{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,31]],"date-time":"2026-10-31T00:42:10Z","timestamp":1793407330545,"version":"4.5.0"},"reference-count":46,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2024,4,26]],"date-time":"2024-04-26T00:00:00Z","timestamp":1714089600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82001924"],"award-info":[{"award-number":["82001924"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["LY22H180005"],"award-info":[{"award-number":["LY22H180005"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Zhejiang Provincial Natural Science Foundation of China","award":["82001924"],"award-info":[{"award-number":["82001924"]}]},{"name":"Zhejiang Provincial Natural Science Foundation of China","award":["LY22H180005"],"award-info":[{"award-number":["LY22H180005"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Bioengineering"],"abstract":"<jats:p>Purpose: This study aimed to employ the incremental digital image correlation (DIC) method to obtain displacement and strain field data of the cornea from Corvis ST (CVS) sequences and access the performance of embedding these biomechanical data with machine learning models to distinguish forme fruste keratoconus (FFKC) from normal corneas. Methods: 100 subjects were categorized into normal (N = 50) and FFKC (N = 50) groups. Image sequences depicting the horizontal cross-section of the human cornea under air puff were captured using the Corvis ST tonometer. The high-speed evolution of full-field corneal displacement, strain, velocity, and strain rate was reconstructed utilizing the incremental DIC approach. Maximum (max-) and average (ave-) values of full-field displacement V, shear strain \u03b3xy, velocity VR, and shear strain rate \u03b3xyR were determined over time, generating eight evolution curves denoting max-V, max-\u03b3xy, max-VR, max-\u03b3xyR, ave-V, ave-\u03b3xy, ave-VR, and ave-\u03b3xyR, respectively. These evolution data were inputted into two machine learning (ML) models, specifically Na\u00efve Bayes (NB) and Random Forest (RF) models, which were subsequently employed to construct a voting classifier. The performance of the models in diagnosing FFKC from normal corneas was compared to existing CVS parameters. Results: The Normal group and the FFKC group each included 50 eyes. The FFKC group did not differ from healthy controls for age (p = 0.26) and gender (p = 0.36) at baseline, but they had significantly lower bIOP (p &lt; 0.001) and thinner central cornea thickness (CCT) (p &lt; 0.001). The results demonstrated that the proposed voting ensemble model yielded the highest performance with an AUC of 1.00, followed by the RF model with an AUC of 0.99. Radius and A2 Time emerged as the best-performing CVS parameters with AUC values of 0.948 and 0.938, respectively. Nonetheless, no existing Corvis ST parameters outperformed the ML models. A progressive enhancement in performance of the ML models was observed with incremental time points during the corneal deformation. Conclusion: This study represents the first instance where displacement and strain data following incremental DIC analysis of Corvis ST images were integrated with machine learning models to effectively differentiate FFKC corneas from normal ones, achieving superior accuracy compared to existing CVS parameters. Considering biomechanical responses of the inner cornea and their temporal pattern changes may significantly improve the early detection of keratoconus.<\/jats:p>","DOI":"10.3390\/bioengineering11050429","type":"journal-article","created":{"date-parts":[[2024,4,26]],"date-time":"2024-04-26T10:56:32Z","timestamp":1714128992000},"page":"429","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Diagnosis of Forme Fruste Keratoconus Using Corvis ST Sequences with Digital Image Correlation and Machine Learning"],"prefix":"10.3390","volume":"11","author":[{"given":"Lanting","family":"Yang","sequence":"first","affiliation":[{"name":"National Engineering Research Center of Ophthalmology and Optometry, Eye Hospital, Wenzhou Medical University, Wenzhou 325027, China"},{"name":"State Key Laboratory of Ophthalmology, Optometry and Visual Science, Eye Hospital, Wenzhou Medical University, Wenzhou 325027, 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Ophthalmology, Optometry and Visual Science, Eye Hospital, Wenzhou Medical University, Wenzhou 325027, China"},{"name":"National Clinical Research