{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,5]],"date-time":"2026-10-05T11:31:49Z","timestamp":1791199909015,"version":"4.2.0"},"reference-count":40,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2023,3,13]],"date-time":"2023-03-13T00:00:00Z","timestamp":1678665600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation Regional Innovation and Development Joint Fund","award":["U20A20386"],"award-info":[{"award-number":["U20A20386"]}]},{"name":"National Natural Science Foundation Regional Innovation and Development Joint Fund","award":["81870635"],"award-info":[{"award-number":["81870635"]}]},{"name":"National Natural Science Foundation Regional Innovation and Development Joint Fund","award":["2021E50007"],"award-info":[{"award-number":["2021E50007"]}]},{"name":"National Natural Science Foundation Regional Innovation and Development Joint Fund","award":["62201323"],"award-info":[{"award-number":["62201323"]}]},{"name":"National Natural Science Foundation Regional Innovation and Development Joint Fund","award":["BK20220266"],"award-info":[{"award-number":["BK20220266"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U20A20386"],"award-info":[{"award-number":["U20A20386"]}],"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":["81870635"],"award-info":[{"award-number":["81870635"]}],"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":["2021E50007"],"award-info":[{"award-number":["2021E50007"]}],"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":["62201323"],"award-info":[{"award-number":["62201323"]}],"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":["BK20220266"],"award-info":[{"award-number":["BK20220266"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Clinical Medical Research Center for Eye Diseases of Zhejiang Province","award":["U20A20386"],"award-info":[{"award-number":["U20A20386"]}]},{"name":"Clinical Medical Research Center for Eye Diseases of Zhejiang Province","award":["81870635"],"award-info":[{"award-number":["81870635"]}]},{"name":"Clinical Medical Research Center for Eye Diseases of Zhejiang Province","award":["2021E50007"],"award-info":[{"award-number":["2021E50007"]}]},{"name":"Clinical Medical Research Center for Eye Diseases of Zhejiang Province","award":["62201323"],"award-info":[{"award-number":["62201323"]}]},{"name":"Clinical Medical Research Center for Eye Diseases of Zhejiang Province","award":["BK20220266"],"award-info":[{"award-number":["BK20220266"]}]},{"name":"National Natural Science Foundation of China","award":["U20A20386"],"award-info":[{"award-number":["U20A20386"]}]},{"name":"National Natural Science Foundation of China","award":["81870635"],"award-info":[{"award-number":["81870635"]}]},{"name":"National Natural Science Foundation of China","award":["2021E50007"],"award-info":[{"award-number":["2021E50007"]}]},{"name":"National Natural Science Foundation of China","award":["62201323"],"award-info":[{"award-number":["62201323"]}]},{"name":"National Natural Science Foundation of China","award":["BK20220266"],"award-info":[{"award-number":["BK20220266"]}]},{"name":"Natural Science Foundation of Jiangsu Province","award":["U20A20386"],"award-info":[{"award-number":["U20A20386"]}]},{"name":"Natural Science Foundation of Jiangsu Province","award":["81870635"],"award-info":[{"award-number":["81870635"]}]},{"name":"Natural Science Foundation of Jiangsu Province","award":["2021E50007"],"award-info":[{"award-number":["2021E50007"]}]},{"name":"Natural Science Foundation of Jiangsu Province","award":["62201323"],"award-info":[{"award-number":["62201323"]}]},{"name":"Natural Science Foundation of Jiangsu Province","award":["BK20220266"],"award-info":[{"award-number":["BK20220266"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JPM"],"abstract":"<jats:p>Infectious keratitis (IK) is a common ophthalmic emergency that requires prompt and accurate treatment. This study aimed to propose a deep learning (DL) system based on slit lamp images to automatically screen and diagnose infectious keratitis. This study established a dataset of 2757 slit lamp images from 744 patients, including normal cornea, viral keratitis (VK), fungal keratitis (FK), and bacterial keratitis (BK). Six different DL algorithms were developed and evaluated for the classification of infectious keratitis. Among all the models, the EffecientNetV2-M showed the best classification ability, with an accuracy of 0.735, a recall of 0.680, and a specificity of 0.904, which was also superior to two ophthalmologists. The area under the receiver operating characteristics curve (AUC) of the EffecientNetV2-M was 0.85; correspondingly, 1.00 for normal cornea, 0.87 for VK, 0.87 for FK, and 0.64 for BK. The findings suggested that the proposed DL system could perform well in the classification of normal corneas and different types of infectious keratitis, based on slit lamp images. This study proves the potential of the DL model to help ophthalmologists to identify infectious keratitis and improve the accuracy and efficiency of diagnosis.