{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,6]],"date-time":"2026-07-06T23:39:33Z","timestamp":1783381173782,"version":"3.54.6"},"reference-count":52,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,7,30]],"date-time":"2024-07-30T00:00:00Z","timestamp":1722297600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2024,7,30]],"date-time":"2024-07-30T00:00:00Z","timestamp":1722297600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"name":"National Cancer Institute","award":["R01CA249992"],"award-info":[{"award-number":["R01CA249992"]}]},{"name":"National Cancer Institute","award":["R01CA202752"],"award-info":[{"award-number":["R01CA202752"]}]},{"name":"National Cancer Institute","award":["R01CA208236"],"award-info":[{"award-number":["R01CA208236"]}]},{"name":"National Cancer Institute","award":["R01CA216579"],"award-info":[{"award-number":["R01CA216579"]}]},{"name":"National Cancer Institute","award":["R01CA220581"],"award-info":[{"award-number":["R01CA220581"]}]},{"name":"National Cancer Institute","award":["R01CA257612"],"award-info":[{"award-number":["R01CA257612"]}]},{"name":"National Cancer Institute","award":["R01CA268207A1"],"award-info":[{"award-number":["R01CA268207A1"]}]},{"name":"National Cancer Institute","award":["U01CA239055"],"award-info":[{"award-number":["U01CA239055"]}]},{"name":"National Cancer Institute","award":["U01CA248226"],"award-info":[{"award-number":["U01CA248226"]}]},{"name":"National Cancer Institute","award":["U54CA254566"],"award-info":[{"award-number":["U54CA254566"]}]},{"DOI":"10.13039\/100000050","name":"National Heart, Lung and Blood Institute","doi-asserted-by":"crossref","award":["R01HL151277"],"award-info":[{"award-number":["R01HL151277"]}],"id":[{"id":"10.13039\/100000050","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/100000050","name":"National Heart, Lung and Blood Institute","doi-asserted-by":"crossref","award":["R01HL158071"],"award-info":[{"award-number":["R01HL158071"]}],"id":[{"id":"10.13039\/100000050","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/100000070","name":"National Institute of Biomedical Imaging and Bioengineering","doi-asserted-by":"publisher","award":["R43EB028736"],"award-info":[{"award-number":["R43EB028736"]}],"id":[{"id":"10.13039\/100000070","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000097","name":"National Center for Research Resources","doi-asserted-by":"publisher","award":["C06 RR12463-01"],"award-info":[{"award-number":["C06 RR12463-01"]}],"id":[{"id":"10.13039\/100000097","id-type":"DOI","asserted-by":"publisher"}]},{"name":"United States Department of Veterans Affairs Biomedical Laboratory Research and Development Service","award":["VA Merit Review Award IBX004121A"],"award-info":[{"award-number":["VA Merit Review Award IBX004121A"]}]},{"name":"Office of the Assistant Secretary of Defense for Health Affairs","award":["Breast Cancer Research Program (W81XWH-19-1-0668), the Prostate Cancer Research Program (W81XWH-15-1-0558, W81XWH-20-1-0851), the Lung Cancer Research Program (W81XWH-18-1-0440, W81XWH-20-1-0595), the Peer Reviewed Cancer Research Program (W81XWH-18-1-0404, W81XWH-21-1-0345), the Kidney Precision Medicine Project (KPMP) Glue Grant and sponsored research agreements from Bristol Myers-Squibb, Boehri"],"award-info":[{"award-number":["Breast Cancer Research Program (W81XWH-19-1-0668), the Prostate Cancer Research Program (W81XWH-15-1-0558, W81XWH-20-1-0851), the Lung Cancer Research Program (W81XWH-18-1-0440, W81XWH-20-1-0595), the Peer Reviewed Cancer Research