{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T04:50:20Z","timestamp":1787028620182,"version":"build-2736575974"},"reference-count":36,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2021,9,21]],"date-time":"2021-09-21T00:00:00Z","timestamp":1632182400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"CONSEJER\u00cdA DE SALUD Y FAMILIAS. JUNTA DE ANDALUC\u00cdA. PROYECTOS DE INNOVACI\u00d3N EN SALUD 2017","award":["PIN-0530-2017 \"."],"award-info":[{"award-number":["PIN-0530-2017 \"."]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JCM"],"abstract":"<jats:p>(1) Background: Keratoconus is a non-inflammatory corneal disease characterized by gradual thinning of the stroma, resulting in irreversible visual quality and quantity decline. Early detection of keratoconus and subsequent prevention of possible risks are crucial factors in its progression. Random forest is a machine learning technique for classification based on the construction of thousands of decision trees. The aim of this study was to use the random forest technique in the classification and prediction of subclinical keratoconus, considering the metrics proposed by Pentacam and Corvis. (2) Methods: The design was a retrospective cross-sectional study. A total of 81 eyes of 81 patients were enrolled: sixty-one eyes with healthy corneas and twenty patients with subclinical keratoconus (SCKC): This initial stage includes patients with the following conditions: (1) minor topographic signs of keratoconus and suspicious topographic findings (mild asymmetric bow tie, with or without deviation; (2) average K (mean corneal curvature) &lt; 46, 5 D; (3) minimum corneal thickness (ECM) &gt; 490 \u03bcm; (4) no slit lamp found; and (5) contralateral clinical keratoconus of the eye. Pentacam topographic and Corvis biomechanical variables were collected. Decision tree and random forest were used as machine learning techniques for classifications. Random forest performed a ranking of the most critical variables in classification. (3) Results: The essential variable was SP A1 (stiffness parameter A1), followed by A2 time, posterior coma 0\u00b0, A2 velocity and peak distance. The model efficiently predicted all patients with subclinical keratoconus (Sp = 93%) and was also a good model for classifying healthy cases (Sen = 86%). The overall accuracy rate of the model was 89%. (4) Conclusions: The random forest model was a good model for classifying subclinical keratoconus. The SP A1 variable was the most critical determinant in classifying and identifying subclinical keratoconus, followed by A2 time.<\/jats:p>","DOI":"10.3390\/jcm10184281","type":"journal-article","created":{"date-parts":[[2021,9,21]],"date-time":"2021-09-21T22:34:01Z","timestamp":1632263641000},"page":"4281","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":29,"title":["Diagnosis of Subclinical Keratoconus Based on Machine Learning Techniques"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6928-9728","authenticated-orcid":false,"given":"Gracia","family":"Castro-Luna","sequence":"first","affiliation":[{"name":"Department of Nursing, Physiotherapy and Medicine, University of Almer\u00eda, 04120 Almer\u00eda, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1169-0428","authenticated-orcid":false,"given":"Diana","family":"Jim\u00e9nez-Rodr\u00edguez","sequence":"additional","affiliation":[{"name":"Department of Nursing, Physiotherapy and Medicine, University of Almer\u00eda, 04120 Almer\u00eda, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0846-6185","authenticated-orcid":false,"given":"Ana Bel\u00e9n","family":"Casta\u00f1o-Fern\u00e1ndez","sequence":"additional","affiliation":[{"name":"Department of Mathematics, University of Almer\u00eda, 04120 Almer\u00eda, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2104-2907","authenticated-orcid":false,"given":"Antonio","family":"P\u00e9rez-Rueda","sequence":"additional","affiliation":[{"name":"Department of Cornea, Hospital of Torrecardenas, 04120 Almer\u00eda, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,9,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"293","DOI":"10.1016\/0039-6257(84)90094-8","article-title":"Keratoconus and related non-inflammatory corneal thinning disorders","volume":"28","author":"Krachmer","year":"1984","journal-title":"Surv. Ophthalmol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"753","DOI":"10.3928\/1081597X-20121011-01","article-title":"Progression of keratoconus and efficacy of pediatric corneal collagen cross-linking in children and adolescents","volume":"28","author":"Chatzis","year":"2012","journal-title":"J. Refract. Surg."