Research progress on the application of deep learning in the whole process management of cataract_West China Medical Journal

Authors：

LU Xiangheng ¹ , XIAO Xiao ² , YIN Wen ^1,3 , WANG Song ¹ ,  LIU Chunling ¹

1. Department of Ophthalmology, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, P. R. China;
2. Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, P. R. China;
3. Department of Ophthalmology, Sichuan Provincial Bayi Rehabilitation Center (Sichuan Provincial Rehabilitation Hospital), Chengdu, Sichuan 61135, P. R. China;

Corresponding?author：

LIU Chunling, Email: liuchunling@wchscu.cn

Keywords：

Artificial intelligence; deep learning; cataract; intraocular lens; whole process management

DOI：

10.7507/1002-0179.202508282

Video：

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Abstract Full text Figures/Tables Video References Cited by

In recent years, with the help of artificial intelligence technology such as deep learning (DL), ophthalmic diagnosis and treatment technology has been continuously developed and improved. This article reviews the DL-assisted cataract screening and diagnosis, surgical navigation and risk prediction, intraocular lens function optimization and postoperative complications prediction, aiming to summarize the research progress of various DL models currently applied in the field of cataract diagnosis and treatment, and explore the challenges and future development direction of DL application in the whole process management of cataract.

1.	黃可馨, 陳慶鋒. 1990-2021 年中國白內障疾病負擔變化趨勢分析及發展趨勢預測. 數理醫藥學雜志, 2024, 37(12): 888-898.
2.	Flaxman SR, Bourne RRA, Resnikoff S, et al. Global causes of blindness and distance vision impairment 1990-2020: a systematic review and meta-analysis. Lancet Glob Health, 2017, 5(12): e1221-e1234.
3.	Deng Y, Yang D, Yu JM, et al. The association of socioeconomic status with the burden of cataract-related blindness and the effect of ultraviolet radiation exposure: an ecological study. Biomed Environ Sci, 2021, 34(2): 101-109.
4.	Gutierrez L, Lim JS, Foo LL, et al. Application of artificial intelligence in cataract management: current and future directions. Eye Vis (Lond), 2022, 9(1): 3.
5.	Lu S, Ba L, Wang J, et al. Deep learning-driven approach for cataract management: towards precise identification and predictive analytics. Front Cell Dev Biol, 2025, 13: 1611216.
6.	Zhang X, Hu Y, Xiao Z, et al. Machine learning for cataract classification/grading on ophthalmic imaging modalities: a survey. MIR, 2022, 19(3): 184-208.
7.	Wang J, Wang K, Yu Y, et al. Self-improving generative foundation model for synthetic medical image generation and clinical applications. Nat Med, 2025, 31(2): 609-617.
8.	Long E, Lin H, Liu Z, et al. An artificial intelligence platform for the multihospital collaborative management of congenital cataracts. Nat Biomed Eng, 2017, 1(2): 24.
9.	Zhang H, Niu K, Xiong Y, et al. Automatic cataract grading methods based on deep learning. Comput Methods Programs Biomed, 2019, 182: 104978.
10.	Zhou Y, Li G, Li H. Automatic cataract classification using deep neural network with discrete state transition. IEEE Trans Med Imaging, 2020, 39(2): 436-446.
11.	Liu X, Jiang J, Zhang K, et al. Localization and diagnosis framework for pediatric cataracts based on slit-lamp images using deep features of a convolutional neural network. PLoS One, 2017, 12(3): e0168606.
12.	Xu C, Zhu X, He W, et al. Fully deep learning for slit-lamp photo based nuclear cataract grading. Lect Notes Comput Sci, 2019: 513-521.
13.	Wu X, Huang Y, Liu Z, et al. Universal artificial intelligence platform for collaborative management of cataracts. Br J Ophthalmol, 2019, 103(11): 1553-1560.
14.	Zhang L, Li J, Zhang L, et al. Automatic cataract detection and grading using deep convolutional neural network. Calabria: IEEE 14th International Conference on Networking, Sensing and Control (ICNSC), 2017: 60-65.
15.	Dong Y, Zhang Q, Qiao Z, et al. Classification of Cataract Fundus Image Based on Deep Learning. Venice: IEEE International Conference on Imaging Systems and Techniques (IST), 2017: 127-131.