Program (W81XWH-18-1-0404, W81XWH-21-1-0345), the Kidney Precision Medicine Project (KPMP) Glue Grant and sponsored research agreements from Bristol Myers-Squibb, Boehri"]}]},{"name":"NIH-NEI P30 Core Gran","award":["IP30EY025585"],"award-info":[{"award-number":["IP30EY025585"]}]},{"name":"Unrestricted Grants from The Research to Prevent Blindness, Inc (Cole Eye Institute), Cleveland Eye Bank Foundation awarded to the Cole Eye Institute","award":["IP30EY025585"],"award-info":[{"award-number":["IP30EY025585"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"DOI":"10.1038\/s41598-024-68259-0","type":"journal-article","created":{"date-parts":[[2024,7,30]],"date-time":"2024-07-30T20:03:33Z","timestamp":1722369813000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Combination of optical coherence tomography-derived shape and texture features are associated with development of sub-foveal geographic atrophy in dry AMD"],"prefix":"10.1038","volume":"14","author":[{"given":"Sudeshna Sil","family":"Kar","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hasan","family":"Cetin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Joseph","family":"Abraham","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sunil K.","family":"Srivastava","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anant","family":"Madabhushi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Justis P.","family":"Ehlers","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,7,30]]},"reference":[{"key":"68259_CR1","doi-asserted-by":"publisher","first-page":"7","DOI":"10.1016\/S0161-6420(97)30368-6","volume":"104","author":"R Klein","year":"1997","unstructured":"Klein, R., Klein, B., Jensen, S. & Meuer, S. The five-year incidence and progression of age-related maculopathy: The beaver dam eye study. Ophthalmology. 104, 7\u201321 (1997).","journal-title":"Ophthalmology."},{"issue":"3","key":"68259_CR2","doi-asserted-by":"publisher","first-page":"369","DOI":"10.1016\/j.ophtha.2017.08.038","volume":"125","author":"M Fleckenstein","year":"2018","unstructured":"Fleckenstein, M. et al. The progression of geographic atrophy secondary to age-related macular degeneration. Ophthalmology 125(3), 369\u2013390. https:\/\/doi.org\/10.1016\/j.ophtha.2017.08.038 (2018).","journal-title":"Ophthalmology"},{"issue":"8","key":"68259_CR3","doi-asserted-by":"publisher","first-page":"1043","DOI":"10.1111\/ceo.13834","volume":"48","author":"S Somasundaran","year":"2020","unstructured":"Somasundaran, S., Constable, I. J., Mellough, C. B. & Carvalho, L. S. Retinal pigment epithelium and age-related macular degeneration: A review of major disease mechanisms. Clin. Exp. Ophthalmol. 48(8), 1043\u20131056. https:\/\/doi.org\/10.1111\/ceo.13834 (2020).","journal-title":"Clin. Exp. Ophthalmol."},{"issue":"6","key":"68259_CR4","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1167\/iovs.61.6.11","volume":"61","author":"GS Reiter","year":"2020","unstructured":"Reiter, G. S. et al. Subretinal drusenoid deposits and photoreceptor loss detecting global and local progression of geographic atrophy by SD-OCT imaging. Invest. Ophthalmol. Vis. Sci. 61(6), 11. https:\/\/doi.org\/10.1167\/iovs.61.6.11 (2020).","journal-title":"Invest. Ophthalmol. Vis. Sci."},{"issue":"12","key":"68259_CR5","doi-asserted-by":"publisher","first-page":"1913","DOI":"10.1016\/j.ophtha.2018.05.028","volume":"125","author":"TD Keenan","year":"2018","unstructured":"Keenan, T. D. et al. Progression of geographic atrophy in age-related macular degeneration: AREDS2 report number 16. Ophthalmology. 