{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,13]],"date-time":"2026-09-13T02:40:59Z","timestamp":1789267259174,"version":"build-2803163510"},"reference-count":49,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2023,10,26]],"date-time":"2023-10-26T00:00:00Z","timestamp":1698278400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"R01 EY024349 (ARD), UH3 EY031809 (ARD): Core Grant for Vision Research EY01792, all from NEI\/NIH"},{"name":"Vision Research Program\u2014Congressionally Directed Medical Research Program VR170180 from the Department of Defense, Unrestricted Grant to the department"},{"name":"Physician\u2013Scientist Award from Research to Prevent Blindness"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Life"],"abstract":"<jats:p>Infectious keratitis (IK), which is one of the most common and catastrophic ophthalmic emergencies, accounts for the leading cause of corneal blindness worldwide. Different pathogens, including bacteria, viruses, fungi, and parasites, can cause IK. The diagnosis and etiology detection of IK pose specific challenges, and delayed or incorrect diagnosis can significantly worsen the outcome. Currently, this process is mainly performed based on slit\u2013lamp findings, corneal smear and culture, tissue biopsy, PCR, and confocal microscopy. However, these diagnostic methods have their drawbacks, including experience dependency, tissue damage, cost, and time consumption. Diagnosis and etiology detection of IK can be especially challenging in rural areas or in countries with limited resources. In recent years, artificial intelligence (AI) has opened new windows in medical fields such as ophthalmology. An increasing number of studies have utilized AI in the diagnosis of anterior segment diseases such as IK. Several studies have demonstrated that AI algorithms can diagnose and detect the etiology of IK accurately and fast, which can be valuable, especially in remote areas and in countries with limited resources. Herein, we provided a comprehensive update on the utility of AI in IK.<\/jats:p>","DOI":"10.3390\/life13112117","type":"journal-article","created":{"date-parts":[[2023,10,26]],"date-time":"2023-10-26T02:40:07Z","timestamp":1698288007000},"page":"2117","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Artificial Intelligence and Infectious Keratitis: Where Are We Now?"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6546-3546","authenticated-orcid":false,"given":"Mohammad","family":"Soleimani","sequence":"first","affiliation":[{"name":"Eye Research Center, Farabi Eye Hospital, Tehran University of Medical Sciences, Tehran 1336616351, Iran"},{"name":"Department of Ophthalmology and Visual Sciences, University of Illinois at Chicago, Chicago, IL 60612, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1273-9166","authenticated-orcid":false,"given":"Kasra","family":"Cheraqpour","sequence":"additional","affiliation":[{"name":"Eye Research Center, Farabi Eye Hospital, Tehran University of Medical Sciences, Tehran 1336616351, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8064-3545","authenticated-orcid":false,"given":"Reza","family":"Sadeghi","sequence":"additional","affiliation":[{"name":"Eye Research Center, Farabi Eye Hospital, Tehran University of Medical Sciences, Tehran 1336616351, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Saharnaz","family":"Pezeshgi","sequence":"additional","affiliation":[{"name":"School of Medicine, Tehran University of Medical Sciences, Tehran 1461884513, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6551-2475","authenticated-orcid":false,"given":"Raghuram","family":"Koganti","sequence":"additional","affiliation":[{"name":"Department of Ophthalmology and Visual Sciences, University of Illinois at Chicago, Chicago, IL 60612, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ali R.","family":"Djalilian","sequence":"additional","affiliation":[{"name":"Department of Ophthalmology and Visual Sciences, University of Illinois at Chicago, Chicago, IL 60612, USA"},{"name":"Cornea Service, Stem Cell Therapy and Corneal Tissue Engineering Laboratory, Illinois Eye and Ear Infirmary, Chicago, IL 60612, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,10,26]]},"reference":[{"key":"ref_1","first-page":"32","article-title":"Evaluation Of Therapeutic Modalities And Outcome In Patients Of Infectious Keratitis: A Prospective Observational Study At A Tertiary Care Eye Hospital","volume":"12","author":"Shah","year":"2021","journal-title":"Natl. J. Integr. Res. Med."