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1097\/00004397-199303320-00023","article-title":"Is keratoconus genetic?","volume":"33","author":"Jacobs","year":"1993","journal-title":"Int. Ophthalmol. Clin."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1111\/j.1442-9071.2004.00805.x","article-title":"Morphological changes in keratoconus: Pathology or pathogenesis","volume":"32","author":"Sherwin","year":"2004","journal-title":"Clin. Exp. Ophthalmol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"935","DOI":"10.1016\/j.ophtha.2019.02.029","article-title":"Keratoconus natural progression: A systematic review and meta-analysis of11 529 eyes","volume":"126","author":"Ferdi","year":"2019","journal-title":"Ophthalmology"},{"key":"ref_6","first-page":"2749","article-title":"Automated keratoconus screening with corneal topography analysis","volume":"35","author":"Maeda","year":"1994","journal-title":"Investig. Ophthalmol. Vis. Sci."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"975","DOI":"10.1016\/S0161-6420(93)31531-9","article-title":"Detection of keratoconus before keratorefractive surgery","volume":"100","author":"Hustead","year":"1993","journal-title":"Ophthalmology"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"400","DOI":"10.3928\/1081-597X-19891101-10","article-title":"Computer-assisted corneal topography in keratoconus","volume":"5","author":"Rabinowitz","year":"1989","journal-title":"Refract. Corneal Surg."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"427","DOI":"10.3928\/1081-597X-19980701-09","article-title":"A quantitative corneal topography index for detection of keratoconus","volume":"14","author":"Dastjerdi","year":"1998","journal-title":"J. Refract. Surg."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"S958","DOI":"10.3928\/1081597X-20090915-11","article-title":"Keratoconus detection using corneal topography","volume":"25","author":"Holladay","year":"2009","journal-title":"J. Refract. Surg."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1136\/bjophthalmol-2018-313173","article-title":"Artificial intelligence and deep learning in ophthalmology","volume":"103","author":"Ting","year":"2019","journal-title":"Br. J. Ophthalmol."},{"key":"ref_12","first-page":"18","article-title":"Artificial intelligence in diabetic eye dis-ease screening","volume":"8","author":"Cheung","year":"2019","journal-title":"Asia Pac. J. Ophthalmol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1097\/ICU.0000000000000552","article-title":"Artificial intelligence in glaucoma","volume":"30","author":"Zheng","year":"2019","journal-title":"Curr. Opin. Ophthalmol."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"3199","DOI":"10.1167\/iovs.18-24106","article-title":"Prediction of Individual Disease Conversion in Early AMD Using Artificial Intelligence","volume":"59","author":"Waldstein","year":"2018","journal-title":"Investig. Ophthalmol Vis. Sci."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1097\/ICU.0000000000000593","article-title":"Artificial intelligence for pediatric ophthalmology","volume":"30","author":"Reid","year":"2019","journal-title":"Curr. Opin. Ophthalmol."},{"key":"ref_16","first-page":"2290","article-title":"Current keratoconus detection methods compared with a neural network approach","volume":"38","author":"Smolek","year":"1997","journal-title":"Investig. Ophthalmol. Vis. Sci."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1016\/S1532-0464(02)00513-0","article-title":"Neural network-based system for early keratoconus detection from corneal topography","volume":"35","author":"Accardo","year":"2002","journal-title":"J. Biomed. Inform."