{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,22]],"date-time":"2026-07-22T05:33:54Z","timestamp":1784698434225,"version":"3.55.0"},"reference-count":46,"publisher":"Springer Science and Business Media LLC","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,2,29]]},"DOI":"10.7759\/cureus.55216","type":"journal-article","created":{"date-parts":[[2024,2,29]],"date-time":"2024-02-29T11:55:51Z","timestamp":1709207751000},"source":"Crossref","is-referenced-by-count":3,"title":["Ocular Pathology and Genetics: Transformative Role of Artificial Intelligence (AI) in Anterior Segment Diseases"],"prefix":"10.7759","author":[{"given":"Priyanka","family":"Venkatapathappa","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ayesha","family":"Sultana","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vidhya","family":"K S","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Romy","family":"Mansour","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Venkateshappa","family":"Chikkanarayanappa","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Harish","family":"Rangareddy","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.18240\/ijo.2018.01.20","article-title":"Genes and genetics in eye diseases: a genomic medicine approach for investigating hereditary and inflammatory ocular disorders","volume":"11","author":"Singh M","year":"2018","unstructured":"Singh M, Tyagi SC. Genes and genetics in eye diseases: a genomic medicine approach for investigating hereditary and inflammatory ocular disorders. Int J Ophthalmol. 2018, 11:117-34. 10.18240\/ijo.2018.01.20","journal-title":"Int J Ophthalmol"},{"key":"ref2","article-title":"Genetics, autosomal dominant","author":"Lewis RG","year":"2023","unstructured":"Lewis RG, Simpson B. Genetics, autosomal dominant. StatPearls. StatPearls Publishing, Treasure Island, FL; 2023."},{"key":"ref3","article-title":"Genetics, autosomal recessive","author":"Gulani A","year":"2023","unstructured":"Gulani A, Weiler T. Genetics, autosomal recessive. StatPearls. StatPearls Publishing, Treasure Island, FL; 2023."},{"key":"ref4","article-title":"Genetics, X-linked inheritance","author":"Basta M","year":"2023","unstructured":"Basta M, Pandya AM. Genetics, X-linked inheritance. StatPearls. StatPearls Publishing, Treasure Island, FL; 2023."},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-13-8367-0_13","article-title":"Mitochondrial genetics","author":"Gon\u00e7alves VF","year":"2019","unstructured":"Gon\u00e7alves VF. Mitochondrial genetics. Mitochondria in Health and in Sickness. Advances in Experimental Medicine and Biology. Urbani A, Babu M (ed): Springer, Singapore; 2019. 1158:10.1007\/978-981-13-8367-0_13"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1016\/j.survophthal.2021.03.003","article-title":"Artificial intelligence: the unstoppable revolution in ophthalmology","volume":"67","author":"Benet D","year":"2022","unstructured":"Benet D, Pellicer-Valero OJ. Artificial intelligence: the unstoppable revolution in ophthalmology. Surv Ophthalmol. 2022, 67:252-70. 10.1016\/j.survophthal.2021.03.003","journal-title":"Surv Ophthalmol"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1186\/1750-1172-4-7","article-title":"Corneal dystrophies","volume":"4","author":"Klintworth GK","year":"2009","unstructured":"Klintworth GK. Corneal dystrophies. Orphanet J Rare Dis. 2009, 4:7. 10.1186\/1750-1172-4-7","journal-title":"Orphanet J Rare Dis"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1097\/ICO.0000000000000307","article-title":"IC3D classification of corneal dystrophies--edition 2","volume":"34","author":"Weiss JS","year":"2015","unstructured":"Weiss JS, M\u00f8ller HU, Aldave AJ, et al.. IC3D classification of corneal dystrophies--edition 2. Cornea. 2015, 34:117-59. 10.1097\/ICO.0000000000000307","journal-title":"Cornea"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1177\/2515841418815802","article-title":"Corneal endothelial cell dysfunction: etiologies and management","volume":"10","author":"Feizi S","year":"2018","unstructured":"Feizi S. Corneal endothelial cell dysfunction: etiologies and management. Ther Adv Ophthalmol. 2018, 10:10.1177\/2515841418815802","journal-title":"Ther Adv Ophthalmol"},{"key":"ref10","article-title":"Genetic disorders of the cornea","author":"Klintworth GK","year":"2008","unstructured":"Klintworth GK. Genetic disorders of the cornea. Garner and Klintworth's Pathobiology of Ocular Disease. 