{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,23]],"date-time":"2026-08-23T17:01:44Z","timestamp":1787504504078,"version":"build-2736575974"},"reference-count":31,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T00:00:00Z","timestamp":1782950400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Background\/Objectives: Keratoconus (KC) is a chronic disease that causes progressive corneal thinning and steepening, thereby negatively impacting visual acuity. Although corneal topography and keratometry are the primary measures to diagnose KC, access to these methods can be limited by various factors. To address these limitations, this study evaluates a novel low-cost deep-learning algorithm that infers keratometric categories from smartphone-assisted Placido ring photographs. Methods: Development utilized 1240 healthy control eye images and 188 K1-labeled KC images for pretraining, without using their K1 labels. A Variational Autoencoder with KL divergence regularization (AutoEncoderKL) was trained on this pool; its encoder generated latent features for KC images (n = 535). A held-out set (n = 70) with Pentacam keratometry was labeled by K1 into &lt;40 D, 40\u201347 D, and &gt;47 D. An ensemble classifier chosen via grid search and cross-validation used the encoder features. Performance was assessed for accuracy, precision, recall, and F1-score. Results: The model achieved 91% accuracy across all classes. Precision of the model was 0.77 (&lt;40 D), 0.98 (40\u201347 D), and 0.86 (&gt;47 D); recall was 0.83, 0.91, and 1.00; and F1-scores were 0.80, 0.94, and 0.92, respectively. Notably, the model achieved perfect recall for the &gt;47 D K1 category. Conclusions: A smartphone-assisted Placido ring imaging approach was able to predict K1-based keratometric categories without requiring tomographic or keratometric measurements as model inputs at inference. These findings provide preliminary proof-of-concept for the potential use of smartphone-assisted Placido ring images as a low-cost approach for K1-based stratification. Larger externally validated studies across different sites, devices, operators, printed Placido discs, acquisition conditions, and patient populations are required before clinical utility can be assessed.<\/jats:p>","DOI":"10.3390\/diagnostics16132076","type":"journal-article","created":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T10:04:44Z","timestamp":1782986684000},"page":"2076","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Smartphone-Assisted Placido Ring Imaging for K1 Stratification in Keratoconus: A Deep Learning Study"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-0053-6416","authenticated-orcid":false,"given":"Enes","family":"Eroglu","sequence":"first","affiliation":[{"name":"Department of Ophthalmology, University of North Carolina at Chapel Hill, 2226 Nelson Highway, Chapel Hill, NC 27517, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-8187-3261","authenticated-orcid":false,"given":"Nicholas","family":"Tomaras","sequence":"additional","affiliation":[{"name":"Department of Ophthalmology, University of Illinois Chicago, 1855 W. Taylor Street, Chicago, IL 60612, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-9837-5731","authenticated-orcid":false,"given":"Kabir Anand","family":"Pathak","sequence":"additional","affiliation":[{"name":"Department of Ophthalmology, University of North Carolina at Chapel Hill, 2226 Nelson Highway, Chapel Hill, NC 27517, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5457-1624","authenticated-orcid":false,"given":"Jaron","family":"Sanchez","sequence":"additional","affiliation":[{"name":"Department of Ophthalmology, University of North Carolina at Chapel Hill, 2226 Nelson Highway, Chapel Hill, NC 27517, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rafael Alejandro","family":"Pinto-Colmenarez","sequence":"additional","affiliation":[{"name":"Department of Ophthalmology, University of North Carolina at Chapel Hill, 2226 Nelson Highway, Chapel Hill, NC 27517, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Juan Carlos","family":"Prieto","sequence":"additional","affiliation":[{"name":"Department of Ophthalmology, University of North Carolina at Chapel Hill, 2226 Nelson Highway, Chapel Hill, NC 27517, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lucie","family":"Dole","sequence":"additional","affiliation":[{"name":"Department of Ophthalmology, University of North Carolina at Chapel Hill, 2226 Nelson Highway, Chapel Hill, NC 27517, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4601-5756","authenticated-orcid":false,"given":"Rohith","family":"Erukulla","sequence":"additional","affiliation":[{"name":"Department of Ophthalmology, University of Illinois Chicago, 1855 W. Taylor Street, Chicago, IL 60612, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-0297-5755","authenticated-orcid":false,"given":"Michael","family":"Maizel","sequence":"additional","affiliation":[{"name":"Department