125(12), 1913\u20131928. https:\/\/doi.org\/10.1016\/j.ophtha.2018.05.028 (2018).","journal-title":"Ophthalmology."},{"key":"68259_CR6","doi-asserted-by":"publisher","first-page":"118","DOI":"10.1016\/j.ajo.2017.03.031","volume":"179","author":"RG Sayegh","year":"2017","unstructured":"Sayegh, R. G. et al. Geographic atrophy and foveal-sparing changes related to visual acuity in patients with dry age-related macular degeneration over time. Am. J. Ophthalmol. 179, 118\u2013128. https:\/\/doi.org\/10.1016\/j.ajo.2017.03.031 (2017) (Epub 2017 Apr 4 PMID: 28385474).","journal-title":"Am. J. Ophthalmol."},{"issue":"5","key":"68259_CR7","doi-asserted-by":"publisher","first-page":"819","DOI":"10.1097\/IAE.0000000000001392","volume":"37","author":"DS Boyer","year":"2017","unstructured":"Boyer, D. S., Schmidt-Erfurth, U., van Lookeren, C. M., Henry, E. C. & Brittain, C. The pathophysiology of geographic atrophy secondary to age-related macular degeneration and the complement pathway as a therapeutic target. Retina. 37(5), 819\u2013835. https:\/\/doi.org\/10.1097\/IAE.0000000000001392 (2017).","journal-title":"Retina."},{"issue":"5","key":"68259_CR8","doi-asserted-by":"publisher","first-page":"2","DOI":"10.1167\/tvst.4.5.2","volume":"4","author":"Q Chen","year":"2015","unstructured":"Chen, Q. et al. Restricted summed-area projection for geographic atrophy visualization in SD-OCT images. Trans. Vis. Sci. Tech. 4(5), 2. https:\/\/doi.org\/10.1167\/tvst.4.5.2 (2015).","journal-title":"Trans. Vis. Sci. Tech."},{"issue":"8","key":"68259_CR9","doi-asserted-by":"publisher","first-page":"1737","DOI":"10.1016\/j.ophtha.2016.04.042","volume":"123","author":"S Niu","year":"2016","unstructured":"Niu, S., de Sisternes, L., Chen, Q., Rubin, D. L. & Leng, T. Fully automated prediction of geographic atrophy growth using quantitative spectral-domain optical coherence tomography biomarkers. Ophthalmology. 123(8), 1737\u20131750. https:\/\/doi.org\/10.1016\/j.ophtha.2016.04.042 (2016).","journal-title":"Ophthalmology."},{"key":"68259_CR10","doi-asserted-by":"publisher","first-page":"257","DOI":"10.1016\/j.ajo.2020.03.042","volume":"216","author":"U Schmidt-Erfurth","year":"2020","unstructured":"Schmidt-Erfurth, U. et al. Role of deep learning-quantified hyperreflective foci for the prediction of geographic atrophy progression. Am. J. Ophthalmol. 216, 257\u2013270. https:\/\/doi.org\/10.1016\/j.ajo.2020.03.042 (2020).","journal-title":"Am. J. Ophthalmol."},{"issue":"6","key":"68259_CR11","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1167\/iovs.61.6.11","volume":"61","author":"GS Reiter","year":"2020","unstructured":"Reiter, G. S. et al. Subretinal drusenoid deposits and photoreceptor loss detecting global and local progression of geographic atrophy by SD-OCT imaging. Investig. Ophthalmol. Visual Sci. 61(6), 11\u201311 (2020).","journal-title":"Investig. Ophthalmol. Visual Sci."},{"issue":"1","key":"68259_CR12","doi-asserted-by":"publisher","first-page":"31","DOI":"10.3928\/23258160-20211210-01","volume":"53","author":"K Sarici","year":"2022","unstructured":"Sarici, K. et al. Risk classification for progression to subfoveal geographic atrophy in dry age-related macular degeneration using machine learning-enabled outer retinal feature extraction. Ophthalmic Surg. Lasers Imaging Retina. 53(1), 31\u201339. https:\/\/doi.org\/10.3928\/23258160-20211210-01 (2022) (Epub 2022 Jan 1 PMID: 34982004).","journal-title":"Ophthalmic Surg. Lasers Imaging Retina."