3rd Edition. Klintworth GK, Garner A (ed): CRC Press, Boca Raton, FL; 2008."},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.3390\/cells12091280","article-title":"Corneal regeneration using gene therapy approaches","volume":"12","author":"Sarkar S","year":"2023","unstructured":"Sarkar S, Panikker P, D'Souza S, Shetty R, Mohan RR, Ghosh A. Corneal regeneration using gene therapy approaches. Cells. 2023, 12:1280. 10.3390\/cells12091280","journal-title":"Cells"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.18240\/ijo.2023.09.03","article-title":"Guidelines for the application of artificial intelligence in the diagnosis of anterior segment diseases (2023)","volume":"16","author":"Shao Y","year":"2023","unstructured":"Shao Y, Jie Y, Liu ZG. Guidelines for the application of artificial intelligence in the diagnosis of anterior segment diseases (2023). Int J Ophthalmol. 2023, 16:1373-85. 10.18240\/ijo.2023.09.03","journal-title":"Int J Ophthalmol"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.4103\/sjopt.sjopt_52_23","article-title":"Artificial intelligence in cornea and ocular surface diseases","volume":"37","author":"Pagano L","year":"2023","unstructured":"Pagano L, Posarelli M, Giannaccare G, Coco G, Scorcia V, Romano V, Borgia A. Artificial intelligence in cornea and ocular surface diseases. Saudi J Ophthalmol. 2023, 37:179-84. 10.4103\/sjopt.sjopt_52_23","journal-title":"Saudi J Ophthalmol"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-020-75027-3","article-title":"Deep learning for identifying corneal diseases from ocular surface slit-lamp photographs","volume":"10","author":"Gu H","year":"2020","unstructured":"Gu H, Guo Y, Gu L, et al.. Deep learning for identifying corneal diseases from ocular surface slit-lamp photographs. Sci Rep. 2020, 10:17851. 10.1038\/s41598-020-75027-3","journal-title":"Sci Rep"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1016\/j.xops.2023.100324","article-title":"Evaluating the performance of ChatGPT in ophthalmology: an analysis of its successes and shortcomings","volume":"3","author":"Antaki F","year":"2023","unstructured":"Antaki F, Touma S, Milad D, El-Khoury J, Duval R. Evaluating the performance of ChatGPT in ophthalmology: an analysis of its successes and shortcomings. Ophthalmol Sci. 2023, 3:100324. 10.1016\/j.xops.2023.100324","journal-title":"Ophthalmol Sci"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1101\/2023.08.25.23294635","article-title":"Performance of ChatGPT in diagnosis of corneal eye diseases [PREPRINT]","author":"Delsoz M","year":"2023","unstructured":"Delsoz M, Madadi Y, Munir WM, et al.. Performance of ChatGPT in diagnosis of corneal eye diseases [PREPRINT]. medRxiv. 2023, 10.1101\/2023.08.25.23294635","journal-title":"medRxiv"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1007\/s00401-016-1609-2","article-title":"Glaucoma: the retina and beyond","volume":"132","author":"Davis BM","year":"2016","unstructured":"Davis BM, Crawley L, Pahlitzsch M, Javaid F, Cordeiro MF. Glaucoma: the retina and beyond. Acta Neuropathol. 2016, 132:807-26. 10.1007\/s00401-016-1609-2","journal-title":"Acta Neuropathol"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1111\/ceo.14035","article-title":"The genetics of glaucoma: disease associations, personalised risk assessment and therapeutic opportunities-a review","volume":"50","author":"Aboobakar IF","year":"2022","unstructured":"Aboobakar IF, Wiggs JL. The genetics of glaucoma: disease associations, personalised risk assessment and therapeutic opportunities-a review. Clin Exp Ophthalmol. 2022, 50:143-62. 10.1111\/ceo.14035","journal-title":"Clin Exp Ophthalmol"},{"key":"ref19","article-title":"Myocilin-associated glaucoma: a historical perspective and recent research progress","volume":"27","author":"Sharma R","year":"2021","unstructured":"Sharma R, Grover A. Myocilin-associated glaucoma: a historical perspective and recent research progress. Mol Vis. 2021, 27:480-93.","journal-title":"Mol Vis"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1167\/iovs.16-20235","article-title":"CYP1B1 regulates ocular fissure closure through a retinoic acid-independent pathway","volume":"58","author":"Williams AL","year":"2017","unstructured":"Williams AL, Eason J, Chawla B, Bohnsack BL. CYP1B1 regulates ocular fissure closure through a retinoic acid-independent pathway. Invest Ophthalmol Vis Sci. 2017, 58:1084-97. 