<\/jats:p>","DOI":"10.3390\/jpm13030519","type":"journal-article","created":{"date-parts":[[2023,3,14]],"date-time":"2023-03-14T03:04:46Z","timestamp":1678763086000},"page":"519","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Automatic Diagnosis of Infectious Keratitis Based on Slit Lamp Images Analysis"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1177-0679","authenticated-orcid":false,"given":"Shaodan","family":"Hu","sequence":"first","affiliation":[{"name":"Department of Ophthalmology, College of Medicine, The Second Affiliated Hospital of Zhejiang University, Hangzhou 310009, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yiming","family":"Sun","sequence":"additional","affiliation":[{"name":"Department of Ophthalmology, College of Medicine, The Second Affiliated Hospital of Zhejiang University, Hangzhou 310009, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9967-4675","authenticated-orcid":false,"given":"Jinhao","family":"Li","sequence":"additional","affiliation":[{"name":"School of Mechanical, Electrical and Information Engineering, Shandong University, Weihai 264209, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peifang","family":"Xu","sequence":"additional","affiliation":[{"name":"Department of Ophthalmology, College of Medicine, The Second Affiliated Hospital of Zhejiang University, Hangzhou 310009, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mingyu","family":"Xu","sequence":"additional","affiliation":[{"name":"Department of Ophthalmology, College of Medicine, The Second Affiliated Hospital of Zhejiang University, Hangzhou 310009, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yifan","family":"Zhou","sequence":"additional","affiliation":[{"name":"Department of Ophthalmology, College of Medicine, The Second Affiliated Hospital of Zhejiang University, Hangzhou 310009, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4627-3392","authenticated-orcid":false,"given":"Yaqi","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Media Engineering, Communication University of Zhejiang, Hangzhou 310018, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuai","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Mechanical, Electrical and Information Engineering, Shandong University, Weihai 264209, China"},{"name":"Suzhou Research Institute, Shandong University, Suzhou 215123, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Juan","family":"Ye","sequence":"additional","affiliation":[{"name":"Department of Ophthalmology, College of Medicine, The Second Affiliated Hospital of Zhejiang University, Hangzhou 310009, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"E1221","DOI":"10.1016\/S2214-109X(17)30393-5","article-title":"Global causes of blindness and distance vision impairment 1990\u20132020: A systematic review and meta-analysis","volume":"5","author":"Flaxman","year":"2017","journal-title":"Lancet Glob. Health"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1016\/j.survophthal.2018.12.003","article-title":"The persistent dilemma of microbial keratitis: Global burden, diagnosis and antimicrobial resistance","volume":"64","author":"Ung","year":"2019","journal-title":"Surv. Ophthalmol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"555","DOI":"10.1097\/00003226-200208000-00004","article-title":"The epidemiological features and laboratory results of fungal keratitis: A 10-year review at a referral eye care center in South India","volume":"21","author":"Gopinathan","year":"2002","journal-title":"Cornea"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"6273","DOI":"10.1167\/iovs.17-22243","article-title":"Designed Host Defense Peptides for the Treatment of Bacterial Keratitis","volume":"58","author":"Clemens","year":"2017","journal-title":"Investig. Ophthalmol. Vis. Sci."},{"key":"ref_5","first-page":"6","article-title":"Distinguishing fungal and bacterial keratitis on clinical signs","volume":"28","author":"Leck","year":"2015","journal-title":"Community Eye Health"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"831","DOI":"10.1016\/S0891-5520(20)30485-2","article-title":"Viral keratitis","volume":"6","author":"Mader","year":"1992","journal-title":"Infect. Dis. Clin. N. Am."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"67","DOI":"10.18773\/austprescr.2018.016","article-title":"Common eye infections","volume":"41","author":"Watson","year":"2018","journal-title":"Aust. Prescr."