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For: Lv J, Zhang K, Chen Q, Chen Q, Huang W, Cui L, Li M, Li J, Chen L, Shen C, Yang Z, Bei Y, Li L, Wu X, Zeng S, Xu F, Lin H. Deep learning-based automated diagnosis of fungal keratitis with in vivo confocal microscopy images. Ann Transl Med 2020;8:706. [PMID: 32617326 DOI: 10.21037/atm.2020.03.134] [Cited by in Crossref: 11] [Cited by in F6Publishing: 12] [Article Influence: 3.7] [Reference Citation Analysis]
Number Citing Articles
1 Kv R, Prasad K, Peralam Yegneswaran P. Segmentation and Classification Approaches of Clinically Relevant Curvilinear Structures: A Review. J Med Syst 2023;47:40. [PMID: 36971852 DOI: 10.1007/s10916-023-01927-2] [Reference Citation Analysis]
2 Xu Z, Xu J, Shi C, Xu W, Jin X, Han W, Jin K, Grzybowski A, Yao K. Artificial Intelligence for Anterior Segment Diseases: A Review of Potential Developments and Clinical Applications. Ophthalmol Ther 2023. [PMID: 36884203 DOI: 10.1007/s40123-023-00690-4] [Reference Citation Analysis]
3 Ting DSJ, Deshmukh R, Ting DSW, Ang M. Big data in corneal diseases and cataract: Current applications and future directions. Front Big Data 2023;6:1017420. [PMID: 36818823 DOI: 10.3389/fdata.2023.1017420] [Reference Citation Analysis]
4 Tang N, Huang G, Lei D, Jiang L, Chen Q, He W, Tang F, Hong Y, Lv J, Qin Y, Lin Y, Lan Q, Qin Y, Lan R, Pan X, Li M, Xu F, Lu P. An artificial intelligence approach to classify pathogenic fungal genera of fungal keratitis using corneal confocal microscopy images. Int Ophthalmol 2023. [PMID: 36595127 DOI: 10.1007/s10792-022-02616-8] [Reference Citation Analysis]
5 Zhang Z, Wang Y, Zhang H, Samusak A, Rao H, Xiao C, Abula M, Cao Q, Dai Q. Artificial intelligence-assisted diagnosis of ocular surface diseases. Front Cell Dev Biol 2023;11:1133680. [PMID: 36875760 DOI: 10.3389/fcell.2023.1133680] [Reference Citation Analysis]
6 Ji Y, Liu S, Hong X, Lu Y, Wu X, Li K, Li K, Liu Y. Advances in artificial intelligence applications for ocular surface diseases diagnosis. Front Cell Dev Biol 2022;10:1107689. [PMID: 36605721 DOI: 10.3389/fcell.2022.1107689] [Reference Citation Analysis]
7 Li J, Wang S, Hu S, Sun Y, Wang Y, Xu P, Ye J. Class-Aware Attention Network for infectious keratitis diagnosis using corneal photographs. Comput Biol Med 2022;151:106301. [PMID: 36403354 DOI: 10.1016/j.compbiomed.2022.106301] [Reference Citation Analysis]
8 Kang L, Ballouz D, Woodward MA. Artificial intelligence and corneal diseases. Curr Opin Ophthalmol 2022. [PMID: 35819899 DOI: 10.1097/ICU.0000000000000885] [Reference Citation Analysis]
9 Kim CB, Armstrong GW. Characterizing Infectious Keratitis Using Artificial Intelligence. Int Ophthalmol Clin 2022;62:41-53. [PMID: 35325909 DOI: 10.1097/IIO.0000000000000405] [Reference Citation Analysis]
10 Bakken IM, Jackson CJ, Utheim TP, Villani E, Hamrah P, Kheirkhah A, Nielsen E, Hau S, Lagali NS. The use of in vivo confocal microscopy in fungal keratitis - Progress and challenges. Ocul Surf 2022:S1542-0124(22)00016-7. [PMID: 35278721 DOI: 10.1016/j.jtos.2022.03.002] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
11 Jiang J, Liu W, Gong J, Pei M. A Two-stage Algorithm for Automatic Diagnosis of Keratitis. 2022 4th International Conference on Natural Language Processing (ICNLP) 2022. [DOI: 10.1109/icnlp55136.2022.00009] [Reference Citation Analysis]
12 Xu F, Jiang L, He W, Huang G, Hong Y, Tang F, Lv J, Lin Y, Qin Y, Lan R, Pan X, Zeng S, Li M, Chen Q, Tang N. The Clinical Value of Explainable Deep Learning for Diagnosing Fungal Keratitis Using in vivo Confocal Microscopy Images. Front Med (Lausanne) 2021;8:797616. [PMID: 34970572 DOI: 10.3389/fmed.2021.797616] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 4.0] [Reference Citation Analysis]
13 Sharma A, Lakhnotra A, Manhas J, Padha D. Deep Learning Based Classification of Microscopic Fungal Images. Rising Threats in Expert Applications and Solutions 2022. [DOI: 10.1007/978-981-19-1122-4_21] [Reference Citation Analysis]
14 Treder M, Eter N. Anwendungsmöglichkeiten von „Künstlicher Intelligenz“ und „Big Data“ in der ophthalmologischen Diagnostik. Der Nuklearmediziner 2021;44:284-288. [DOI: 10.1055/a-1232-3629] [Reference Citation Analysis]
15 Koo T, Kim MH, Jue MS. Automated detection of superficial fungal infections from microscopic images through a regional convolutional neural network. PLoS One 2021;16:e0256290. [PMID: 34403443 DOI: 10.1371/journal.pone.0256290] [Cited by in F6Publishing: 2] [Reference Citation Analysis]
16 Song A, Deshmukh R, Lin H, Ang M, Mehta JS, Chodosh J, Said DG, Dua HS, Ting DSJ. Post-keratoplasty Infectious Keratitis: Epidemiology, Risk Factors, Management, and Outcomes. Front Med (Lausanne) 2021;8:707242. [PMID: 34307431 DOI: 10.3389/fmed.2021.707242] [Cited by in Crossref: 4] [Cited by in F6Publishing: 5] [Article Influence: 2.0] [Reference Citation Analysis]
17 Rampat R, Deshmukh R, Chen X, Ting DSW, Said DG, Dua HS, Ting DSJ. Artificial Intelligence in Cornea, Refractive Surgery, and Cataract: Basic Principles, Clinical Applications, and Future Directions. Asia Pac J Ophthalmol (Phila) 2021;10:268-81. [PMID: 34224467 DOI: 10.1097/APO.0000000000000394] [Cited by in Crossref: 8] [Cited by in F6Publishing: 10] [Article Influence: 4.0] [Reference Citation Analysis]
18 Li Z, Jiang J, Chen K, Chen Q, Zheng Q, Liu X, Weng H, Wu S, Chen W. Preventing corneal blindness caused by keratitis using artificial intelligence. Nat Commun 2021;12:3738. [PMID: 34145294 DOI: 10.1038/s41467-021-24116-6] [Cited by in Crossref: 8] [Cited by in F6Publishing: 9] [Article Influence: 4.0] [Reference Citation Analysis]
19 Xu F, Qin Y, He W, Huang G, Lv J, Xie X, Diao C, Tang F, Jiang L, Lan R, Cheng X, Xiao X, Zeng S, Chen Q, Cui L, Li M, Tang N. A deep transfer learning framework for the automated assessment of corneal inflammation on in vivo confocal microscopy images. PLoS One 2021;16:e0252653. [PMID: 34081736 DOI: 10.1371/journal.pone.0252653] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.5] [Reference Citation Analysis]