},{"issue":"2","key":"68259_CR13","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1167\/tvst.9.2.57","volume":"9","author":"J Arslan","year":"2020","unstructured":"Arslan, J. et al. Artificial intelligence algorithms for analysis of geographic atrophy: A review and evaluation. Transl. Vis. Sci. Technol. 9(2), 57. https:\/\/doi.org\/10.1167\/tvst.9.2.57 (2020).","journal-title":"Transl. Vis. Sci. Technol."},{"key":"68259_CR14","doi-asserted-by":"publisher","first-page":"1768","DOI":"10.1016\/S0161-6420(99)90340-8","volume":"106","author":"JS Sunness","year":"1999","unstructured":"Sunness, J. S. et al. Enlargement of atrophy and visual acuity loss in the geographic atrophy form of age-related macular degeneration. Ophthalmology 106, 1768\u20131779 (1999).","journal-title":"Ophthalmology"},{"key":"68259_CR15","doi-asserted-by":"publisher","first-page":"1168","DOI":"10.1001\/archophthalmol.2009.198","volume":"127","author":"AS Lindblad","year":"2009","unstructured":"Lindblad, A. S., Lloyd, P. C. & Clemons, T. E. Change in area of geographic atrophy in the age-related eye disease study. Arch Ophthalmol. 127, 1168\u20131174 (2009).","journal-title":"Arch Ophthalmol."},{"key":"68259_CR16","doi-asserted-by":"publisher","first-page":"2042","DOI":"10.1016\/j.ophtha.2013.03.029","volume":"120","author":"N Joachim","year":"2013","unstructured":"Joachim, N. et al. Incidence and progression of geographic atrophy: Observations from a populationbased cohort. Ophthalmology 120, 2042\u20132050 (2013).","journal-title":"Ophthalmology"},{"key":"68259_CR17","doi-asserted-by":"publisher","first-page":"1850","DOI":"10.1097\/IAE.0b013e31828991b2","volume":"33","author":"L Xu","year":"2013","unstructured":"Xu, L. et al. Reticular macular disease is associated with multilobular geographic atrophy in age-related macular degeneration. Retina 33, 1850\u20131862 (2013).","journal-title":"Retina"},{"issue":"13","key":"68259_CR18","doi-asserted-by":"publisher","first-page":"8375","DOI":"10.1167\/iovs.13-12552","volume":"54","author":"H Zhihong","year":"2013","unstructured":"Zhihong, H. et al. Segmentation of the geographic atrophy in spectral-domain optical coherence tomography and fundus autofluorescence images. Invest. Ophthalmol. Vis. Sci. 54(13), 8375\u20138383. https:\/\/doi.org\/10.1167\/iovs.13-12552 (2013).","journal-title":"Invest. Ophthalmol. Vis. Sci."},{"key":"68259_CR19","first-page":"3683","volume":"60","author":"JW Baish","year":"2000","unstructured":"Baish, J. W. & Jain, R. K. Fractals and cancer. Cancer Res. 60, 3683\u20133688 (2000).","journal-title":"Cancer Res."},{"key":"68259_CR20","doi-asserted-by":"publisher","first-page":"4615","DOI":"10.1007\/s00330-020-06788-8","volume":"30","author":"YW Park","year":"2020","unstructured":"Park, Y. W. et al. Magnetic resonance imaging-based 3-dimensional fractal dimension and lacunarity analyses may predict the meningioma grade. Eur. Radiol. 30, 4615\u20134622. https:\/\/doi.org\/10.1007\/s00330-020-06788-8 (2020).","journal-title":"Eur. Radiol."},{"key":"68259_CR21","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/1756-6649-10-1","volume":"10","author":"JO Rodr\u00edguez","year":"2010","unstructured":"Rodr\u00edguez, J. O. et al. Theoretical generalization of normal and sick coronary arteries with fractal dimensions and the arterial intrinsic mathematical harmony. BMC Med. Phys. 10, 1. https:\/\/doi.org\/10.1186\/1756-6649-10-1 (2010).","journal-title":"BMC Med. Phys."