16.	Xu X, Zhang L, Li J, et al. A hybrid global-local representation CNN model for automatic cataract grading. IEEE J Biomed Health Inform, 2020, 24(2): 556-567.
17.	Xiao Z, Zhang X, Higashita R, et al. A 3D CNN-based multi-task learning for cataract screening and left and right eye classification on 3D AS-OCT images. Macau: the 3rd International Conference on Intelligent Medicine and Health, 2021: 1-7.
18.	Zhang X, Xiao Z, Li X, et al. Mixed pyramid attention network for nuclear cataract classification based on anterior segment OCT images. Health Inf Sci Syst, 2022, 10(1): 3.
19.	Deng L, Jiao P, Pei J, et al. GXNOR-net: training deep neural networks with ternary weights and activations without full-precision memory under a unified discretization framework. Neural Netw, 2018, 100: 49-58.
20.	Yin P, Tan M, Min H, et al. Automatic segmentation of cortex and nucleus in anterior segment OCT images. Lect Notes Comput Sci, 2018: 269-276.
21.	Zhang S, Yan Y, Yin P, et al. Guided M-net for high-resolution biomedical image segmentation with weak boundaries. Lect Notes Comput Sci, 2019: 43-51.
22.	Cao G, Zhao W, Higashita R, et al. An efficient lens structures segmentation method on AS-OCT images. Montreal: 42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 43rd Annual Conference of the Canadian Medical and Biological Engineering Society, 2020: 1646-1649.
23.	Ghamsarian N, Taschwer M, Sznitman R, et al. DeepPyramid: enabling pyramid view and deformable pyramid reception for semantic segmentation in cataract surgery videos. Lect Notes Comput Sci, 2022: 276-286.
24.	Fang X, Wang Y, Li X, et al. A two-stage multi-scale attention-based network for weakly supervised cataract fundus image enhancement. Sci Rep, 2025, 15(1): 27610.
25.	Caixinha M, Jesus DA, Velte E, et al. Using ultrasound backscattering signals and Nakagami statistical distribution to assess regional cataract hardness. IEEE Trans Biomed Eng, 2014, 61(12): 2921-2929.
26.	Touvron H, Bojanowski P, Caron M, et al. ResMLP: feedforward networks for image classification with data-efficient training. IEEE Trans Pattern Anal Mach Intell, 2023, 45(4): 5314-5321.
27.	Kumar D, Verma C, Illés Z. GLAAM and GLAAI: pioneering attention models for robust automated cataract detection. Comput Meth Prog Bio, 2025, 7(6): 100182.
28.	Liu X, Song W, Zhang Y, et al. Complementary discriminative region feature fusion network with ordinal regression for fine-grained nuclear cataract grading in AS-OCT images. Biomed Signal Proces, 2026: 112.
29.	Zhou Q, Zou H, Jiang H, et al. Incomplete multimodal learning for visual acuity prediction after cataract surgery using masked self-attention. Lect Notes Comput Sci, 2023: 735-744.
30.	Rombach R, Blattmann A, Lorenz D, et al. High-resolution image synthesis with latent diffusion models. Los Alamos: arXiv, 2021: 10674-10685.
31.	Lu Q, Wei L, He W, et al. Lens opacities classification system iii-based artificial intelligence program for automatic cataract grading. J Cataract Refract Surg, 2022, 48(5): 528-534.
32.	Son KY, Ko J, Kim E, et al. Deep learning-based cataract detection and grading from slit-lamp and retro-illumination photographs: model development and validation study. Ophthalmol Sci, 2022, 2(2): 100147.
33.	P L L, Vaddi R, Elish MO, et al. CSDNet: a novel deep learning framework for improved cataract state detection. Diagnostics (Basel), 2024, 14(10): 983.
34.	Gan F, Liu H, Qin WG, et al. Application of artificial intelligence for automatic cataract staging based on anterior segment images: comparing automatic segmentation approaches to manual segmentation. Front Neurosci, 2023, 17: 1182388.
35.	Zhao J, Wan C, Li J, et al. NCME-net: nuclear cataract mask encoder network for intelligent grading using self-supervised learning from anterior segment photographs. Heliyon, 2024, 10(14): e34726.
36.	Wang J, Xu Z, Zheng W, et al. A transformer-based knowledge distillation network for cortical cataract grading. IEEE Trans Med Imaging, 2024, 43(3): 1089-1101.
37.	Vivino MA, Mahurkar A, Trus B, et al. Quantitative analysis of retroillumination images. Eye (Lond), 1995, 9(1): 77-84.
38.	Wu X, Xu D, Ma T, et al. Artificial intelligence model for antiinterference cataract automatic diagnosis: a diagnostic accuracy study. Front Cell Dev Biol, 2022, 10: 906042.
