-7","volume":"8","author":"H Shiihara","year":"2018","unstructured":"Shiihara, H. et al. Objective evaluation of size and shape of superficial foveal avascular zone in normal subjects by optical coherence tomography angiography. Sci. Rep. 8, 1\u20139 (2018).","journal-title":"Sci. Rep."},{"key":"51612_CR70","doi-asserted-by":"crossref","first-page":"1244","DOI":"10.1167\/iovs.18-25986","volume":"60","author":"S Philip","year":"2019","unstructured":"Philip, S. et al. Macula vessel density and foveal avascular zone parameters in exfoliation glaucoma compared to primary open-angle glaucoma. Investig. Ophthalmol. Vis. Sci. 60, 1244\u20131253 (2019).","journal-title":"Investig. Ophthalmol. Vis. Sci."},{"key":"51612_CR71","volume":"14","author":"JU Werner","year":"2019","unstructured":"Werner, J. U. et al. Comparison of foveal avascular zone between optical coherence tomography angiography and fluorescein angiography in patients with retinal vein occlusion. PloS ONE 14, e0217849 (2019).","journal-title":"PloS ONE"}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-024-51612-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-024-51612-8","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-024-51612-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,3]],"date-time":"2024-07-03T16:17:14Z","timestamp":1720023434000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-024-51612-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,22]]},"references-count":71,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["51612"],"URL":"https:\/\/doi.org\/10.1038\/s41598-024-51612-8","relation":{},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,1,22]]},"assertion":[{"value":"18 February 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 January 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 January 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors have no conflicts of interest or financial disclosures to declare.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"1841"}}