},{"key":"68259_CR22","doi-asserted-by":"publisher","first-page":"175","DOI":"10.1016\/j.semcancer.2011.04.002","volume":"21","author":"M Bizzarri","year":"2011","unstructured":"Bizzarri, M. et al. Fractal analysis in a systems biology approach to cancer. Semin. Cancer Biol. 21, 175\u2013182. https:\/\/doi.org\/10.1016\/j.semcancer.2011.04.002 (2011).","journal-title":"Semin. Cancer Biol."},{"key":"68259_CR23","doi-asserted-by":"publisher","first-page":"274","DOI":"10.1076\/ceyr.24.4.274.8411","volume":"24","author":"A Avakian","year":"2002","unstructured":"Avakian, A. et al. Fractal analysis of region-based vascular change in the normal and non-proliferative diabetic retina. Curr. Eye Res. 24, 274\u2013280. https:\/\/doi.org\/10.1076\/ceyr.24.4.274.8411 (2002).","journal-title":"Curr. Eye Res."},{"key":"68259_CR24","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1080\/10739680601072123","volume":"14","author":"TA Gardiner","year":"2007","unstructured":"Gardiner, T. A., Archer, D. B., Curtis, T. M. & Stitt, A. W. Arteriolar involvement in the microvascular lesions of diabetic retinopathy: Implications for pathogenesis. Microcirculation. 14, 25\u201338. https:\/\/doi.org\/10.1080\/10739680601072123 (2007).","journal-title":"Microcirculation."},{"key":"68259_CR25","doi-asserted-by":"publisher","first-page":"1400","DOI":"10.1016\/j.ophtha.2009.10.047","volume":"117","author":"J Grauslund","year":"2010","unstructured":"Grauslund, J. et al. Retinal vascular fractals and microvascular and macrovascular complications in type 1 diabetes. Ophthalmology. 117, 1400\u20131405. https:\/\/doi.org\/10.1016\/j.ophtha.2009.10.047 (2010).","journal-title":"Ophthalmology."},{"key":"68259_CR26","doi-asserted-by":"crossref","unstructured":"Doan NT, van Rooden S, Versluis MJ, Buijs M, Webb AG, van der Grond J, et al. An automated tool for cortical feature analysis: Application to differences on 7 Tesla T2 (*)-weighted images between young and older healthy subjects. Magn. Reson. Med. 1\u20139 (2014).","DOI":"10.1002\/mrm.25397"},{"key":"68259_CR27","doi-asserted-by":"publisher","DOI":"10.1155\/2013\/262931","volume":"2013","author":"B Braverman","year":"2013","unstructured":"Braverman, B. & Tambasco, M. Scale-specific multifractal medical image analysis. Comput. Math. Methods Med. 2013, 262931. https:\/\/doi.org\/10.1155\/2013\/262931 (2013).","journal-title":"Comput. Math. Methods Med."},{"key":"68259_CR28","doi-asserted-by":"publisher","first-page":"80","DOI":"10.1186\/s12968-015-0179-0","volume":"17","author":"G Captur","year":"2015","unstructured":"Captur, G. et al. Fractal frontiers in cardiovascular magnetic resonance: Towards clinical implementation. J. Cardiovasc. Magn. Reson. 17, 80. https:\/\/doi.org\/10.1186\/s12968-015-0179-0 (2015).","journal-title":"J. Cardiovasc. Magn. Reson."},{"issue":"10","key":"68259_CR29","doi-asserted-by":"publisher","first-page":"2914","DOI":"10.1109\/TBME.2023.3270201","volume":"70","author":"SS Kar","year":"2023","unstructured":"Kar, S. S. et al. Novel fractal-based sub-RPE compartment OCT radiomics biomarkers are associated with subfoveal geographic atrophy in dry AMD. IEEE Trans. Biomed. Eng. 70(10), 2914\u20132921. https:\/\/doi.org\/10.1109\/TBME.2023.3270201 (2023).","journal-title":"IEEE Trans. Biomed. Eng."},{"issue":"9","key":"68259_CR30","doi-asserted-by":"publisher","first-page":"1000113","DOI":"10.1109\/JTEHM.2021.3096378","volume":"12","author":"S Sil Kar","year":"2021","unstructured":"Sil Kar, S. et al. Multi-compartment spatially-derived radiomics from optical coherence tomography predict anti-VEGF treatment durability in macular edema secondary to retinal vascular disease: Preliminary findings. IEEE J. Transl. Eng. Health Med. 12(9), 1000113. https:\/\/doi.org\/10.1109\/JTEHM.2021.3096378 (2021).","journal-title":"IEEE J. Transl. Eng. Health Med."