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1097\/ICO.0b013e318156caf2","article-title":"Risk factors and causative organisms in microbial keratitis","volume":"27","author":"Green","year":"2008","journal-title":"Cornea"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"2161","DOI":"10.1016\/j.ophtha.2011.04.021","article-title":"Antibiotic Resistance in Microbial Keratitis: Ten-Year Experience of Corneal Scrapes in the United Kingdom","volume":"118","author":"Shalchi","year":"2011","journal-title":"Ophthalmology"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1324","DOI":"10.1099\/jmm.0.001041","article-title":"Use of a corneal impression membrane and PCR for the detection of herpes simplex virus type-1","volume":"68","author":"Brunner","year":"2019","journal-title":"J. Med. Microbiol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"105875","DOI":"10.1016\/j.mimet.2020.105875","article-title":"A SYBR Green based multiplex Real-Time PCR assay for rapid detection and differentiation of ocular bacterial pathogens","volume":"171","author":"Geetha","year":"2020","journal-title":"J. Microbiol. Methods"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"775","DOI":"10.1097\/ICO.0000000000002284","article-title":"Novel Duplex Polymerase Chain Reaction for the Rapid Detection of Pythium insidiosum Directly from Corneal Specimens of Patients with Ocular Pythiosis","volume":"39","author":"Kulandai","year":"2020","journal-title":"Cornea"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"2865","DOI":"10.1007\/s10792-019-01134-4","article-title":"Role of in vivo confocal microscopy in the diagnosis of infectious keratitis","volume":"39","author":"Wang","year":"2019","journal-title":"Int. Ophthalmol."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1787","DOI":"10.1167\/iovs.11-8478","article-title":"The Clinical Differentiation of Bacterial and Fungal Keratitis: A Photographic Survey","volume":"53","author":"Dalmon","year":"2012","journal-title":"Investig. Ophthalmol. Vis. Sci."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1007\/978-3-030-33128-3_1","article-title":"Deep Learning in Medical Image Analysis","volume":"Volume 1213","author":"Lee","year":"2020","journal-title":"Deep Learning in Medical Image Analysis: Challenges and Applications. Advances in Experimental Medicine and Biology"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"106691","DOI":"10.1016\/j.asoc.2020.106691","article-title":"Automated medical diagnosis of COVID-19 through EfficientNet convolutional neural network","volume":"96","author":"Marques","year":"2020","journal-title":"Appl. Soft. Comput."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"783","DOI":"10.1136\/bjophthalmol-2011-301378","article-title":"The number of ophthalmologists in practice and training worldwide: A growing gap despite more than 200,000 practitioners","volume":"96","author":"Resnikoff","year":"2012","journal-title":"Br. J. Ophthalmol."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"198","DOI":"10.4103\/0970-0218.120153","article-title":"Burden of corneal blindness in India","volume":"38","author":"Gupta","year":"2013","journal-title":"Indian J. Commun. Med."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"2211","DOI":"10.1001\/jama.2017.18152","article-title":"Development and Validation of a Deep Learning System for Diabetic Retinopathy and Related Eye Diseases Using Retinal Images from Multiethnic Populations with Diabetes","volume":"318","author":"Ting","year":"2017","journal-title":"J. Am. Med. Assoc."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1207","DOI":"10.1016\/j.ophtha.2018.04.020","article-title":"Efficacy of a Deep Learning System for Detecting Glaucomatous Optic Neuropathy Based on Color Fundus Photographs","volume":"125","author":"Hood","year":"2018","journal-title":"Ophthalmology"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1038\/s42003-019-0730-x","article-title":"Deep learning for detecting retinal detachment and discerning macular status using ultra-widefield fundus images","volume":"3","author":"Li","year":"2020","journal-title":"Commun. Biol."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"4926","DOI":"10.1002\/mp.15002","article-title":"Attention-based deep learning system for automated diagnoses of age-related macular degeneration in optical coherence tomography images","volume":"48","author":"Yan","year":"2021","journal-title":"Med. Phys."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1663","DOI":"10.1007\/s00417-021-05503-7","article-title":"End-to-end diabetic retinopathy grading based on fundus fluorescein angiography images using deep learning","volume":"260","author":"Gao","year":"2022","journal-title":"Graefes Arch. Clin. Exp. Ophthalmol."