10.1167\/iovs.16-20235","journal-title":"Invest Ophthalmol Vis Sci"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1016\/j.ajhg.2009.03.017","article-title":"Null mutations in LTBP2 cause primary congenital glaucoma","volume":"84","author":"Ali M","year":"2009","unstructured":"Ali M, McKibbin M, Booth A, et al.. Null mutations in LTBP2 cause primary congenital glaucoma. Am J Hum Genet. 2009, 84:664-71. 10.1016\/j.ajhg.2009.03.017","journal-title":"Am J Hum Genet"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1172\/JCI85830","article-title":"Angiopoietin receptor TEK mutations underlie primary congenital glaucoma with variable expressivity","volume":"126","author":"Souma T","year":"2016","unstructured":"Souma T, Tompson SW, Thomson BR, et al.. Angiopoietin receptor TEK mutations underlie primary congenital glaucoma with variable expressivity. J Clin Invest. 2016, 126:2575-87. 10.1172\/JCI85830","journal-title":"J Clin Invest"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1172\/JCI95545","article-title":"Angiopoietin-1 is required for Schlemm's canal development in mice and humans","volume":"127","author":"Thomson BR","year":"2017","unstructured":"Thomson BR, Souma T, Tompson SW, et al.. Angiopoietin-1 is required for Schlemm's canal development in mice and humans. J Clin Invest. 2017, 127:4421-36. 10.1172\/JCI95545","journal-title":"J Clin Invest"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.18502\/jovr.v18i1.12730","article-title":"Clinical applications of artificial intelligence in glaucoma","volume":"18","author":"Yousefi S","year":"2023","unstructured":"Yousefi S. Clinical applications of artificial intelligence in glaucoma. J Ophthalmic Vis Res. 2023, 18:97-112. 10.18502\/jovr.v18i1.12730","journal-title":"J Ophthalmic Vis Res"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1111\/j.1475-1313.1994.tb00004.x","article-title":"Visual field analysis using artificial neural networks","volume":"14","author":"Spenceley SE","year":"1994","unstructured":"Spenceley SE, Henson DB, Bull DR. Visual field analysis using artificial neural networks. Ophthalmic Physiol Opt. 1994, 14:239-48. 10.1111\/j.1475-1313.1994.tb00004.x","journal-title":"Ophthalmic Physiol Opt"},{"key":"ref26","doi-asserted-by":"crossref","DOI":"10.1097\/00061198-199902000-00014","article-title":"Neural networks for visual field analysis: how do they compare with other algorithms?","volume":"8","author":"Lietman T","year":"1999","unstructured":"Lietman T, Eng J, Katz J, Quigley HA. Neural networks for visual field analysis: how do they compare with other algorithms?. J Glaucoma. 1999, 8:77-80.","journal-title":"J Glaucoma"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/TBME.2002.802012","article-title":"Comparison of machine learning and traditional classifiers in glaucoma diagnosis","volume":"49","author":"Chan K","year":"2002","unstructured":"Chan K, Lee TW, Sample PA, Goldbaum MH, Weinreb RN, Sejnowski TJ. Comparison of machine learning and traditional classifiers in glaucoma diagnosis. IEEE Trans Biomed Eng. 2002, 49:963-74. 10.1109\/TBME.2002.802012","journal-title":"IEEE Trans Biomed Eng"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1016\/j.ajo.2018.10.007","article-title":"Using deep learning and transfer learning to accurately diagnose early-onset glaucoma from macular optical coherence tomography images","volume":"198","author":"Asaoka R","year":"2019","unstructured":"Asaoka R, Murata H, Hirasawa K, et al.. Using deep learning and transfer learning to accurately diagnose early-onset glaucoma from macular optical coherence tomography images. Am J Ophthalmol. 2019, 198:136-45. 10.1016\/j.ajo.2018.10.007","journal-title":"Am J Ophthalmol"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1186\/s12880-018-0273-5","article-title":"Automatic differentiation of glaucoma visual field from non-glaucoma visual filed using deep convolutional neural network","volume":"18","author":"Li F","year":"2018","unstructured":"Li F, Wang Z, Qu G, et al.. Automatic differentiation of glaucoma visual field from non-glaucoma visual filed using deep convolutional neural network. BMC Med Imaging. 2018, 18:35. 