FLAXMAN S R, BOURNE R R, RESNIKOFF S, et al. Global causes of blindness and distance vision impairment 1990–2020: a systematic review and meta-analysis[J]. The lancet global health, 2017, 5(12): e1221–e1234.

Article  Google Scholar 

LAM D, RAO S K, RATRA V, et al. Cataract[J]. Nature reviews disease primers, 2015, 1(1): 1–15.

Article  Google Scholar 

DAY A C, FINDL O. Femtosecond laser-assisted vs conventional cataract surgery[J]. The lancet, 2020, 395(10219): 170–171.

Article  Google Scholar 

HE Y, ZHANG R, ZHANG C, et al. Clinical outcome of phacoemulsification combined with intraocular lens implantation for primary angle closure/glaucoma (PAC/PACG) with cataract[J]. American journal of translational research, 2021, 13(12): 13498.

Google Scholar 

SCHWEITZER C, BREZIN A, COCHENER B, et al. Femtosecond laser-assisted versus phacoemulsification cataract surgery (FEMCAT): a multicentre participant-masked randomised superiority and cost-effectiveness trial[J]. The lancet, 2020, 395(10219): 212–224.

Article  Google Scholar 

World Health Organization. World report on vision[R]. Geneva: WHO, 2019.

Google Scholar 

RESNIKOFF S, FELCH W, GAUTHIER T M, et al. The number of ophthalmologists in practice and training worldwide: a growing gap despite more than 200 000 practitioners[J]. British journal of ophthalmology, 2012, 96(6): 783–787.

Article  Google Scholar 

SHEN D, WU G, SUK H I. Deep learning in medical image analysis[J]. Annual review of biomedical engineering, 2017, 19: 221.

Article  Google Scholar 

KHOJASTE-SARAKHSI M, HAGHIGHI S S, GHOMI S F, et al. Deep learning for Alzheimer’s disease diagnosis: a survey[J]. Artificial intelligence in medicine, 2022: 102332.

CHEN S, QIU C, YANG W, et al. Combining edge guidance and feature pyramid for medical image segmentation[J]. Biomedical signal processing and control, 2022, 78: 103960.

Article  Google Scholar 

LOTTER W, DIAB A R, HASLAM B, et al. Robust breast cancer detection in mammography and digital breast tomosynthesis using an annotation-efficient deep learning approach[J]. Nature medicine, 2021, 27(2): 244–249.

Article  Google Scholar 

GRZYBOWSKI A, BRONA P, LIM G, et al. Artificial intelligence for diabetic retinopathy screening: a review[J]. Eye, 2020, 34(3): 451–460.

Article  Google Scholar 

YU K H, BEAM A L, KOHANE I S. Artificial intelligence in healthcare[J]. Nature biomedical engineering, 2018, 2(10): 719–731.

Article  Google Scholar 

SENGUPTA S, SINGH A, LEOPOLD H A, et al. Ophthalmic diagnosis using deep learning with fundus images-a critical review[J]. Artificial intelligence in medicine, 2020, 102: 101758.

Article  Google Scholar 

LI Z, JIANG J, CHEN K, et al. Preventing corneal blindness caused by keratitis using artificial intelligence[J]. Nature communications, 2021, 12(1): 1–12.

ADS  Google Scholar 

LI Z, QIANG W, CHEN H, et al. Artificial intelligence to detect malignant eyelid tumors from photographic images[J]. NPJ digital medicine, 2022, 5(1): 1–9.

Article  Google Scholar 

ZHANG H, NIU K, XIONG Y, et al. Automatic cataract grading methods based on deep learning[J]. Computer methods and programs in biomedicine, 2019, 182: 104978.

Article  Google Scholar 

JUNAYED M S, ISLAM M B, SADEGHZADEH A, et al. CataractNet: an automated cataract detection system using deep learning for fundus images[J]. IEEE access, 2021, 9: 128799–128808.

Article  Google Scholar 

XU X, ZHANG L, LI J, et al. A hybrid global-local representation CNN model for automatic cataract grading[J]. IEEE journal of biomedical and health informatics, 2020, 24(2): 556–567.

Article  Google Scholar 

BLOICE M D, ROTH P M, HOLZINGER A. Biomedical image augmentation using augmentor[J]. Bioinformatics, 2019, 35(21): 4522–4524.

Article  Google Scholar 

HUANG G, LIU Z, VAN DER MAATEN L, et al. Densely connected convolutional networks[C]//Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition, July 21–26, 2017, Honolulu, USA. New York: IEEE, 2017: 4700–4708.

Google Scholar 

WOO S, PARK J, LEE J Y, et al. CBAM: convolutional block attention module[C]//Proceedings of the European Conference on Computer Vision, September 8–14, 2018, Munich, Germany. Berlin: Springer, 2018, 11211: 3–19.

Google Scholar 

CHOLLET F. Xception: deep learning with depthwise separable convolutions[C]//Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition, July 21–26, 2017, Honolulu, USA. New York: IEEE, 2017: 1800–1807.

Google Scholar 

JIANG J, WANG L, FU H, et al. Automatic classification of heterogeneous slit-illumination images using an ensemble of cost-sensitive convolutional neural networks[J]. Annals of translational medicine, 2021, 9(7): 550.

Article  Google Scholar