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"e19","DOI":"10.1111\/aos.14514","article-title":"Prediction of response to anti-vascular endothelial growth factor treatment in diabetic macular oedema using an optical coherence tomography-based machine learning method","volume":"99","author":"Cao","year":"2021","journal-title":"Acta Ophthalmol."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"17851","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","year":"2020","journal-title":"Sci. Rep."},{"key":"ref_26","unstructured":"Tran, T., Pham, T., Carneiro, G., Palmer, L., and Reid, I. (2017, January 4\u20139). A Bayesian Data Augmentation Approach for Learning Deep Models. Proceedings of the Advances in Neural Information Processing Systems 30 (NIPS 2017), Long Beach, CA, USA."},{"key":"ref_27","unstructured":"Simonyan, K., and Zisserman, A.-P. (2014). Very Deep Convolutional Networks for Large-Scale Image Recognition. arXiv."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Jia, Y.Q., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. (2015, January 7\u201312). Going Deeper with Convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Ioffe, S., Vanhoucke, V., and Alemi, A. (2016). Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning. AAAI Conf. Artif. Intell., 31.","DOI":"10.1609\/aaai.v31i1.11231"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., van der Maaten, L., and Weinberger, K.Q. (2017, January 21\u201326). Densely Connected Convolutional Networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref_31","unstructured":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., and Gelly, S. (2020). An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. arXiv."},{"key":"ref_32","unstructured":"Tan, M.X., and Le, Q.V. (2021, January 18\u201324). EfficientNetV2: Smaller Models and Faster Training. Proceedings of the International Conference on Machine Learning, Virtual."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., and Batra, D. (2017, January 22\u201329). Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.74"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"14424","DOI":"10.1038\/s41598-020-71425-9","article-title":"A deep learning approach in diagnosing fungal keratitis based on corneal photographs","volume":"10","author":"Kuo","year":"2020","journal-title":"Sci. Rep."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"616","DOI":"10.1097\/ICO.0000000000002830","article-title":"Deep Learning for Discrimination Between Fungal Keratitis and Bacterial Keratitis: DeepKeratitis","volume":"41","author":"Ghosh","year":"2022","journal-title":"Cornea"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"3738","DOI":"10.1038\/s41467-021-24116-6","article-title":"Preventing corneal blindness caused by keratitis using artificial intelligence","volume":"12","author":"Li","year":"2021","journal-title":"Nat. Commun."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1002","DOI":"10.1016\/j.eng.2020.04.012","article-title":"Deep Sequential Feature Learning in Clinical Image Classification of Infectious Keratitis","volume":"7","author":"Xu","year":"2021","journal-title":"Engineering"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"227","DOI":"10.1016\/j.ophtha.2021.09.019","article-title":"Expert Performance in Visual Differentiation of Bacterial and Fungal Keratitis","volume":"129","author":"Redd","year":"2022","journal-title":"Ophthalmology"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Yahya, G., Ebada, A., Khalaf, E.M., Mansour, B., Nouh, N.A., Mosbah, R.A., Saber, S., Moustafa, M., Negm, S., and El-Sokkary, M.M.A. (2021). Soil-Associated Bacillus Species: A Reservoir of Bioactive Compounds with Potential Therapeutic Activity against Human Pathogens. Microorganisms, 9.","DOI":"10.3390\/microorganisms9061131"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"El-Telbany, M., Mohamed, A.A., Yahya, G., Abdelghafar, A., Abdel-Halim, M.S., Saber, S., Alfaleh, M.A., Mohamed, A.H., Abdelrahman, F., and Fathey, H.A. (2022). Combination of Meropenem and Zinc Oxide Nanoparticles; Antimicrobial Synergism, Exaggerated Antibiofilm Activity, and Efficient Therapeutic Strategy against Bacterial Keratitis. Antibiotics, 11.","DOI":"10.3390\/antibiotics11101374"}],"container-title":["Journal of Personalized Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-4426\/13\/3\/519\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:54:00Z","timestamp":1760122440000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-4426\/13\/3\/519"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,3,13]]},"references-count":40,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2023,3]]}},"alternative-id":["jpm13030519"],"URL":"https:\/\/doi.org\/10.3390\/jpm13030519","relation":{},"ISSN":["2075-4426"],"issn-type":[{"value":"2075-4426","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,3,13]]}}}