39.	Cao L, Li H, Zhang Y, et al. Hierarchical method for cataract grading based on retinal images using improved Haar wavelet. Inform Fusion, 2020, 53(34): 196-208.
40.	Yang CN, Hsieh YT, Yeh HH, et al. Prediction of visual acuity after cataract surgery by deep learning methods using clinical information and color fundus photography. Curr Eye Res, 2025, 50(3): 276-281.
41.	Mai ELC, Chen BH, Su TY. Innovative utilization of ultra-wide field fundus images and deep learning algorithms for screening high-risk posterior polar cataract. J Cataract Refract Surg, 2024, 50(6): 618-623.
42.	Panthier C, Zeboulon P, Rouger H, et al. CATALYZE: a deep learning approach for cataract assessment and grading on SS-OCT images. J Cataract Refract Surg, 2025, 51(3): 222-228.
43.	Xiao Z, Zhang X, Zheng B, et al. Multi-style spatial attention module for cortical cataract classification in AS-OCT image with supervised contrastive learning. Comput Methods Programs Biomed, 2024, 244: 107958.
44.	Schwarzenbacher L, Seeb?ck P, Schartmüller D, et al. Automatic segmentation of intraocular lens, the retrolental space and Berger’s space using deep learning. Acta Ophthalmol, 2022, 100(8): e1611-e1616.
45.	Moriguchi T, Tabuchi H, Mao T, et al. Localization of intraocular lens position in anterior segment optical coherence tomography(AS-OCT) using deep learning. Invest Ophth Vis Sci, 2024, 65(7): 2.
46.	Soh ZD, Tan M, Nongpiur ME, et al. Deep learning-based quantification of anterior segment OCT parameters. Ophthalmol Sci, 2023, 4(1): 100360.
47.	Fernández-Vigo JI, Macarro-Merino A, De Moura-Ramos JJ, et al. Comparative study of the glistening between four intraocular lens models assessed by OCT and deep learning. J Cataract Refract Surg, 2024, 50(1): 37-42.
48.	Xin C, Bian GB, Zhang H, et al. Optical coherence tomography-based deep learning algorithm for quantification of the location of the intraocular lens. Ann Transl Med, 2020, 8(14): 872.
49.	Zhang X, Lv J, Zheng H, et al. Attention-based multi-model ensemble for automatic cataract detection in B-scan eye ultrasound images. Glasgow: International Joint Conference on Neural Networks (IJCNN), 2020: 9207696.
50.	Caixinha M, Amaro J, Santos M, et al. In-vivo automatic nuclear cataract detection and classification in an animal model by ultrasounds. IEEE Trans Biomed Eng, 2016, 63(11): 2326-2335.
51.	Wu H, Lv J, Wang J. Automatic cataract detection with multi-task learning. Shenzhen: International Joint Conference on Neural Networks (IJCNN), 2021: 1-8.
52.	Miao Q, Zhou S, Yang J, et al. Keypoint localization and parameter measurement in ultrasound biomicroscopy anterior segment images based on deep learning. Biomed Eng Online, 2025, 24(1): 53.
53.	Ting DSJ, Ang M, Mehta JS, et al. Artificial intelligence-assisted telemedicine platform for cataract screening and management: a potential model of care for global eye health. Br J Ophthalmol, 2019, 103(11): 1537-1538.
54.	Long E, Chen J, Wu X, et al. Artificial intelligence manages congenital cataract with individualized prediction and telehealth computing. NPJ Digit Med, 2020, 3: 112.
55.	Janti SS, Saluja R, Tiwari N, et al. Evaluation of the clinical impact of a smartphone application for cataract detection. Cureus, 2024, 16(10): e71467.
56.	Saqib SM, Iqbal M, Zubair Asghar M, et al. Cataract and glaucoma detection based on transfer learning using mobilenet. Heliyon, 2024, 10(17): e36759.
57.	Ueno Y, Oda M, Yamaguchi T, et al. Deep learning model for extensive smartphone-based diagnosis and triage of cataracts and multiple corneal diseases. Br J Ophthalmol, 2024, 108(10): 1406-1413.
58.	Ignatowicz AA, Marciniak T, Marciniak E. AI-powered mobile app for nuclear cataract detection. Sensors (Basel), 2025, 25(13): 3954.
59.	Ahuja AS, Paredes Iii AA, Eisel MLS, et al. Applications of artificial intelligence in cataract surgery: a review. Clin Ophthalmol, 2024, 18: 2969-2975.
60.	Yeh HH, Jain AM, Fox O, et al. PhacoTrainer: deep learning for cataract surgical videos to track surgical tools. Transl Vis Sci Technol, 2023, 12(3): 23.