},{"key":"68259_CR31","unstructured":"https:\/\/www.wma.net\/policies-post\/wma-declaration-of-helsinki-ethical-principles-for-medical-research-involving-human-subjects\/"},{"issue":"8","key":"68259_CR32","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0200730","volume":"13","author":"G Penzias","year":"2018","unstructured":"Penzias, G. et al. Identifying the morphologic basis for radiomic features in distinguishing different Gleason grades of prostate cancer on MRI: Preliminary findings. PLoS One. 13(8), e0200730. https:\/\/doi.org\/10.1371\/journal.pone.0200730 (2018).","journal-title":"PLoS One."},{"issue":"3","key":"68259_CR33","doi-asserted-by":"publisher","first-page":"295","DOI":"10.1136\/bjophthalmol-2015-307105","volume":"100","author":"Y Itoh","year":"2016","unstructured":"Itoh, Y. et al. Volumetric ellipsoid zone mapping for enhanced visualisation of outer retinal integrity with optical coherence tomography. Br. J. Ophthalmol. 100(3), 295\u2013299 (2016).","journal-title":"Br. J. Ophthalmol."},{"issue":"1","key":"68259_CR34","doi-asserted-by":"publisher","first-page":"40","DOI":"10.1177\/2474126420943418","volume":"5","author":"JR Abraham","year":"2021","unstructured":"Abraham, J. R. et al. Longitudinal assessment of ellipsoid zone mapping parameters in retinal venous occlusive disease with associated macular edema. J. VitreoRetinal Diseases. 5(1), 40\u201345 (2021).","journal-title":"J. VitreoRetinal Diseases."},{"key":"68259_CR35","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1016\/j.neucom.2017.03.032","volume":"247","author":"M Abdechiri","year":"2017","unstructured":"Abdechiri, M., Faez, K. & Amindavar, H. Visual object tracking with online weighted chaotic multiple instance learning. Neurocomputing. 247, 16\u201330 (2017).","journal-title":"Neurocomputing."},{"key":"68259_CR36","doi-asserted-by":"publisher","first-page":"181","DOI":"10.1007\/s11242-016-0770-3","volume":"116","author":"S Al-Zainaldin","year":"2017","unstructured":"Al-Zainaldin, S., Glover, P. W. J. & Lorinczi, P. Synthetic fractal modelling of heterogeneous and anisotropic reservoirs for use in simulation studies: Implications on their hydrocarbon recovery prediction. Transp. Porous Media. 116, 181\u2013212. https:\/\/doi.org\/10.1007\/s11242-016-0770-3 (2017).","journal-title":"Transp. Porous Media."},{"key":"68259_CR37","first-page":"39","volume":"10","author":"SR Nayak","year":"2018","unstructured":"Nayak, S. R., Mishra, J. & Mohan, J. P. Fractal analysis of image sets using differential box counting techniques. Int. J. Inf. Technol. 10, 39\u201347 (2018).","journal-title":"Int. J. Inf. Technol."},{"key":"68259_CR38","unstructured":"J. Zhang (2022). Calculation of fractal dimension of a 3D volume using fft (https:\/\/www.mathworks.com\/matlabcentral\/fileexchange\/6964-calculation-of-fractal-dimension-of-a-3d-volume-using-fft), MATLAB Central File Exchange. Retrieved January 3, 2022."},{"issue":"6","key":"68259_CR39","doi-asserted-by":"publisher","first-page":"610","DOI":"10.1109\/TSMC.1973.4309314","volume":"3","author":"RM Haralick","year":"1973","unstructured":"Haralick, R. M., Shanmugam, K. & Dinstein, I. Textural features for image classification. IEEE Trans. Syst. Man Cybernetics. 3(6), 610\u2013621. https:\/\/doi.org\/10.1109\/TSMC.1973.4309314 (1973).","journal-title":"IEEE Trans. Syst. Man Cybernetics."