Center for Ocular Diseases, Eye Hospital, Wenzhou Medical University, Wenzhou 325027, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yun","family":"Jin","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Structural Analysis, Optimization and CAE Software for Industrial Equipment, Department of Engineering Mechanics, Dalian University of Technology, Dalian 116023, China"},{"name":"International Research Center for Computational Mechanics, Dalian University of Technology, Dalian 116023, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haochen","family":"Ci","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Structural Analysis, Optimization and CAE Software for Industrial Equipment, Department of Engineering Mechanics, Dalian University of Technology, 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review","volume":"45","author":"Carracedo","year":"2022","journal-title":"Contact Lens Anterior Eye"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"293","DOI":"10.1016\/0039-6257(84)90094-8","article-title":"Keratoconus and related noninflammatory corneal thinning disorders","volume":"28","author":"Krachmer","year":"1984","journal-title":"Surv. Ophthalmol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"905","DOI":"10.1016\/j.ajo.2008.06.034","article-title":"Comparison of Three Different Techniques of Corneal Transplantation for Keratoconus","volume":"146","author":"Bahar","year":"2008","journal-title":"Am. J. Ophthalmol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1111\/j.1442-9071.1995.tb00141.x","article-title":"Clinical detection of unilateral keratoconus","volume":"23","author":"Lee","year":"1995","journal-title":"Aust. N. Z. J. Ophthalmol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1016\/S0161-6420(93)31673-8","article-title":"Videokeratography of the Fellow Eye in Unilateral Keratoconus","volume":"100","author":"Rabinowitz","year":"1993","journal-title":"Ophthalmology"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"109","DOI":"10.4103\/0301-4738.179725","article-title":"Detection of the early keratoconus based on corneal biomechanical properties in the refractive surgery candidates","volume":"64","author":"Pniakowska","year":"2016","journal-title":"Indian J. Ophthalmol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1465","DOI":"10.1136\/bjophthalmol-2014-305120","article-title":"Early diagnosis of keratoconus: What difference is it making?","volume":"98","year":"2014","journal-title":"Br. J. Ophthalmol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"140","DOI":"10.1016\/j.ajo.2017.06.031","article-title":"Keratoconus Screening Indices and Their Diagnostic Ability to Distinguish Normal From Ectatic Corneas","volume":"181","author":"Shetty","year":"2017","journal-title":"Am. J. Ophthalmol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"5915","DOI":"10.1038\/s41467-021-26216-9","article-title":"Annotation-efficient deep learning for automatic medical image segmentation","volume":"12","author":"Wang","year":"2021","journal-title":"Nat. Commun."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"351","DOI":"10.1038\/s42256-023-00633-5","article-title":"Multimodal data fusion for cancer biomarker discovery with deep learning","volume":"5","author":"Steyaert","year":"2023","journal-title":"Nat. Mach. Intell."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1113","DOI":"10.1038\/s41591-023-02332-5","article-title":"A deep learning algorithm to predict risk of pancreatic cancer from disease trajectories","volume":"29","author":"Placido","year":"2023","journal-title":"Nat. Med."},{"key":"ref_12","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_13","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_14","doi-asserted-by":"crossref","first-page":"126","DOI":"10.1016\/j.ajo.2022.12.016","article-title":"Optimized Artificial Intelligence for Enhanced Ectasia Detection Using Scheimpflug-Based Corneal Tomography and Biomechanical Data","volume":"251","author":"Machado","year":"2023","journal-title":"Am. J. Ophthalmol."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1167\/tvst.11.9.32","article-title":"Artificial Intelligence\u2013Based Diagnostic Model for Detecting Keratoconus Using Videos of Corneal Force Deformation","volume":"11","author":"Tan","year":"2022","journal-title":"Transl. Vis. Sci. Technol."