Schoenberger SD, Kim SJ, Thorne JE, Mruthyunjaya P, Yeh S, Bakri SJ, et al. Diagnosis and treatment of acute retinal necrosis: a report by the American Academy of Ophthalmology. Ophthalmology. 2017;124(3):382–92.

Article  PubMed  Google Scholar 

Uruyama A, Yamada N, Sasaki T. Unilateral acute uveitis with periarteritis and detachment. Jpn J Clin Ophthalmol. 1971;25:607–19.

Google Scholar 

Henderly DE, Genstler AJ, Smith RE, Rao NA. Changing patterns of uveitis. Am J Ophthalmol. 1987;103(2):131–6.

Article  CAS  PubMed  Google Scholar 

Yang P, Zhang Z, Zhou H, Li B, Huang X, Gao Y, et al. Clinical patterns and characteristics of uveitis in a tertiary center for uveitis in China. Curr Eye Res. 2005;30(11):943–8.

Article  PubMed  Google Scholar 

Khairallah M, Yahia SB, Ladjimi A, Messaoud R, Zaouali S, Attia S, et al. Pattern of uveitis in a referral centre in Tunisia, North Africa. Eye (London). 2007;21(1):33–9.

Article  CAS  Google Scholar 

Grajewski RS, Caramoy A, Frank KF, Rubbert-Roth A, Fätkenheuer G, Kirchhof B, et al. Spectrum of uveitis in a German tertiary center: review of 474 consecutive patients. Ocul Immunol Inflamm. 2015;23(4):346–52.

Article  PubMed  Google Scholar 

Jones NP. The Manchester uveitis clinic: the first 3000 patients–epidemiology and casemix. Ocul Immunol Inflamm. 2015;23(2):118–26.

Article  PubMed  Google Scholar 

Llorenç V, Mesquida M, Sainz de la Maza M, Keller J, Molins B, Espinosa G, et al. Epidemiology of uveitis in a Western urban multiethnic population. The challenge of globalization. Acta Ophthalmol. 2015;93(6):561–7.

Article  PubMed  Google Scholar 

Winterhalter S, Stuebiger N, Maier AK, Pleyer U, Heiligenhaus A, Mackensen F, et al. Acute retinal necrosis: diagnostic and treatment strategies in Germany. Ocul Immunol Inflamm. 2016;24(5):537–43.

Article  PubMed  Google Scholar 

Cochrane TF, Silvestri G, McDowell C, Foot B, McAvoy CE. Acute retinal necrosis in the United Kingdom: results of a prospective surveillance study. Eye (Lond). 2012;26(3):370–7; quiz 378.

Article  CAS  PubMed Central  Google Scholar 

Muthiah MN, Michaelides M, Child CS, Mitchell SM. Acute retinal necrosis: a national population-based study to assess the incidence, methods of diagnosis, treatment strategies and outcomes in the UK. Br J Ophthalmol. 2007;91(11):1452–5.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Hillenkamp J, Nölle B, Bruns C, Rautenberg P, Fickenscher H, Roider J. Acute retinal necrosis: clinical features, early vitrectomy, and outcomes. Ophthalmology. 2009;116(10):1971–5.e2.

Article  PubMed  Google Scholar 

Meghpara B, Sulkowski G, Kesen MR, Tessler HH, Goldstein DA. Long-term follow-up of acute retinal necrosis. Retina. 2010;30(5):795–800.

Article  PubMed  Google Scholar 

Baltinas J, Lightman S, Tomkins-Netzer O. Comparing treatment of acute retinal necrosis with either oral valacyclovir or intravenous acyclovir. Am J Ophthalmol. 2018;188:173–80.

Article  CAS  PubMed  Google Scholar 

Holland GN. Standard diagnostic criteria for the acute retinal necrosis syndrome. Executive Committee of the American Uveitis Society. Am J Ophthalmol. 1994;117(5):663–7.

Article  CAS  PubMed  Google Scholar 

Wong R, Pavesio CE, Laidlaw DA, Williamson TH, Graham EM, Stanford MR. Acute retinal necrosis: the effects of intravitreal foscarnet and virus type on outcome. Ophthalmology. 2010;117(3):556–60.

Article  PubMed  Google Scholar 

Sugita S, Shimizu N, Watanabe K, Mizukami M, Morio T, Sugamoto Y, et al. Use of multiplex PCR and real-time PCR to detect human herpes virus genome in ocular fluids of patients with uveitis. Br J Ophthalmol. 2008;92(7):928–32.

Article  CAS  PubMed  Google Scholar 

Pendergast SD, Werner J, Drevon A, Wiedbrauk DL. Absence of herpesvirus DNA by polymerase chain reaction in ocular fluids obtained from immunocompetent patients. Retina. 2000;20(4):389–93.

