{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,7]],"date-time":"2026-10-07T05:19:30Z","timestamp":1791350370309,"version":"4.3.1"},"reference-count":64,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2022,1,18]],"date-time":"2022-01-18T00:00:00Z","timestamp":1642464000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000925","name":"National Health and Medical Research Council","doi-asserted-by":"publisher","award":["APP1187763"],"award-info":[{"award-number":["APP1187763"]}],"id":[{"id":"10.13039\/501100000925","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000925","name":"National Health and Medical Research Council","doi-asserted-by":"publisher","award":["1138585"],"award-info":[{"award-number":["1138585"]}],"id":[{"id":"10.13039\/501100000925","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Lions Eye Donation Service","award":["1"],"award-info":[{"award-number":["1"]}]},{"name":"Angior Family Foundation","award":["1"],"award-info":[{"award-number":["1"]}]},{"name":"Perpetual Impact Philanthropy grant","award":["1"],"award-info":[{"award-number":["1"]}]},{"name":"Keratoconus Australia Funding","award":["1"],"award-info":[{"award-number":["1"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JCM"],"abstract":"<jats:p>(1) Background: The objective of this review was to synthesize available data on the use of machine learning to evaluate its accuracy (as determined by pooled sensitivity and specificity) in detecting keratoconus (KC), and measure reporting completeness of machine learning models in KC based on TRIPOD (the transparent reporting of multivariable prediction models for individual prognosis or diagnosis) statement. (2) Methods: Two independent reviewers searched the electronic databases for all potential articles on machine learning and KC published prior to 2021. The TRIPOD 29-item checklist was used to evaluate the adherence to reporting guidelines of the studies, and the adherence rate to each item was computed. We conducted a meta-analysis to determine the pooled sensitivity and specificity of machine learning models for detecting KC. (3) Results: Thirty-five studies were included in this review. Thirty studies evaluated machine learning models for detecting KC eyes from controls and 14 studies evaluated machine learning models for detecting early KC eyes from controls. The pooled sensitivity for detecting KC was 0.970 (95% CI 0.949\u20130.982), with a pooled specificity of 0.985 (95% CI 0.971\u20130.993), whereas the pooled sensitivity of detecting early KC was 0.882 (95% CI 0.822\u20130.923), with a pooled specificity of 0.947 (95% CI 0.914\u20130.967). Between 3% and 48% of TRIPOD items were adhered to in studies, and the average (median) adherence rate for a single TRIPOD item was 23% across all studies. (4) Conclusions: Application of machine learning model has the potential to make the diagnosis and monitoring of KC more efficient, resulting in reduced vision loss to the patients. This review provides current information on the machine learning models that have been developed for detecting KC and early KC. Presently, the machine learning models performed poorly in identifying early KC from control eyes and many of these research studies did not follow established reporting standards, thus resulting in the failure of these clinical translation of these machine learning models. We present possible approaches for future studies for improvement in studies related to both KC and early KC models to more efficiently and widely utilize machine learning models for diagnostic process.<\/jats:p>","DOI":"10.3390\/jcm11030478","type":"journal-article","created":{"date-parts":[[2022,1,18]],"date-time":"2022-01-18T08:09:20Z","timestamp":1642493360000},"page":"478","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":30,"title":["Accuracy of Machine Learning Assisted Detection of Keratoconus: A Systematic Review and Meta-Analysis"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7437-8845","authenticated-orcid":false,"given":"Ke","family":"Cao","sequence":"first","affiliation":[{"name":"Centre for Eye Research Australia, Melbourne, VIC 3002, Australia"},{"name":"Department of Surgery, Ophthalmology, The University of Melbourne, Melbourne, VIC 3002, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8661-1544","authenticated-orcid":false,"given":"Karin","family":"Verspoor","sequence":"additional","affiliation":[{"name":"School of Computing Technologies, RMIT University, Melbourne, VIC 3000, Australia"},{"name":"School of Computing and Information Systems, The University of Melbourne, Melbourne, VIC 3010, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1945-7209","authenticated-orcid":false,"given":"Srujana","family":"Sahebjada","sequence":"additional","affiliation":[{"name":"Centre