10.1186\/s12880-018-0273-5","journal-title":"BMC Med Imaging"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1016\/j.ophtha.2020.12.020","article-title":"Assessing glaucoma progression using machine learning trained on longitudinal visual field and clinical data","volume":"128","author":"Dixit A","year":"2021","unstructured":"Dixit A, Yohannan J, Boland MV. Assessing glaucoma progression using machine learning trained on longitudinal visual field and clinical data. Ophthalmology. 2021, 128:1016-26. 10.1016\/j.ophtha.2020.12.020","journal-title":"Ophthalmology"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-020-00329-9","article-title":"Development and clinical deployment of a smartphone-based visual field deep learning system for glaucoma detection","volume":"3","author":"Li F","year":"2020","unstructured":"Li F, Song D, Chen H, et al.. Development and clinical deployment of a smartphone-based visual field deep learning system for glaucoma detection. NPJ Digit Med. 2020, 3:123. 10.1038\/s41746-020-00329-9","journal-title":"NPJ Digit Med"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1016\/S2589-7500(19)30085-8","article-title":"Detection of glaucomatous optic neuropathy with spectral-domain optical coherence tomography: a retrospective training and validation deep-learning analysis","volume":"1","author":"Ran AR","year":"2019","unstructured":"Ran AR, Cheung CY, Wang X, et al.. Detection of glaucomatous optic neuropathy with spectral-domain optical coherence tomography: a retrospective training and validation deep-learning analysis. Lancet Digit Health. 2019, 1:e172-82. 10.1016\/S2589-7500(19)30085-8","journal-title":"Lancet Digit Health"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1167\/tvst.11.5.11","article-title":"Deep learning for glaucoma detection and identification of novel diagnostic areas in diverse real-world datasets","volume":"11","author":"Noury E","year":"2022","unstructured":"Noury E, Mannil SS, Chang RT, et al.. Deep learning for glaucoma detection and identification of novel diagnostic areas in diverse real-world datasets. Transl Vis Sci Technol. 2022, 11:11. 10.1167\/tvst.11.5.11","journal-title":"Transl Vis Sci Technol"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1016\/j.xops.2022.100222","article-title":"Visual field prediction: evaluating the clinical relevance of deep learning models","volume":"3","author":"Eslami M","year":"2023","unstructured":"Eslami M, Kim JA, Zhang M, Boland MV, Wang M, Chang DS, Elze T. Visual field prediction: evaluating the clinical relevance of deep learning models. Ophthalmol Sci. 2023, 3:100222. 10.1016\/j.xops.2022.100222","journal-title":"Ophthalmol Sci"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-019-54653-6","article-title":"Estimating rates of progression and predicting future visual fields in glaucoma using a deep variational autoencoder","volume":"9","author":"Berchuck SI","year":"2019","unstructured":"Berchuck SI, Mukherjee S, Medeiros FA. Estimating rates of progression and predicting future visual fields in glaucoma using a deep variational autoencoder. Sci Rep. 2019, 9:18113. 10.1038\/s41598-019-54653-6","journal-title":"Sci Rep"},{"key":"ref36","article-title":"Congenital and hereditary cataracts: epidemiology and genetics","author":"Shoshany N","year":"2020","unstructured":"Shoshany N, Hejtmancik JF, Shiels A, Datiles M. Congenital and hereditary cataracts: epidemiology and genetics. Pediatric Cataract Surgery and IOL Implantation. Kraus CL (ed): Springer, Cham, Switzerland; 2020. 3-24."},{"key":"ref37","article-title":"Crystallin gene mutations in Indian families with inherited pediatric cataract","volume":"14","author":"Devi RR","year":"2008","unstructured":"Devi RR, Yao W, Vijayalakshmi P, Sergeev YV, Sundaresan P, Hejtmancik JF. Crystallin gene mutations in Indian families with inherited pediatric cataract. Mol Vis. 2008, 14:1157-70.","journal-title":"Mol Vis"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1002\/mgg3.1290","article-title":"A novel dominant mutation in CRYAB gene leading to a severe phenotype with childhood onset","volume":"8","author":"Marcos AT","year":"2020","unstructured":"Marcos AT, Amor\u00f3s D, Mu\u00f1oz-Cabello B, Gal\u00e1n F, Rivas Infante E, Alcaraz-Mas L, Navarro-Pando JM. A novel dominant mutation in CRYAB gene leading to a severe phenotype with childhood onset. Mol Genet Genomic Med. 2020, 8:e1290. 