1. Liu Z, et al. Efficacy and Safety of Houttuynia Eye Drops Atomization Treatment for Meibomian Gland Dysfunction-Related Dry Eye Disease: A Randomized, Double-Blinded, Placebo-Controlled Clinical Trial. J Clin Med 9, (2020).
2. Lemp MA, Crews LA, Bron AJ, Foulks GN, Sullivan BD. Distribution of aqueous-deficient and evaporative dry eye in a clinic-based patient cohort: a retrospective study. Cornea 31, 472-478 (2012).
3. Kiyat P, Palamar M, Gerceker Turk B, Yagci A. Evaluation of dry eye and Meibomian gland dysfunction in female androgenetic alopecia patients. Int Ophthalmol, (2021).
4. Pedrotti E, et al. In Vivo Confocal Microscopy of the Corneal-Conjunctival Transition in the Evaluation of Epithelial Renewal after SLET. J Clin Med 9, (2020).
5. Wei S, Ren X, Wang Y, Chou Y, Li X. Therapeutic Effect of Intense Pulsed Light (IPL) Combined with Meibomian Gland Expression (MGX) on Meibomian Gland Dysfunction (MGD). J Ophthalmol 2020, 3684963 (2020).
6. Ibrahim OM, et al. The efficacy, sensitivity, and specificity of in vivo laser confocal microscopy in the diagnosis of meibomian gland dysfunction. Ophthalmology 117, 665-672 (2010).
7. Jin C, et al. Development and evaluation of an artificial intelligence system for COVID-19 diagnosis. Nat Commun 11, 5088 (2020).
8. Ting DSW, et al. Development and Validation of a Deep Learning System for Diabetic Retinopathy and Related Eye Diseases Using Retinal Images From Multiethnic Populations With Diabetes. JAMA 318, 2211-2223 (2017).
9. HONG,X. Expert consensus on diagnosis and treatment of meibomian gland dysfunction in China. Chin J Ophthalmol 53,657-661 (2017).
10. Llorella FR, Patow G, Azorin JM. Convolutional neural networks and genetic algorithm for visual imagery classification. Phys Eng Sci Med 43, 973-983 (2020).
11. Zhang X, Jiang L, Yang D, Yan J, Lu X. Correction to: Urine Sediment Recognition Method Based on Multi-View Deep Residual Learning in Microscopic Image. J Med Syst 44, 84 (2020).
12. Devalla SK, et al. Glaucoma management in the era of artificial intelligence. Br J Ophthalmol 104, 301-311 (2020).
13. Moraru AD, Costin D, Moraru RL, Branisteanu DC. Artificial intelligence and deep learning in ophthalmology - present and future (Review). Exp Ther Med 20, 3469-3473 (2020).
14. Long E, et al. An artificial intelligence platform for the multihospital collaborative management of congenital cataracts. Nature Biomedical Engineering 1, (2017).
15. Panda R, Puhan NB, Rao A, Mandal B, Padhy D, Panda G. Deep convolutional neural network-based patch classification for retinal nerve fiber layer defect detection in early glaucoma. J Med Imaging (Bellingham) 5, 044003 (2018).
16. Voets M, Mollersen K, Bongo LA. Reproduction study using public data of: Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. PLoS One 14, e0217541 (2019).
17. Li Z, He Y, Keel S, Meng W, Chang RT, He M. Efficacy of a Deep Learning System for Detecting Glaucomatous Optic Neuropathy Based on Color Fundus Photographs. Ophthalmology 125, 1199-1206 (2018).
18. Torsten P, Jan F, Arthur K, Henner S. Using Local Convolutional Neural Networks for Genomic Prediction. Frontiers in Genetics, (2020).
19. Woldegiorgis S, Enqvist A, Baciak J. ResNet and CycleGAN for pulse shape discrimination of He-4 detector pulses: Recovering pulses conventional algorithms fail to label unanimously. Appl Radiat Isot 176, 109819 (2021).
20. Belmonte C, et al. TFOS DEWS II pain and sensation report. Ocul Surf 15, 404-437 (2017).
21. Ucar IC, Esen F, Turhan SA, Oguz H, Ulasoglu HC, Aykut V. Corneal neuropathic pain in irritable bowel syndrome: clinical findings and in vivo corneal confocal microscopy. Graefes Arch Clin Exp Ophthalmol, (2021).
22. Kobayashi A, Yoshita T, Sugiyama K. In vivo findings of the bulbar/palpebral conjunctiva and presumed meibomian glands by laser scanning confocal microscopy. Cornea 24, 985-988 (2005).
23. F F, et al. Facial Seborrheic Keratosis with Unusual Dermoscopic Patterns can be differentiated from other skin malignancies by In Vivo Reflectance Confocal Microscopy. Journal of the European Academy of Dermatology and Venereology : JEADV, (2021).
24. Shengnan C, Yueqi Y, Jin C, Lin Y, Xinghua W, Fagang J. In vivo confocal microscopy assessment of meibomian glands microstructure in patients with Graves' orbitopathy. BMC ophthalmology 21, (2021).
25. Szalai E, Szucs G, Szamosi S, Aszalos Z, Afra I, Kemeny-Beke A. An in vivo confocal microscopy study of corneal changes in patients with systemic sclerosis. Sci Rep 11, 11111 (2021).
26. Wang J, Yeh TN, Chakraborty R, Yu SX, Lin MC. A Deep Learning Approach for Meibomian Gland Atrophy Evaluation in Meibography Images. Transl Vis Sci Technol 8, 37 (2019).
27. Koh YW, Celik T, Lee HK, Petznick A, Tong L. Detection of meibomian glands and classification of meibography images. J Biomed Opt 17, 086008 (2012).
28. Maruoka S, et al. Deep Neural Network-Based Method for Detecting Obstructive Meibomian Gland Dysfunction With in Vivo Laser Confocal Microscopy. Cornea 39, 720-725 (2020).
29. Zhongwen L, et al. Preventing corneal blindness caused by keratitis using artificial intelligence. Nature communications 12, (2021).
30. Zhang K, et al. Deep-learning models for the detection and incidence prediction of chronic kidney disease and type 2 diabetes from retinal fundus images. Nat Biomed Eng 5, 533-545 (2021).
31. Yang HK, Kim YJ, Sung JY, Kim DH, Kim KG, Hwang J-M. Efficacy for Differentiating Nonglaucomatous Versus Glaucomatous Optic Neuropathy Using Deep Learning Systems. American Journal of Ophthalmology 216, (2020).
32. Khan SMA, Jens H, Stefan S, E SM, Philipp S. Deep learning-based automatic meibomian gland segmentation and morphology assessment in infrared meibography. Scientific reports 11, (2021).
33. Daisuke N, et al. Automated detection of a nonperfusion area caused by retinal vein occlusion in optical coherence tomography angiography images using deep learning. PloS one 14, (2019).