for Eye Research Australia, Melbourne, VIC 3002, Australia"},{"name":"Department of Surgery, Ophthalmology, The University of Melbourne, Melbourne, VIC 3002, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1305-3502","authenticated-orcid":false,"given":"Paul N.","family":"Baird","sequence":"additional","affiliation":[{"name":"Department of Surgery, Ophthalmology, The University of Melbourne, Melbourne, VIC 3002, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,1,18]]},"reference":[{"key":"ref_1","first-page":"214","article-title":"Corneal blindness: A global perspective","volume":"79","author":"Whitcher","year":"2001","journal-title":"Bull. World Health Organ."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"150","DOI":"10.1016\/j.preteyeres.2018.05.002","article-title":"Pathogenesis of Keratoconus: The intriguing therapeutic potential of Prolactin-inducible protein","volume":"67","author":"Sharif","year":"2018","journal-title":"Prog. Retin. Eye Res."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1592","DOI":"10.1097\/ICO.0000000000002420","article-title":"Keratoconus in Children: A Literature Review","volume":"39","author":"Buzzonetti","year":"2020","journal-title":"Cornea"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"R\u00f6ck, T., Bartz-Schmidt, K.U., and R\u00f6ck, D. (2018). Trends in corneal transplantation at the University Eye Hospital in T\u00fcbingen, Germany over the last 12 years: 2004\u20132015. PLoS ONE, 13.","DOI":"10.1371\/journal.pone.0198793"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"691","DOI":"10.1001\/archophthalmol.2011.7","article-title":"Corneal transplantation for keratoconus: A registry study","volume":"129","author":"Kelly","year":"2011","journal-title":"Arch. Ophthalmol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1193","DOI":"10.1109\/JBHI.2015.2450362","article-title":"Big data for health","volume":"19","author":"Poon","year":"2015","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"14","DOI":"10.15265\/IY-2014-0020","article-title":"Big Data in Medicine is Driving Big Changes","volume":"23","author":"Verspoor","year":"2014","journal-title":"Yearb. Med. Inform."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1080\/13645706.2019.1575882","article-title":"Introduction to artificial intelligence in medicine","volume":"28","author":"Mintz","year":"2019","journal-title":"Minim. Invasive Ther. Allied Technol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1347","DOI":"10.1056\/NEJMra1814259","article-title":"Machine learning in medicine","volume":"380","author":"Rajkomar","year":"2019","journal-title":"N. Engl. J. Med."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1586\/17469899.2015.1044979","article-title":"Topography and tomography in the diagnosis of corneal ectasia","volume":"10","author":"Ramamurthy","year":"2015","journal-title":"Expert Rev. Ophthalmol."},{"key":"ref_11","first-page":"177","article-title":"Pentacam\u00ae Corneal Tomography for Screening of Refractive Surgery Candidates: A Review of the Literature, Part I","volume":"8","author":"Motlagh","year":"2019","journal-title":"Med. Hypothesis Discov. Innov. Ophthalmol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"870","DOI":"10.1001\/archopht.1995.01100070044023","article-title":"Comparison of Methods for Detecting Keratoconus Using Videokeratography","volume":"113","author":"Maeda","year":"1995","journal-title":"Arch. Ophthalmol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"982","DOI":"10.1016\/j.jclinepi.2005.02.022","article-title":"Bivariate analysis of sensitivity and specificity produces informative summary measures in diagnostic reviews","volume":"58","author":"Reitsma","year":"2005","journal-title":"J. Clin. Epidemiol."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"van Enst, W.A., Ochodo, E., Scholten, R.J., Hooft, L., and Leeflang, M.M. (2014). Investigation of publication bias in meta-analyses of diagnostic test accuracy: A meta-epidemiological study. BMC Med. Res. Methodol., 14.","DOI":"10.1186\/1471-2288-14-70"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"882","DOI":"10.1016\/j.jclinepi.2005.01.016","article-title":"The performance of tests of publication bias and other sample size effects in systematic reviews of diagnostic test accuracy was assessed","volume":"58","author":"Deeks","year":"2005","journal-title":"J. Clin. Epidemiol."},{"key":"ref_16","first-page":"164","article-title":"Automated topographic screening for keratoconus in refractive surgery candidates","volume":"22","author":"Kalin","year":"1996","journal-title":"CLAO J. Off. Publ. Contact Lens Assoc. Ophthalmol. Inc."