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"374","DOI":"10.3928\/1081597X-20220414-02","article-title":"Combining Spectral-Domain OCT and Air-Puff Tonometry Analysis to Diagnose Keratoconus","volume":"38","author":"Lu","year":"2022","journal-title":"J. Refract. Surg."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1097\/IIO.0000000000000172","article-title":"Biomechanical diagnostics of the cornea","volume":"57","author":"Dupps","year":"2017","journal-title":"Int. Ophthalmol. Clin."},{"key":"ref_18","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\u2013induced biomechanical deformation parameters with a dynamic Scheimpflug analyzer","volume":"33","author":"Roberts","year":"2017","journal-title":"J. Refract. Surg."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"803","DOI":"10.3928\/1081597X-20160629-01","article-title":"Detection of keratoconus with a new biomechanical index","volume":"32","author":"Vinciguerra","year":"2016","journal-title":"J. Refract. Surg."},{"key":"ref_20","first-page":"24","article-title":"Corneal parameters in healthy subjects assessed by Corvis ST","volume":"15","author":"Salouti","year":"2020","journal-title":"J. Ophthalmic Vis. Res."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"808","DOI":"10.3928\/1081-597X-20071001-11","article-title":"Determination of the modulus of elasticity of the human cornea","volume":"23","author":"Elsheikh","year":"2007","journal-title":"J. Refract. Surg."},{"key":"ref_22","first-page":"25","article-title":"Strain-rate sensitivity of porcine and ovine corneas","volume":"13","author":"Vol","year":"2011","journal-title":"Acta Bioeng. Biomech."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Maczynska, E., Karnowski, K., Szulzycki, K., Malinowska, M., Dolezyczek, H., Cichanski, A., Wojtkowski, M., Kaluzny, B., and Grulkowski, I. (2019). Assessment of the influence of viscoelasticity of cornea in animal ex vivo model using air-puff optical coherence tomography and corneal hysteresis. J. Biophotonics, 12.","DOI":"10.1002\/jbio.201800154"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"454","DOI":"10.1002\/jbio.201500245","article-title":"Determining in vivo elasticity and viscosity with dynamic Scheimpflug imaging analysis in keratoconic and healthy eyes","volume":"463","author":"Wang","year":"2016","journal-title":"J. Biophotonics"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Wang, B., Yang, L., Cheng, J., Wang, J., and Mei, Y. (2023). In-vivo high-speed biomechanical imaging of the cornea using Corvis ST and digital image correlation. Comput. Biol. Med., 153.","DOI":"10.1016\/j.compbiomed.2023.106540"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"586","DOI":"10.1016\/j.optlaseng.2011.05.005","article-title":"Incremental calculation for large deformation measurement using reliability-guided digital image correlation","volume":"50","author":"Bing","year":"2012","journal-title":"Opt. Lasers Eng."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"5261","DOI":"10.1128\/AEM.00062-07","article-title":"Na\u00efve Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy","volume":"73","author":"Wang","year":"2007","journal-title":"Appl. Environ. Microbiol."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random Forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_29","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_30","doi-asserted-by":"crossref","unstructured":"Chandapura, R., Salom\u00e3o, M.Q., Ambr\u00f3sio, R., Swarup, R., Shetty, R., and Roy, A.S. (2019). Bowman\u2019s topography for improved detection of early ectasia. J. Biophotonics, 12.","DOI":"10.1002\/jbio.201900126"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"101840","DOI":"10.1016\/j.clae.2023.101840","article-title":"Combinations of Scheimpflug tomography, ocular coherence tomography and air-puff tonometry improve the detection of keratoconus","volume":"46","author":"Lu","year":"2023","journal-title":"Contact Lens Anterior Eye"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"991","DOI":"10.1016\/j.jcrs.2014.04.013","article-title":"Biomechanics of corneal ectasia and biomechanical treatments","volume":"40","author":"Roberts","year":"2014","journal-title":"J. Cataract. Refract. Surg."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"695697","DOI":"10.3389\/fmed.2021.695697","article-title":"Comparison of Corneal Biomechanics in Post-SMILE, Post-LASEK, and Keratoconic Eyes","volume":"8","author":"Shang","year":"2021","journal-title":"Front. Med."