Article  CAS  PubMed  Google Scholar 

Takase H, Okada AA, Goto H, Mizuki N, Namba K, Ohguro N, et al. Development and validation of new diagnostic criteria for acute retinal necrosis. Jpn J Ophthalmol. 2015;59(1):14–20.

Article  CAS  PubMed  Google Scholar 

Jabs DA, Belfort R Jr, Bodaghi B, Graham E, Holland GN, Lightman SL, et al. Classification criteria for acute retinal necrosis syndrome. Am J Ophthalmol. 2021;228:237–44.

Article  Google Scholar 

Peng Y, Dharssi S, Chen Q, Keenan TD, Agrón E, Wong WT, et al. DeepSeeNet: a deep learning model for automated classification of patient-based age-related macular degeneration severity from color fundus photographs. Ophthalmology. 2019;126(4):565–75.

Article  PubMed  Google Scholar 

Grassmann F, Mengelkamp J, Brandl C, Harsch S, Zimmermann ME, Linkohr B, et al. A deep learning algorithm for prediction of age-related eye disease study severity scale for age-related macular degeneration from color fundus photography. Ophthalmology. 2018;125(9):1410–20.

Article  PubMed  Google Scholar 

Gulshan V, Peng L, Coram M, Stumpe MC, Wu D, Narayanaswamy A, et al. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA. 2016;316(22):2402–10.

Article  PubMed  Google Scholar 

Xu K, Huang S, Yang Z, Zhang Y, Fang Y, Zheng G, et al. Automatic detection and differential diagnosis of age-related macular degeneration from color fundus photographs using deep learning with hierarchical vision transformer. Comput Biol Med. 2023;167:107616.

Article  PubMed  Google Scholar 

Yang Z, Zhang Y, Xu K, Sun J, Wu Y, Zhou M. DeepDrRVO: a GAN-auxiliary two-step masked transformer framework benefits early recognition and differential diagnosis of retinal vascular occlusion from color fundus photographs. Comput Biol Med. 2023;163:107148.

Article  PubMed  Google Scholar 

Lei B, Zhou M, Wang Z, Chang Q, Xu G, Jiang R. Ultra-wide-field fundus imaging of acute retinal necrosis: clinical characteristics and visual significance. Eye (Lond). 2020;34(5):864–72.

Article  Google Scholar 

Liu Z, Lin Y, Cao Y, Hu H, Wei Y, Zhang Z, et al. Swin transformer: hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE/CVF international conference on computer vision. 2021. p. 10012–22.

Alowais SA, Alghamdi SS, Alsuhebany N, Alqahtani T, Alshaya AI, Almohareb SN, et al. Revolutionizing healthcare: the role of artificial intelligence in clinical practice. BMC Med Educ. 2023;23(1):689.

Article  PubMed  PubMed Central  Google Scholar 

Tran KA, Kondrashova O, Bradley A, Williams ED, Pearson JV, Waddell N. Deep learning in cancer diagnosis, prognosis and treatment selection. Genome Med. 2021;13(1):152.

Article  PubMed  PubMed Central  Google Scholar 

Zhang Y, Yan C, Yang Z, Zhou M, Sun J. Multi-omics deep-learning prediction of homologous recombination deficiency-like phenotype improved risk stratification and guided therapeutic decisions in gynecological cancers. IEEE J Biomed Health Inform. 2023;PP. https://doi.org/10.1109/JBHI.2023.3308440.

Zhang Z, Chen H, Yan D, Chen L, Sun J, Zhou M. Deep learning identifies a T-cell exhaustion-dependent transcriptional signature for predicting clinical outcomes and response to immune checkpoint blockade. Oncogenesis. 2023;12(1):37.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Yang Z, Zhang Y, Zhuo L, Sun K, Meng F, Zhou M, et al. Prediction of prognosis and treatment response in ovarian cancer patients from histopathology images using graph deep learning: a multicenter retrospective study. Eur J Cancer. 2024;199:113532.

Article  CAS  PubMed  Google Scholar 

Zhang Y, Yang Z, Chen R, Zhu Y, Liu L, Dong J, et al. Histopathology images-based deep learning prediction of prognosis and therapeutic response in small cell lung cancer. NPJ Digit Med. 2024;7(1):15.

Article  PubMed  PubMed Central  Google Scholar 

Ruamviboonsuk P, Tiwari R, Sayres R, Nganthavee V, Hemarat K, Kongprayoon A, et al. Real-time diabetic retinopathy screening by deep learning in a multisite national screening programme: a prospective interventional cohort study. Lancet Digit Health. 2022;4(4):e235–44.

Article  CAS  PubMed