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1084","DOI":"10.1038\/s41433-020-01339-3","article-title":"Infectious keratitis: An update on epidemiology, causative microorganisms, risk factors, and antimicrobial resistance","volume":"35","author":"Ting","year":"2021","journal-title":"Eye"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"3110","DOI":"10.1038\/s41433-020-01378-w","article-title":"Infectious keratitis: Trends in microbiological and antibiotic sensitivity patterns","volume":"35","author":"Soleimani","year":"2021","journal-title":"Eye"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"287","DOI":"10.2147\/OPTH.S181997","article-title":"Treatment-resistant bacterial keratitis: Challenges and solutions","volume":"14","author":"Egrilmez","year":"2020","journal-title":"Clin. Ophthalmol."},{"key":"ref_5","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_6","doi-asserted-by":"crossref","first-page":"1900143","DOI":"10.1002\/aisy.201900143","article-title":"Artificial intelligence to power the future of materials science and engineering","volume":"2","author":"Sha","year":"2020","journal-title":"Adv. Intell. Syst."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"268","DOI":"10.1097\/APO.0000000000000394","article-title":"Artificial intelligence in cornea, refractive surgery, and cataract: Basic principles, clinical applications, and future directions","volume":"10","author":"Rampat","year":"2021","journal-title":"Asia-Pacific J. Ophthalmol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"588","DOI":"10.1136\/bjophthalmol-2019-314336","article-title":"Estimated number of ophthalmologists worldwide (International Council of Ophthalmology update): Will we meet the needs?","volume":"104","author":"Resnikoff","year":"2020","journal-title":"Br. J. Ophthalmol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1111\/vop.12928","article-title":"Applications of in vivo confocal microscopy in the management of infectious keratitis in veterinary ophthalmology","volume":"25","author":"Ledbetter","year":"2022","journal-title":"Vet. Ophthalmol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"835804","DOI":"10.3389\/fmed.2022.835804","article-title":"Benefits of integrating telemedicine and artificial intelligence into outreach eye care: Stepwise approach and future directions","volume":"9","author":"Chia","year":"2022","journal-title":"Front. Med."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"451","DOI":"10.1038\/s41433-019-0566-0","article-title":"Artificial intelligence for diabetic retinopathy screening: A review","volume":"34","author":"Grzybowski","year":"2020","journal-title":"Eye"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"e69","DOI":"10.1016\/j.ophtha.2022.02.008","article-title":"Artificial intelligence for retinopathy of prematurity: Validation of a vascular severity scale against international expert diagnosis","volume":"129","author":"Campbell","year":"2022","journal-title":"Ophthalmology"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"2159","DOI":"10.1177\/15353702211031547","article-title":"Imaging and artificial intelligence for progression of age-related macular degeneration","volume":"246","author":"Romond","year":"2021","journal-title":"Exp. Biol. Med."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1895","DOI":"10.18240\/ijo.2021.12.13","article-title":"Artificial intelligence can assist with diagnosing retinal vein occlusion","volume":"14","author":"Chen","year":"2021","journal-title":"Int. J. Ophthalmol."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"e157968","DOI":"10.1172\/JCI157968","article-title":"A deep-learning system predicts glaucoma incidence and progression using retinal photographs","volume":"132","author":"Li","year":"2022","journal-title":"J. Clin. Investig."},{"key":"ref_16","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_17","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Wang, Y., Zhang, H., Samusak, A., Rao, H., Xiao, C., Abula, M., Cao, Q., and Dai, Q. (2023). Artificial intelligence-assisted diagnosis of ocular surface diseases. Front. Cell Dev. Biol., 11.","DOI":"10.3389\/fcell.2023.1133680"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Ji, Y., Liu, S., Hong, X., Lu, Y., Wu, X., Li, K., Li, K., and Liu, Y. (2022). Advances in artificial intelligence applications for ocular surface diseases diagnosis. Front. Cell Dev. Biol., 10.","DOI":"10.3389\/fcell.2022.1107689"},{"key":"ref_19","first-page":"4236","article-title":"Artificial intelligence based detection of infectious keratitis using slit-lamp images","volume":"60","author":"Vupparaboina","year":"2019","journal-title":"Investig. Ophthalmol. Vis. Sci."