61.	Mueller S, Sachdeva B, Prasad SN, et al. Phase recognition in manual small-incision cataract surgery with MS-TCN ^{+ +} on the novel SICS-105 dataset. Sci Rep, 2025, 15(1): 16886.
62.	Zeng Z, Giap BD, Kahana E, et al. Evaluation of methods for detection and semantic segmentation of the anterior capsulotomy in cataract surgery video. Clin Ophthalmol, 2024, 18: 647-657.
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1. 黃可馨, 陳慶鋒. 1990-2021 年中國白內障疾病負擔變化趨勢分析及發展趨勢預測. 數理醫藥學雜志, 2024, 37(12): 888-898.
2. Flaxman SR, Bourne RRA, Resnikoff S, et al. Global causes of blindness and distance vision impairment 1990-2020: a systematic review and meta-analysis. Lancet Glob Health, 2017, 5(12): e1221-e1234.
3. Deng Y, Yang D, Yu JM, et al. The association of socioeconomic status with the burden of cataract-related blindness and the effect of ultraviolet radiation exposure: an ecological study. Biomed Environ Sci, 2021, 34(2): 101-109.
4. Gutierrez L, Lim JS, Foo LL, et al. Application of artificial intelligence in cataract management: current and future directions. Eye Vis (Lond), 2022, 9(1): 3.
5. Lu S, Ba L, Wang J, et al. Deep learning-driven approach for cataract management: towards precise identification and predictive analytics. Front Cell Dev Biol, 2025, 13: 1611216.
6. Zhang X, Hu Y, Xiao Z, et al. Machine learning for cataract classification/grading on ophthalmic imaging modalities: a survey. MIR, 2022, 19(3): 184-208.
7. Wang J, Wang K, Yu Y, et al. Self-improving generative foundation model for synthetic medical image generation and clinical applications. Nat Med, 2025, 31(2): 609-617.
8. Long E, Lin H, Liu Z, et al. An artificial intelligence platform for the multihospital collaborative management of congenital cataracts. Nat Biomed Eng, 2017, 1(2): 24.
9. Zhang H, Niu K, Xiong Y, et al. Automatic cataract grading methods based on deep learning. Comput Methods Programs Biomed, 2019, 182: 104978.
10. Zhou Y, Li G, Li H. Automatic cataract classification using deep neural network with discrete state transition. IEEE Trans Med Imaging, 2020, 39(2): 436-446.
11. Liu X, Jiang J, Zhang K, et al. Localization and diagnosis framework for pediatric cataracts based on slit-lamp images using deep features of a convolutional neural network. PLoS One, 2017, 12(3): e0168606.
12. Xu C, Zhu X, He W, et al. Fully deep learning for slit-lamp photo based nuclear cataract grading. Lect Notes Comput Sci, 2019: 513-521.
13. Wu X, Huang Y, Liu Z, et al. Universal artificial intelligence platform for collaborative management of cataracts. Br J Ophthalmol, 2019, 103(11): 1553-1560.
14. Zhang L, Li J, Zhang L, et al. Automatic cataract detection and grading using deep convolutional neural network. Calabria: IEEE 14th International Conference on Networking, Sensing and Control (ICNSC), 2017: 60-65.
15. Dong Y, Zhang Q, Qiao Z, et al. Classification of Cataract Fundus Image Based on Deep Learning. Venice: IEEE International Conference on Imaging Systems and Techniques (IST), 2017: 127-131.
16. Xu X, Zhang L, Li J, et al. A hybrid global-local representation CNN model for automatic cataract grading. IEEE J Biomed Health Inform, 2020, 24(2): 556-567.
17. Xiao Z, Zhang X, Higashita R, et al. A 3D CNN-based multi-task learning for cataract screening and left and right eye classification on 3D AS-OCT images. Macau: the 3rd International Conference on Intelligent Medicine and Health, 2021: 1-7.
18. Zhang X, Xiao Z, Li X, et al. Mixed pyramid attention network for nuclear cataract classification based on anterior segment OCT images. Health Inf Sci Syst, 2022, 10(1): 3.
19. Deng L, Jiao P, Pei J, et al. GXNOR-net: training deep neural networks with ternary weights and activations without full-precision memory under a unified discretization framework. Neural Netw, 2018, 100: 49-58.
20. Yin P, Tan M, Min H, et al. Automatic segmentation of cortex and nucleus in anterior segment OCT images. Lect Notes Comput Sci, 2018: 269-276.
21. Zhang S, Yan Y, Yin P, et al. Guided M-net for high-resolution biomedical image segmentation with weak boundaries. Lect Notes Comput Sci, 2019: 43-51.