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1016\/j.exer.2018.05.019","article-title":"Axial mechanical and structural characterization of keratoconus corneas","volume":"175","author":"Mikula","year":"2018","journal-title":"Exp. Eye Res."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"258","DOI":"10.1111\/aos.12056","article-title":"Biomechanical diagnosis of keratoconus: Evaluation of the keratoconus match index and the keratoconus match probability","volume":"91","author":"Labiris","year":"2013","journal-title":"Acta Ophthalmol."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"182","DOI":"10.1016\/j.ajo.2023.04.002","article-title":"Detection of Keratoconus with a New Corvis Biomechanical Index Optimized for Chinese Populations","volume":"252","author":"Vinciguerra","year":"2023","journal-title":"Am. J. Ophthalmol."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1247","DOI":"10.1007\/s10792-020-01679-9","article-title":"Comprehensive evaluation of corneas from normal, forme fruste keratoconus and clinical keratoconus patients using morphological and biomechanical properties","volume":"41","author":"Zhang","year":"2021","journal-title":"Int. Ophthalmol."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"671","DOI":"10.1080\/08820538.2021.1896752","article-title":"Comparison of the morphological and biomechanical characteristics of keratoconus, forme fruste keratoconus, and normal corneas","volume":"36","author":"Guo","year":"2021","journal-title":"Semin. Ophthalmol."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1186\/s40662-021-00266-y","article-title":"Comparisons of corneal biomechanical and tomographic parameters among thin normal cornea, forme fruste keratoconus, and mild keratoconus","volume":"8","author":"Tian","year":"2021","journal-title":"Eye Vis."},{"key":"ref_40","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_41","doi-asserted-by":"crossref","unstructured":"Cao, K., Verspoor, K., Chan, E., Daniell, M., Sahebjada, S., and Baird, P.N. (2021). Machine learning with a reduced dimensionality representation of comprehensive Pentacam tomography parameters to identify subclinical keratoconus. Comput. Biol. Med., 138.","DOI":"10.1016\/j.compbiomed.2021.104884"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Castro-Luna, G., Jim\u00e9nez-Rodr\u00edguez, D., Casta\u00f1o-Fern\u00e1ndez, A.B., and P\u00e9rez-Rueda, A. (2021). Diagnosis of subclinical keratoconus based on machine learning techniques. J. Clin. Med., 10.","DOI":"10.3390\/jcm10184281"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"635","DOI":"10.1136\/bjophthalmol-2021-319309","article-title":"Universal architecture of corneal segmental tomography biomarkers for artificial intelligence-driven diagnosis of early keratoconus","volume":"107","author":"Kundu","year":"2023","journal-title":"Br. J. Ophthalmol."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Alturki, N., Umer, M., Ishaq, A., Abuzinadah, N., Alnowaiser, K., Mohamed, A., Saidani, O., and Ashraf, I. (2023). Combining CNN Features with Voting Classifiers for Optimizing Performance of Brain Tumor Classification. Cancers, 15.","DOI":"10.3390\/cancers15061767"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Umer, M., Naveed, M., Alrowais, F., Ishaq, A., Al Hejaili, A., Alsubai, S., Eshmawi, A.A., Mohamed, A., and Ashraf, I. (2022). Breast Cancer Detection Using Convoluted Features and Ensemble Machine Learning Algorithm. Cancers, 14.","DOI":"10.3390\/cancers14236015"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1038\/s41746-019-0135-8","article-title":"Adopting machine learning to automatically identify candidate patients for corneal refractive surgery","volume":"2","author":"Yoo","year":"2019","journal-title":"npj Digit. Med."}],"container-title":["Bioengineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2306-5354\/11\/5\/429\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:34:15Z","timestamp":1760106855000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2306-5354\/11\/5\/429"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4,26]]},"references-count":46,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2024,5]]}},"alternative-id":["bioengineering11050429"],"URL":"https:\/\/doi.org\/10.3390\/bioengineering11050429","relation":{},"ISSN":["2306-5354"],"issn-type":[{"value":"2306-5354","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,4,26]]}}}