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"706","DOI":"10.21037\/atm.2020.03.134","article-title":"Deep learning-based automated diagnosis of fungal keratitis with in vivo confocal microscopy images","volume":"8","author":"Lv","year":"2020","journal-title":"Ann. Transl. Med."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1076\/ceyr.27.2.111.15949","article-title":"Neural network approach to classify infective keratitis","volume":"27","author":"Saini","year":"2003","journal-title":"Curr. Eye Res."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1109\/JBHI.2020.2983549","article-title":"Open-source automatic segmentation of ocular structures and biomarkers of microbial keratitis on slit-lamp photography images using deep learning","volume":"25","author":"Loo","year":"2020","journal-title":"IEEE J. Biomed. Health Inf."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1195188","DOI":"10.3389\/fnins.2023.1195188","article-title":"Comparisons of artificial intelligence algorithms in automatic segmentation for fungal keratitis diagnosis by anterior segment images","volume":"17","author":"Li","year":"2023","journal-title":"Front. Neurosci."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1016\/j.ophtha.2021.07.033","article-title":"Differentiation of Active Corneal Infections from Healed Scars Using Deep Learning","volume":"129","author":"Tiwari","year":"2022","journal-title":"Ophthalmology"},{"key":"ref_25","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_26","doi-asserted-by":"crossref","first-page":"24227","DOI":"10.1038\/s41598-021-03572-6","article-title":"Comparisons of deep learning algorithms for diagnosing bacterial keratitis via external eye photographs","volume":"11","author":"Kuo","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Wei, Z., Wang, S., Wang, Z., Zhang, Y., Chen, K., Gong, L., Li, G., Zheng, Q., Zhang, Q., and He, Y. (2023). Development and multi-center validation of machine learning model for early detection of fungal keratitis. EBioMedicine, 88.","DOI":"10.1016\/j.ebiom.2023.104438"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"3279","DOI":"10.4103\/ijo.IJO_178_22","article-title":"Advances in the diagnosis of herpes simplex stromal necrotising keratitis: A feasibility study on deep learning approach","volume":"70","author":"Natarajan","year":"2022","journal-title":"Indian J. Ophthalmol."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Alquran, H., Al-Issa, Y., Alsalatie, M., Mustafa, W.A., Qasmieh, I.A., and Zyout, A.a. (2022). Intelligent Diagnosis and Classification of Keratitis. Diagnostics, 12.","DOI":"10.3390\/diagnostics12061344"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Hung, N., Kang, E.Y.-C., Shih, A.G.-Y., Lin, C.-H., Kuo, M.T., Hwang, Y.-S., Wu, W.-C., Kuo, C.-F., and Hsiao, C.-H. (2021). Using Slit-Lamp Images for Deep Learning\u2013Based Identification of Bacterial and Fungal Keratitis. Diagnostics, 11.","DOI":"10.20944\/preprints202105.0438.v1"},{"key":"ref_31","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_32","doi-asserted-by":"crossref","first-page":"1162124","DOI":"10.3389\/fmed.2023.1162124","article-title":"Deep learning-based classification system of bacterial keratitis and fungal keratitis using anterior segment images","volume":"10","author":"Won","year":"2023","journal-title":"Front. Med."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"20406223221136071","DOI":"10.1177\/20406223221136071","article-title":"Deep learning-based classification of infectious keratitis on slit-lamp images","volume":"13","author":"Zhang","year":"2022","journal-title":"Ther. Adv. Chronic Dis."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Kuo, M.-T., Hsu, B.W.-Y., Lin, Y.S., Fang, P.-C., Yu, H.-J., Hsiao, Y.