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"196","DOI":"10.1016\/S0886-3350(98)80200-9","article-title":"Accuracy of ultrasonic pachymetry and videokeratography in detecting keratoconus","volume":"24","author":"Rabinowitz","year":"1998","journal-title":"J. Cataract. Refract. Surg."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1038","DOI":"10.1097\/01.opx.0000192350.01045.6f","article-title":"Automated decision tree classification of corneal shape","volume":"82","author":"Twa","year":"2005","journal-title":"Optom. Vis. Sci. Off. Publ. Am. Acad. Optom."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"409","DOI":"10.1007\/s10384-006-0349-6","article-title":"Automated keratoconus defection using height data of anterior and posterior corneal surfaces","volume":"50","author":"Bessho","year":"2006","journal-title":"Jpn. J. Ophthalmol."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"5546","DOI":"10.1167\/iovs.10-5369","article-title":"Topographic and Tomographic Properties of Forme Fruste Keratoconus Corneas","volume":"51","author":"Saad","year":"2010","journal-title":"Investig. Opthalmol. Vis. Sci."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1016\/j.ajo.2013.03.034","article-title":"Detection of Subclinical Keratoconus Using an Automated Decision Tree Classification","volume":"156","author":"Smadja","year":"2013","journal-title":"Am. J. Ophthalmol."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1102","DOI":"10.1016\/j.ajo.2013.07.018","article-title":"Expanding the Cone Location and Magnitude Index to Include Corneal Thickness and Posterior Surface Information for the Detection of Keratoconus","volume":"156","author":"Mahmoud","year":"2013","journal-title":"Am. J. Ophthalmol."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"542","DOI":"10.3928\/1081597X-20140711-07","article-title":"Corneal Enantiomorphism in Normal and Keratoconic Eyes","volume":"30","author":"Saad","year":"2014","journal-title":"J. Refract. Surg."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1580","DOI":"10.1167\/iovs.13-12578","article-title":"Epithelial Remodeling as Basis for Machine-Based Identification of Keratoconus","volume":"55","author":"Silverman","year":"2014","journal-title":"Investig. Opthalmol. Vis. Sci."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"170","DOI":"10.1016\/j.compbiomed.2015.09.007","article-title":"Quantitative assessment of corneal vibrations during intraocular pressure measurement with the air-puff method in patients with keratocdnus","volume":"66","author":"Koprowski","year":"2015","journal-title":"Comput. Biol. Med."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"453","DOI":"10.1016\/j.ajo.2015.06.001","article-title":"A Novel Zernike Application to Differentiate between Three-dimensional Corneal Thickness of Normal Corneas and Corneas with Keratoconus","volume":"160","author":"Shetty","year":"2015","journal-title":"Am. J. Ophthalmol."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"275","DOI":"10.1016\/j.jcrs.2015.09.020","article-title":"Accuracy of machine learning classifiers using bilateral data from a Scheimpflug camera for identifying eyes with preclinical signs of keratoconus","volume":"42","author":"Dienes","year":"2016","journal-title":"J. Cataract. Refract. Surg."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"827","DOI":"10.1097\/ICO.0000000000000834","article-title":"Evaluation of a Machine-Learning Classifier for Keratoconus Detection Based on Scheimpflug Tomography","volume":"35","author":"Hidalgo","year":"2016","journal-title":"Cornea"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"434","DOI":"10.3928\/1081597X-20170426-02","article-title":"Integration of Scheimpflug-Based Corneal Tomography and Biomechanical Assessments for Enhancing Ectasia Detection","volume":"33","author":"Lopes","year":"2017","journal-title":"J. Refract. Surg."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"129","DOI":"10.5301\/ejo.5000850","article-title":"Combined tomography and epithelial thickness mapping for diagnosis of keratoconus","volume":"27","author":"Silverman","year":"2017","journal-title":"Eur. J. Ophthalmol."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"223","DOI":"10.1016\/j.ajo.2018.08.005","article-title":"Enhanced Tomographic Assessment to Detect Corneal Ectasia Based on Artificial Intelligence","volume":"195","author":"Lopes","year":"2018","journal-title":"Am. J. Ophthalmol."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"e201900126","DOI":"10.1002\/jbio.201900126","article-title":"Bowman\u2019s topography for improved detection of early ectasia","volume":"12","author":"Chandapura","year":"2019","journal-title":"J. Biophotonics"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1016\/j.compbiomed.2019.04.024","article-title":"Computer aided diagnosis for suspect keratoconus detection","volume":"109","author":"Issarti","year":"2019","journal-title":"Comput. Biol. Med."