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1862","DOI":"10.1016\/j.ophtha.2018.06.020","article-title":"Distinguishing highly asymmetric keratoconus eyes using combined scheimpflug and spectral-domain OCT analysis","volume":"125","author":"Hwang","year":"2018","journal-title":"Ophthalmology"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"P\u00e9rez-Rueda, A., Jim\u00e9nez-Rodr\u00edguez, D., and Castro-Luna, G. (2021). Diagnosis of Subclinical Keratoconus with a Combined Model of Biomechanical and Topographic Parameters. J. Clin. Med., 10.","DOI":"10.3390\/jcm10132746"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Castro-Luna, G., and P\u00e9rez-Rueda, A. (2020). A predictive model for early diagnosis of keratoconus. BMC Ophthalmol., 20.","DOI":"10.1186\/s12886-020-01531-9"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"2231","DOI":"10.1016\/j.ophtha.2012.06.005","article-title":"Use of a support vector machine for keratoconus and subclinical keratoconus detection by topographic and tomographic data","volume":"119","author":"Arbelaez","year":"2012","journal-title":"Ophthalmology"},{"key":"ref_22","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_23","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_24","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_25","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":"Cont. Lens Anterior Eye"},{"key":"ref_26","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_27","doi-asserted-by":"crossref","first-page":"519","DOI":"10.1001\/jamaophthalmol.2020.0507","article-title":"Screening Candidates for Refractive Surgery with Corneal Tomographic-Based Deep Learning","volume":"138","author":"Xie","year":"2020","journal-title":"JAMA Ophthalmol."},{"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":"Rodriguez","year":"2016","journal-title":"Cornea"},{"key":"ref_29","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_30","doi-asserted-by":"crossref","first-page":"317","DOI":"10.1080\/08820538.2019.1620812","article-title":"A Review of Machine Learning Techniques for Keratoconus Detection and Refractive Surgery Screening","volume":"34","author":"Lin","year":"2019","journal-title":"Semin. Ophthalmol."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"158","DOI":"10.1136\/bjophthalmol-2019-315651","article-title":"Artificial intelligence for anterior segment diseases: Emerging applications in ophthalmology","volume":"105","author":"Ting","year":"2021","journal-title":"Br. J. Ophthalmol."},{"key":"ref_32","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_33","doi-asserted-by":"crossref","unstructured":"Peris-Mart\u00ednez, C., D\u00edez-Ajenjo, M.A., Garc\u00eda-Domene, M.C., Pinazo-Dur\u00e1n, M.D., Luque-Cobija, M.J., Del Buey-Sayas, M.\u00c1., and Or-t\u00ed-Navarro, S. (2021). Evaluation of Intraocular Pressure and Other Biomechanical Parameters to Distinguish between Subclinical Keratoconus and Healthy Corneas. J. Clin. Med., 10.","DOI":"10.3390\/jcm10091905"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"4962","DOI":"10.1038\/s41598-021-84370-y","article-title":"Accuracy of new Corvis ST parameters for detecting subclinical and clinical keratoconus eyes in a Chinese population","volume":"11","author":"Ren","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"266","DOI":"10.3928\/1081597X-20161221-03","article-title":"Introduction of Two Novel Stiffness Parameters and Interpretation of Air Puff-Induced Biomechanical Deformation Parameters With a Dynamic Scheimpflug Analyzer","volume":"33","author":"Roberts","year":"2017","journal-title":"J. Refract. Surg."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"740","DOI":"10.3389\/fphys.2019.00740","article-title":"Relationship Among Corneal Stiffness, Thickness, and Biomechanical Parameters Measured by Corvis ST, Pentacam and ORA in Keratoconus","volume":"10","author":"Zhao","year":"2019","journal-title":"Front. Physiol."}],"container-title":["Journal of Clinical Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2077-0383\/10\/18\/4281\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:02:53Z","timestamp":1760166173000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2077-0383\/10\/18\/4281"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,9,21]]},"references-count":36,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2021,9]]}},"alternative-id":["jcm10184281"],"URL":"https:\/\/doi.org\/10.3390\/jcm10184281","relation":{},"ISSN":["2077-0383"],"issn-type":[{"value":"2077-0383","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,9,21]]}}}