of Ophthalmology, University of North Carolina at Chapel Hill, 2226 Nelson Highway, Chapel Hill, NC 27517, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ali R.","family":"Djalilian","sequence":"additional","affiliation":[{"name":"Department of Ophthalmology, University of Illinois Chicago, 1855 W. Taylor Street, Chicago, IL 60612, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6546-3546","authenticated-orcid":false,"given":"Mohammad","family":"Soleimani","sequence":"additional","affiliation":[{"name":"Department of Ophthalmology, University of North Carolina at Chapel Hill, 2226 Nelson Highway, Chapel Hill, NC 27517, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,7,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1038\/s41572-024-00565-3","article-title":"Keratoconus","volume":"10","author":"Singh","year":"2024","journal-title":"Nat. Rev. Dis. Primers"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2705","DOI":"10.2147\/OPTH.S392665","article-title":"Keratoconus diagnosis and treatment: Recent advances and future directions","volume":"17","author":"Bui","year":"2023","journal-title":"Clin. Ophthalmol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"812","DOI":"10.1016\/j.ophtha.2013.10.028","article-title":"A randomized, controlled trial of corneal collagen cross-linking in progressive keratoconus: Three-year results","volume":"121","author":"Chan","year":"2014","journal-title":"Ophthalmology"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1016\/j.ajo.2023.11.009","article-title":"Prevalence and economic burden of keratoconus in the United States","volume":"259","author":"Singh","year":"2024","journal-title":"Am. J. Ophthalmol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"259","DOI":"10.2147\/OPTH.S447036","article-title":"A comparison of Black and non-Black patients in the presentation and treatment of keratoconus","volume":"18","author":"Christensen","year":"2024","journal-title":"Clin. Ophthalmol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1097\/ICO.0000000000003302","article-title":"Racial variation in visual impairment of patients with keratoconus at presentation","volume":"43","author":"Nugent","year":"2024","journal-title":"Cornea"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Abou Said, G.A., Gispets, J., and Shneor, E. (2025). Strategies for early keratoconus diagnosis: A narrative review of evaluating affordable and effective detection techniques. J. Clin. Med., 14.","DOI":"10.3390\/jcm14020460"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1038\/s41433-020-1039-1","article-title":"A review of imaging modalities for detecting early keratoconus","volume":"35","author":"Zhang","year":"2021","journal-title":"Eye"},{"key":"ref_9","first-page":"CD014911","article-title":"Artificial intelligence for detecting keratoconus","volume":"11","author":"Vandevenne","year":"2023","journal-title":"Cochrane Database Syst. Rev."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"5117","DOI":"10.1038\/s41598-025-89768-6","article-title":"Artificial intelligence support improves diagnosis accuracy in anterior segment eye diseases","volume":"15","author":"Maehara","year":"2025","journal-title":"Sci. Rep."},{"key":"ref_11","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_12","doi-asserted-by":"crossref","first-page":"335","DOI":"10.1097\/ICU.0000000000001150","article-title":"Advancements in artificial intelligence for the diagnosis and management of anterior segment diseases","volume":"36","author":"Jin","year":"2025","journal-title":"Curr. Opin. Ophthalmol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1316","DOI":"10.3390\/s25051316","article-title":"An IoT-enabled mHealth sensing approach for remote detection of keratoconus using smartphone technology","volume":"25","author":"Askarian","year":"2025","journal-title":"Sensors"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1017","DOI":"10.1007\/s00417-023-06154-6","article-title":"Comparison of different corneal imaging modalities using artificial intelligence for diagnosis of keratoconus: A systematic review and meta-analysis","volume":"262","author":"Hashemi","year":"2024","journal-title":"Graefes Arch. Clin. Exp. Ophthalmol."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1381","DOI":"10.1364\/AO.449875","article-title":"Smartphone-based corneal topography with null-screens","volume":"61","year":"2022","journal-title":"Appl. Opt."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"511","DOI":"10.1038\/s41433-022-01961-3","article-title":"Global metrics on ocular biometry: Representative averages and standard deviations across ten countries from four continents","volume":"37","author":"Ganesh","year":"2023","journal-title":"Eye"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"459","DOI":"10.1136\/bjophthalmol-2013-304132","article-title":"Evaluation of corneal elevation, pachymetry and keratometry in keratoconic eyes with respect to the stage of Amsler-Krumeich classification","volume":"98","author":"Kamiya","year":"2014","journal-title":"Br. J. Ophthalmol."