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"263","DOI":"10.1016\/j.compbiomed.2019.04.019","article-title":"Corneal deformation amplitude analysis for keratoconus detection through compensation for intraocular pressure and integration with horizontal thickness profile","volume":"109","author":"Ren","year":"2019","journal-title":"Comput. Biol. Med."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Bolar\u00edn, J.M., Cavas, F., Vel\u00e1zquez, J., and Ali\u00f3, J. (2020). A Machine-Learning Model Based on Morphogeometric Parameters for RETICS Disease Classification and GUI Development. Appl. Sci., 10.","DOI":"10.3390\/app10051874"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"366","DOI":"10.1016\/j.clae.2019.12.006","article-title":"Robust keratoconus detection with Bayesian network classifier for Placido-based corneal indices","volume":"43","year":"2020","journal-title":"Contact Lens Anterior Eye"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"103809","DOI":"10.1016\/j.compbiomed.2020.103809","article-title":"Logistic index for keratoconus detection and severity scoring (Logik)","volume":"122","author":"Issarti","year":"2020","journal-title":"Comput. Biol. Med."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"149113","DOI":"10.1109\/ACCESS.2020.3016060","article-title":"Detecting Keratoconus from Corneal Imaging Data Using Machine Learning","volume":"8","author":"Lavric","year":"2020","journal-title":"IEEE Access"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1186\/s40662-020-00213-3","article-title":"Machine learning helps improve diagnostic ability of subclinical keratoconus using Scheimpflug and OCT imaging modalities","volume":"7","author":"Shi","year":"2020","journal-title":"Eye Vis."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1167\/tvst.9.2.30","article-title":"EMKLAS: A New Automatic Scoring System for Early and Mild Keratoconus Detection","volume":"9","year":"2020","journal-title":"Transl. Vis. Sci. Technol."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"622","DOI":"10.1364\/BOE.10.000622","article-title":"CorneaNet: Fast segmentation of cornea OCT scans of healthy and keratoconic eyes using deep learning","volume":"10","author":"Schmetterer","year":"2019","journal-title":"Biomed. Opt. Express"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"e031313","DOI":"10.1136\/bmjopen-2019-031313","article-title":"Keratoconus detection using deep learning of colour-coded maps with anterior segment optical coherence tomography: A diagnostic accuracy study","volume":"9","author":"Kamiya","year":"2019","journal-title":"BMJ Open"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"8162567","DOI":"10.1155\/2019\/8162567","article-title":"KeratoDetect: Keratoconus Detection Algorithm Using Convolutional Neural Networks","volume":"2019","author":"Lavric","year":"2019","journal-title":"Comput. Intell. Neurosci."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1167\/tvst.9.2.53","article-title":"Keratoconus Screening Based on Deep Learning Approach of Corneal Topography","volume":"9","author":"Kuo","year":"2020","journal-title":"Transl. Vis. Sci. Technol."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"637","DOI":"10.3928\/1081597X-20130819-05","article-title":"Analysis of Waveform-Derived ORA Parameters in Early Forms of Keratoconus and Normal Corneas","volume":"29","author":"Ventura","year":"2013","journal-title":"J. Refract. Surg."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"996","DOI":"10.1097\/ICO.0000000000000529","article-title":"Validation of an Objective Scoring System for Forme Fruste Keratoconus Detection and Post-LASIK Ectasia Risk Assessment in Asian Eyes","volume":"34","author":"Chan","year":"2015","journal-title":"Cornea"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"16486","DOI":"10.1038\/s41598-017-16568-y","article-title":"Characteristic of entire corneal topography and tomography for the detection of sub-clinical keratoconus with Zernike polynomials using Pentacam","volume":"7","author":"Xu","year":"2017","journal-title":"Sci. Rep."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1167\/tvst.9.2.24","article-title":"Evaluating the Performance of Various Machine Learning Algorithms to Detect Subclinical Keratoconus","volume":"9","author":"Cao","year":"2020","journal-title":"Transl. Vis. Sci. Technol."