},{"key":"68259_CR40","doi-asserted-by":"crossref","unstructured":"Laws KI. Textured image segmentation. University of Southern California Los Angeles Image Processing Inst, 1980 Jan. Report No.: USCIPI-940. http:\/\/www.dtic.mil\/docs\/citations\/ADA083283. Accessed November 24, 2016.","DOI":"10.21236\/ADA083283"},{"key":"68259_CR41","doi-asserted-by":"publisher","unstructured":"A. K. Jain and F. Farrokhnia. (1990) Unsupervised texture segmentation using Gabor filters. in 1990 IEEE International Conference on Systems, Man, and Cybernetics Conference Proceedings, Los Angeles, CA, USA., pp. 14\u201319. https:\/\/doi.org\/10.1109\/ICSMC.1990.142050.","DOI":"10.1109\/ICSMC.1990.142050"},{"key":"68259_CR42","doi-asserted-by":"publisher","first-page":"37241","DOI":"10.1038\/srep37241","volume":"6","author":"P Prasanna","year":"2016","unstructured":"Prasanna, P. et al. Co-occurrence of local anisotropic gradient orientations (CoLlAGe): A new radiomics descriptor. Sci. Rep. 6, 37241. https:\/\/doi.org\/10.1038\/srep37241 (2016).","journal-title":"Sci. Rep."},{"key":"68259_CR43","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1186\/s12859-016-1423-9","volume":"18","author":"M Radovic","year":"2017","unstructured":"Radovic, M. et al. Minimum redundancy maximum relevance feature selection approach for temporal gene expression data. BMC Bioinform. 18, 9. https:\/\/doi.org\/10.1186\/s12859-016-1423-9 (2017).","journal-title":"BMC Bioinform."},{"key":"68259_CR44","doi-asserted-by":"publisher","first-page":"581","DOI":"10.1364\/BOE.7.000581","volume":"7","author":"S Niu","year":"2016","unstructured":"Niu, S., de Sisternes, L., Chen, Q., Leng, T. & Rubin, D. L. Automated geographic atrophy segmentation for SD-OCT images using region-based C-V model via local similarity factor. Biomed. Opt. Express. 7, 581\u2013600 (2016).","journal-title":"Biomed. Opt. Express."},{"issue":"8","key":"68259_CR45","doi-asserted-by":"publisher","first-page":"1527","DOI":"10.1097\/IAE.0000000000002206","volume":"39","author":"M Pfau","year":"2019","unstructured":"Pfau, M. et al. Prognostic value of shape-descriptive factors for the progression of geographic atrophy secondary to age-related macular degeneration. Retina 39(8), 1527\u20131540. https:\/\/doi.org\/10.1097\/IAE.0000000000002206 (2019).","journal-title":"Retina"},{"issue":"8","key":"68259_CR46","doi-asserted-by":"publisher","first-page":"1086","DOI":"10.1016\/j.ophtha.2020.02.009","volume":"127","author":"B Liefers","year":"2020","unstructured":"Liefers, B. et al. A deep learning model for segmentation of geographic atrophy to study its long-term natural history. Ophthalmology. 127(8), 1086\u20131096 (2020).","journal-title":"Ophthalmology."},{"issue":"3","key":"68259_CR47","doi-asserted-by":"publisher","first-page":"243","DOI":"10.1016\/j.oret.2022.08.018","volume":"7","author":"N Anegondi","year":"2023","unstructured":"Anegondi, N. et al. Deep learning to predict geographic atrophy area and growth rate from multimodal imaging. Ophthalmol. Retina 7(3), 243\u2013252. https:\/\/doi.org\/10.1016\/j.oret.2022.08.018 (2023).","journal-title":"Ophthalmol. Retina"},{"key":"68259_CR48","doi-asserted-by":"publisher","DOI":"10.1001\/jamaophthalmol.2023.4659","author":"ER Dow","year":"2023","unstructured":"Dow, E. R. et al. A deep-learning algorithm to predict short-term progression to geographic atrophy on spectral-domain optical coherence tomography. JAMA Ophthalmol. https:\/\/doi.org\/10.1001\/jamaophthalmol.2023.4659 (2023).","journal-title":"JAMA Ophthalmol."