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1005","DOI":"10.1097\/ICO.0000000000000537","article-title":"Keratoconus clinical findings according to different classifications","volume":"34","author":"Naderan","year":"2015","journal-title":"Cornea"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"12436","DOI":"10.1038\/s41598-018-31008-1","article-title":"Comparison of simulated keratometry and total refractive power for keratoconus according to the stage of Amsler-Krumeich classification","volume":"8","author":"Kamiya","year":"2018","journal-title":"Sci. Rep."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"3941","DOI":"10.2147\/OPTH.S436492","article-title":"Scheimpflug-derived keratometric, pachymetric and pachymetric progression indices in the diagnosis of keratoconus: A systematic review and meta-analysis","volume":"17","author":"Owusu","year":"2023","journal-title":"Clin. Ophthalmol."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1016\/j.neunet.2018.06.011","article-title":"An end-to-end deep neural network for iris segmentation in unconstrained scenarios","volume":"106","author":"Bazrafkan","year":"2018","journal-title":"Neural Netw."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1501","DOI":"10.3390\/s18051501","article-title":"IrisDenseNet: Robust iris segmentation using densely connected fully convolutional networks in the images by visible light and near-infrared light camera sensors","volume":"18","author":"Arsalan","year":"2018","journal-title":"Sensors"},{"key":"ref_23","unstructured":"Prieto, J.C., Shah, H., Jones, K.R., Chew, R.F., Kana, H.M., Weaver, J., Flueckiger, R.M., McPherson, S., and Gower, E.W. (2021, January 7\u20139). Image sequence analysis via GRU and attention for trachomatous trichiasis classification. Proceedings of the Fourth Conference on Medical Imaging with Deep Learning, PMLR, L\u00fcbeck, Germany."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Rombach, R., Blattmann, A., Lorenz, D., Esser, P., and Ommer, B. (2022, January 18\u201324). High-resolution image synthesis with latent diffusion models. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.01042"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1146\/annurev-bioeng-071516-044442","article-title":"Deep learning in medical image analysis","volume":"19","author":"Shen","year":"2017","journal-title":"Annu. Rev. Biomed. Eng."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"100759","DOI":"10.1016\/j.preteyeres.2019.04.003","article-title":"Deep learning in ophthalmology: The technical and clinical considerations","volume":"72","author":"Ting","year":"2019","journal-title":"Prog. Retin. Eye Res."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1642","DOI":"10.1136\/bjophthalmol-2021-318866","article-title":"Deep learning algorithms for automatic detection of pterygium using anterior segment photographs from slit-lamp and hand-held cameras","volume":"106","author":"Fang","year":"2022","journal-title":"Br. J. Ophthalmol."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"104583","DOI":"10.1016\/j.ijmedinf.2021.104583","article-title":"Feasibility assessment of infectious keratitis depicted on slit-lamp and smartphone photographs using deep learning","volume":"155","author":"Wang","year":"2021","journal-title":"Int. J. Med. Inform."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"e000824","DOI":"10.1136\/bmjophth-2021-000824","article-title":"Keratoconus detection of changes using deep learning of colour-coded maps","volume":"6","author":"Chen","year":"2021","journal-title":"BMJ Open Ophthalmol."},{"key":"ref_30","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 of 11,529 eyes","volume":"126","author":"Ferdi","year":"2019","journal-title":"Ophthalmology"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Goodman, D., and Zhu, A.Y. (2024). Utility of artificial intelligence in the diagnosis and management of keratoconus: A systematic review. Front. Ophthalmol., 4.","DOI":"10.3389\/fopht.2024.1380701"}],"container-title":["Diagnostics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-4418\/16\/13\/2076\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T10:48:13Z","timestamp":1782989293000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-4418\/16\/13\/2076"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,2]]},"references-count":31,"journal-issue":{"issue":"13","published-online":{"date-parts":[[2026,7]]}},"alternative-id":["diagnostics16132076"],"URL":"https:\/\/doi.org\/10.3390\/diagnostics16132076","relation":{},"ISSN":["2075-4418"],"issn-type":[{"value":"2075-4418","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,2]]}}}