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Yousefi, S., Yousefi, E., Takahashi, H., Hayashi, T., Tampo, H., Inoda, S., Arai, Y., and Asbell, P. (2018). Keratoconus severity identification using unsupervised machine learning. PLoS ONE, 13.","DOI":"10.1371\/journal.pone.0205998"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"270","DOI":"10.3928\/1081597X-20200212-03","article-title":"A systematic review of subclinical keratoconus and forme fruste keratoconus","volume":"36","author":"Henriquez","year":"2020","journal-title":"J. Refract. Surg."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"1","DOI":"10.5005\/jp-journals-10025-1095","article-title":"Keratoconus management guidelines","volume":"4","author":"Alio","year":"2015","journal-title":"Int. J. Keratoconus Ectatic Corneal Dis."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"359","DOI":"10.1097\/ICO.0000000000000408","article-title":"Global consensus on keratoconus and ectatic diseases","volume":"34","author":"Gomes","year":"2015","journal-title":"Corneal"},{"key":"ref_53","unstructured":"Pentacam User Guide (2021, June 08). System for Measuring and Analysing the Front Part of the Eye. User Guide Pentacam\u00ae\/Pentacam\u00aeHR\/Pentacam\u00aeAXL. Available online: https:\/\/www.pentacam.com\/fileadmin\/user_upload\/pentacam.de\/downloads\/interpretations-leitfaden\/interpretation_guideline_3rd_edition_0915.pdf2018."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"110","DOI":"10.1111\/j.1444-0938.2008.00336.x","article-title":"Reliability and repeatability of the Pentacam on corneal curvatures","volume":"92","author":"Chen","year":"2009","journal-title":"Clin. Exp. Optom."},{"key":"ref_55","unstructured":"Michael, W., and Belin, M. (2021, June 08). Point\/Counterpoint: The Pentacam Versus the Orbscan. Available online: https:\/\/crstoday.com\/."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Shanthi, S., Aruljyothi, L., Balasundaram, M.B., Janakiraman, A., Nirmala, D.K., and Pyingkodi, M. (2021). Artificial intelligence applications in different imaging modalities for corneal topography. Surv. Ophthalmol.","DOI":"10.1016\/j.survophthal.2021.08.004"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"m1328","DOI":"10.1136\/bmj.m1328","article-title":"Prediction models for diagnosis and prognosis of covid-19 infection: Systematic review and critical appraisal","volume":"369","author":"Wynants","year":"2020","journal-title":"BMJ"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1186\/s40662-018-0107-0","article-title":"Optical coherence tomography for ocular surface and corneal diseases: A review","volume":"5","author":"Venkateswaran","year":"2018","journal-title":"Eye Vis."},{"key":"ref_59","unstructured":"Hall, M.A. (1999). Correlation-Based Feature Selection for Machine Learning, Department of Computer Science University of Waikato."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"BIO141","DOI":"10.1167\/iovs.17-21789","article-title":"Machine Learning of the Progression of Intermediate Age-Related Macular Degeneration Based on OCT Imaging","volume":"58","author":"Montuoro","year":"2017","journal-title":"Investig. Opthalmol. Vis. Sci."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1038\/s41746-019-0172-3","article-title":"Deep learning algorithm predicts diabetic retinopathy progression in individual patients","volume":"2","author":"Arcadu","year":"2019","journal-title":"NPJ Digit. Med."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"365","DOI":"10.1167\/iovs.18-25568","article-title":"An Artificial Intelligence Approach to Detect Visual Field Progression in Glaucoma Based on Spatial Pattern Analysis","volume":"60","author":"Wang","year":"2019","journal-title":"Investig. Opthalmol. Vis. Sci."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1167\/tvst.9.2.25","article-title":"Artificial Intelligence to Stratify Severity of Age-Related Macular Degeneration (AMD) and Predict Risk of Progression to Late AMD","volume":"9","author":"Bhuiyan","year":"2020","journal-title":"Transl. Vis. Sci. Technol."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"3684","DOI":"10.1167\/iovs.04-1168","article-title":"Unsupervised machine learning with independent component analysis to identify areas of progression in glaucomatous visual fields","volume":"46","author":"Sample","year":"2005","journal-title":"Investig. Opthalmol. Vis. Sci."}],"container-title":["Journal of Clinical Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2077-0383\/11\/3\/478\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:03:11Z","timestamp":1760133791000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2077-0383\/11\/3\/478"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,1,18]]},"references-count":64,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2022,2]]}},"alternative-id":["jcm11030478"],"URL":"https:\/\/doi.org\/10.3390\/jcm11030478","relation":{},"ISSN":["2077-0383"],"issn-type":[{"value":"2077-0383","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,1,18]]}}}