10.1002\/mgg3.1290","journal-title":"Mol Genet Genomic Med"},{"key":"ref39","article-title":"Autosomal recessive congenital ichthyosis","author":"Richard G","year":"2023","unstructured":"Richard G. Autosomal recessive congenital ichthyosis. GeneReviews. Adam MP, Feldman J, Mirzaa GM, et al. (ed): GeneReviews, Seattle, WA; 2023."},{"key":"ref40","article-title":"Mapping the gene for X-linked cataracts and microcornea with facial, dental, and skeletal features to Xp22: an appraisal of the Nance-Horan syndrome","volume":"87","author":"Lewis RA","year":"1989","unstructured":"Lewis RA. Mapping the gene for X-linked cataracts and microcornea with facial, dental, and skeletal features to Xp22: an appraisal of the Nance-Horan syndrome. Trans Am Ophthalmol Soc. 1989, 87:658-728.","journal-title":"Trans Am Ophthalmol Soc"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1167\/iovs.04-0979","article-title":"Risk factors for idiopathic congenital\/infantile cataract","volume":"46","author":"Haargaard B","year":"2005","unstructured":"Haargaard B, Wohlfahrt J, Rosenberg T, Fledelius HC, Melbye M. Risk factors for idiopathic congenital\/infantile cataract. Invest Ophthalmol Vis Sci. 2005, 46:3067-73. 10.1167\/iovs.04-0979","journal-title":"Invest Ophthalmol Vis Sci"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1016\/j.exer.2021.108662","article-title":"Inherited cataracts: genetic mechanisms and pathways new and old","volume":"209","author":"Shiels A","year":"2021","unstructured":"Shiels A, Hejtmancik JF. Inherited cataracts: genetic mechanisms and pathways new and old. Exp Eye Res. 2021, 209:108662. 10.1016\/j.exer.2021.108662","journal-title":"Exp Eye Res"},{"key":"ref43","article-title":"Molecular biology and inherited disorders of the eye lens","author":"Hejtmancik JF","year":"2001","unstructured":"Hejtmancik JF, Kaiser-Kupfer MI, Piatigorsky J. Molecular biology and inherited disorders of the eye lens. The Metabolic and Molecular Basis of Inherited Disease. Scriver CR, Beaudet AL, Valle D, Sly WS, Childs B, Kinzler KW, Vogelstein B (ed): McGraw Hill, New York, NY; 2001."},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1097\/APO.0000000000000394","article-title":"Artificial intelligence in cornea, refractive surgery, and cataract: basic principles, clinical applications, and future directions","volume":"10","author":"Rampat R","year":"2021","unstructured":"Rampat R, Deshmukh R, Chen X, Ting DS, Said DG, Dua HS, Ting DS. Artificial intelligence in cornea, refractive surgery, and cataract: basic principles, clinical applications, and future directions. Asia Pac J Ophthalmol (Phila). 2021, 10:268-81. 10.1097\/APO.0000000000000394","journal-title":"Asia Pac J Ophthalmol (Phila)"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1016\/j.ebiom.2019.102621","article-title":"A practical model for the identification of congenital cataracts using machine learning","volume":"51","author":"Lin D","year":"2020","unstructured":"Lin D, Chen J, Lin Z, et al.. A practical model for the identification of congenital cataracts using machine learning. EBioMedicine. 2020, 51:102621. 10.1016\/j.ebiom.2019.102621","journal-title":"EBioMedicine"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-020-00319-x","article-title":"Artificial intelligence manages congenital cataract with individualized prediction and telehealth computing","volume":"3","author":"Long E","year":"2020","unstructured":"Long E, Chen J, Wu X, et al.. Artificial intelligence manages congenital cataract with individualized prediction and telehealth computing. NPJ Digit Med. 2020, 3:112. 10.1038\/s41746-020-00319-x","journal-title":"NPJ Digit Med"}],"container-title":["Cureus"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.cureus.com\/articles\/221908-ocular-pathology-and-genetics-transformative-role-of-artificial-intelligence-ai-in-anterior-segment-diseases","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,29]],"date-time":"2024-02-29T11:55:57Z","timestamp":1709207757000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.cureus.com\/articles\/221908-ocular-pathology-and-genetics-transformative-role-of-artificial-intelligence-ai-in-anterior-segment-diseases"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,2,29]]},"references-count":46,"URL":"https:\/\/doi.org\/10.7759\/cureus.55216","relation":{},"ISSN":["2168-8184"],"issn-type":[{"value":"2168-8184","type":"print"}],"subject":[],"published":{"date-parts":[[2024,2,29]]}}}