},{"key":"68259_CR49","doi-asserted-by":"publisher","first-page":"852107","DOI":"10.3389\/fopht.2022.852107","volume":"2","author":"V Dong","year":"2022","unstructured":"Dong, V. et al. Evaluating the utility of deep learning for predicting therapeutic response in diabetic eye disease. Front. Ophthalmol. (Lausanne). 2, 852107. https:\/\/doi.org\/10.3389\/fopht.2022.852107 (2022).","journal-title":"Front. Ophthalmol. (Lausanne)."},{"issue":"8","key":"68259_CR50","doi-asserted-by":"publisher","first-page":"1155","DOI":"10.1136\/bjophthalmol-2020-317182","volume":"105","author":"P Prasanna","year":"2021","unstructured":"Prasanna, P. et al. Radiomics-based assessment of ultra-widefield leakage patterns and vessel network architecture in the PERMEATE study: Insights into treatment durability. Br. J. Ophthalmol. 105(8), 1155\u20131160 (2021).","journal-title":"Br. J. Ophthalmol."},{"issue":"Suppl 1","key":"68259_CR51","doi-asserted-by":"publisher","first-page":"S26","DOI":"10.1097\/IAE.0000000000001330","volume":"36","author":"EC Zanzottera","year":"2016","unstructured":"Zanzottera, E. C. et al. Visualizing retinal pigment epithelium phenotypes in the transition to atrophy in neovascular age-related macular degeneration. Retina. 36(Suppl 1), S26\u2013S39. https:\/\/doi.org\/10.1097\/IAE.0000000000001330 (2016).","journal-title":"Retina."},{"key":"68259_CR52","doi-asserted-by":"publisher","first-page":"552","DOI":"10.1038\/eye.1988.106","volume":"2","author":"JP Sarks","year":"1988","unstructured":"Sarks, J. P., Sarks, S. H. & Killingsworth, M. C. Evolution of geographic atrophy of the retinal pigment epithelium. Eye. 2, 552\u2013577 (1988).","journal-title":"Eye."}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-024-68259-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-024-68259-0","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-024-68259-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,30]],"date-time":"2024-07-30T20:07:29Z","timestamp":1722370049000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-024-68259-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,7,30]]},"references-count":52,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["68259"],"URL":"https:\/\/doi.org\/10.1038\/s41598-024-68259-0","relation":{},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,7,30]]},"assertion":[{"value":"12 March 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 July 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 July 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"Dr. Srivastava has research support from Regeneron, Allergan, and Gilead; is a consultant for Bausch and Lomb, Novartis, and Regeneron. Dr. Madabhushi has research Funding: Astrazeneca, Bristol Myers-Squibb, Boehringer-Ingelheim, Eli-Lilly. Equity: Picture Health Inc, Inspirata Inc., Elucid Bioimaging, Consultant: Aiforia, Caris, Roche, Biohme, Castle Biosciences, Simbiosys. Dr. Ehlers has research support from the following: Aerpio, Alcon, Thrombogenics\/Oxurion, Regeneron, Genentech, Novartis, Allergan, Apellis, Iveric, Stealth, Perceive Biotherapeutics, Roche, Alexion; is a consultant for the following: Aerpio, Adverum, Alcon, Allegro, Allergan, Genentech\/Roche, Stealth, Novartis, Thrombogenics\/Oxurion, Leica, Zeiss, Regeneron, Santen, Perceive Biotherapeutics, Boerhinger-Ingelheim, Exegenesis, RegenxBIO, Roche, Alexion; and holds a patent with Leica. Dr. Sil Kar, Dr. Cetin and Dr. Abraham have no financial disclosures to report.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"17602"}}