-T., and Tseng, V.S. (2022). Deep Learning Approach in Image Diagnosis of Pseudomonas Keratitis. Diagnostics, 12.","DOI":"10.3390\/diagnostics12122948"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Hu, S., Sun, Y., Li, J., Xu, P., Xu, M., Zhou, Y., Wang, Y., Wang, S., and Ye, J. (2023). Automatic Diagnosis of Infectious Keratitis Based on Slit Lamp Images Analysis. J. Pers. Med., 13.","DOI":"10.3390\/jpm13030519"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"22642","DOI":"10.1038\/s41598-021-02138-w","article-title":"Determination of probability of causative pathogen in infectious keratitis using deep learning algorithm of slit-lamp images","volume":"11","author":"Koyama","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Sajeev, S., and Senthil, M.P. (2021, January 1\u20135). Classifying infective keratitis using a deep learning approach. Proceedings of the 2021 Australasian Computer Science Week Multiconference, Dunedin, New Zealand.","DOI":"10.1145\/3437378.3437388"},{"key":"ref_38","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. Inf."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"100119","DOI":"10.1016\/j.xops.2022.100119","article-title":"Image-based differentiation of bacterial and fungal keratitis using deep convolutional neural networks","volume":"2","author":"Redd","year":"2022","journal-title":"Ophthalmol. Sci."},{"key":"ref_40","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_41","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_42","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1167\/tvst.12.1.12","article-title":"Deep Convolutional Neural Networks Detect no Morphological Differences Between Culture-Positive and Culture-Negative Infectious Keratitis Images","volume":"12","author":"Kogachi","year":"2023","journal-title":"Transl. Vis. Sci. Technol."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"483","DOI":"10.1007\/s10792-019-01209-2","article-title":"The use of in vivo confocal microscopy to track treatment success in fungal keratitis and to differentiate between Fusarium and Aspergillus keratitis","volume":"40","author":"Tabatabaei","year":"2020","journal-title":"Int. Ophthalmol."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"13449","DOI":"10.1109\/ACCESS.2018.2808941","article-title":"Hyphae detection in fungal keratitis images with adaptive robust binary pattern","volume":"6","author":"Wu","year":"2018","journal-title":"IEEE Access"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Liu, Z., Cao, Y., Li, Y., Xiao, X., Qiu, Q., Yang, M., Zhao, Y., and Cui, L. (2020). Automatic diagnosis of fungal keratitis using data augmentation and image fusion with deep convolutional neural network. Comput. Methods Programs Biomed., 187.","DOI":"10.1016\/j.cmpb.2019.105019"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"3667","DOI":"10.3934\/mbe.2021183","article-title":"Medical image management and analysis system based on web for fungal keratitis images","volume":"4","author":"Hou","year":"2021","journal-title":"Math. Biosci. Eng."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Zhang, X., Ding, G., Gao, C., Li, C., Hu, B., Zhang, C., and Wang, Q. (2020, January 22\u201324). Deep Learning for Three Types of Keratitis Classification based on Confocal Microscopy Images. Proceedings of the 2020 3rd International Conference on Signal Processing and Machine Learning, Beijing, China.","DOI":"10.1145\/3432291.3432310"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"2203","DOI":"10.1007\/s10792-022-02616-8","article-title":"An artificial intelligence approach to classify pathogenic fungal genera of fungal keratitis using corneal confocal microscopy images","volume":"43","author":"Tang","year":"2023","journal-title":"Int. Ophthalmol."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"8953","DOI":"10.1038\/s41598-023-35085-9","article-title":"Interpretable deep learning for diagnosis of fungal and acanthamoeba keratitis using in vivo confocal microscopy images","volume":"13","author":"Essalat","year":"2023","journal-title":"Sci. Rep."}],"container-title":["Life"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-1729\/13\/11\/2117\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T21:11:55Z","timestamp":1760130715000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-1729\/13\/11\/2117"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,26]]},"references-count":49,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2023,11]]}},"alternative-id":["life13112117"],"URL":"https:\/\/doi.org\/10.3390\/life13112117","relation":{},"ISSN":["2075-1729"],"issn-type":[{"value":"2075-1729","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,10,26]]}}}