22. Cao G, Zhao W, Higashita R, et al. An efficient lens structures segmentation method on AS-OCT images. Montreal: 42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 43rd Annual Conference of the Canadian Medical and Biological Engineering Society, 2020: 1646-1649.
23. Ghamsarian N, Taschwer M, Sznitman R, et al. DeepPyramid: enabling pyramid view and deformable pyramid reception for semantic segmentation in cataract surgery videos. Lect Notes Comput Sci, 2022: 276-286.
24. Fang X, Wang Y, Li X, et al. A two-stage multi-scale attention-based network for weakly supervised cataract fundus image enhancement. Sci Rep, 2025, 15(1): 27610.
25. Caixinha M, Jesus DA, Velte E, et al. Using ultrasound backscattering signals and Nakagami statistical distribution to assess regional cataract hardness. IEEE Trans Biomed Eng, 2014, 61(12): 2921-2929.
26. Touvron H, Bojanowski P, Caron M, et al. ResMLP: feedforward networks for image classification with data-efficient training. IEEE Trans Pattern Anal Mach Intell, 2023, 45(4): 5314-5321.
27. Kumar D, Verma C, Illés Z. GLAAM and GLAAI: pioneering attention models for robust automated cataract detection. Comput Meth Prog Bio, 2025, 7(6): 100182.
28. Liu X, Song W, Zhang Y, et al. Complementary discriminative region feature fusion network with ordinal regression for fine-grained nuclear cataract grading in AS-OCT images. Biomed Signal Proces, 2026: 112.
29. Zhou Q, Zou H, Jiang H, et al. Incomplete multimodal learning for visual acuity prediction after cataract surgery using masked self-attention. Lect Notes Comput Sci, 2023: 735-744.
30. Rombach R, Blattmann A, Lorenz D, et al. High-resolution image synthesis with latent diffusion models. Los Alamos: arXiv, 2021: 10674-10685.
31. Lu Q, Wei L, He W, et al. Lens opacities classification system iii-based artificial intelligence program for automatic cataract grading. J Cataract Refract Surg, 2022, 48(5): 528-534.
32. Son KY, Ko J, Kim E, et al. Deep learning-based cataract detection and grading from slit-lamp and retro-illumination photographs: model development and validation study. Ophthalmol Sci, 2022, 2(2): 100147.
33. P L L, Vaddi R, Elish MO, et al. CSDNet: a novel deep learning framework for improved cataract state detection. Diagnostics (Basel), 2024, 14(10): 983.
34. Gan F, Liu H, Qin WG, et al. Application of artificial intelligence for automatic cataract staging based on anterior segment images: comparing automatic segmentation approaches to manual segmentation. Front Neurosci, 2023, 17: 1182388.
35. Zhao J, Wan C, Li J, et al. NCME-net: nuclear cataract mask encoder network for intelligent grading using self-supervised learning from anterior segment photographs. Heliyon, 2024, 10(14): e34726.
36. Wang J, Xu Z, Zheng W, et al. A transformer-based knowledge distillation network for cortical cataract grading. IEEE Trans Med Imaging, 2024, 43(3): 1089-1101.
37. Vivino MA, Mahurkar A, Trus B, et al. Quantitative analysis of retroillumination images. Eye (Lond), 1995, 9(1): 77-84.
38. Wu X, Xu D, Ma T, et al. Artificial intelligence model for antiinterference cataract automatic diagnosis: a diagnostic accuracy study. Front Cell Dev Biol, 2022, 10: 906042.
39. Cao L, Li H, Zhang Y, et al. Hierarchical method for cataract grading based on retinal images using improved Haar wavelet. Inform Fusion, 2020, 53(34): 196-208.
40. Yang CN, Hsieh YT, Yeh HH, et al. Prediction of visual acuity after cataract surgery by deep learning methods using clinical information and color fundus photography. Curr Eye Res, 2025, 50(3): 276-281.
41. Mai ELC, Chen BH, Su TY. Innovative utilization of ultra-wide field fundus images and deep learning algorithms for screening high-risk posterior polar cataract. J Cataract Refract Surg, 2024, 50(6): 618-623.
42. Panthier C, Zeboulon P, Rouger H, et al. CATALYZE: a deep learning approach for cataract assessment and grading on SS-OCT images. J Cataract Refract Surg, 2025, 51(3): 222-228.
43. Xiao Z, Zhang X, Zheng B, et al. Multi-style spatial attention module for cortical cataract classification in AS-OCT image with supervised contrastive learning. Comput Methods Programs Biomed, 2024, 244: 107958.
44. Schwarzenbacher L, Seeb?ck P, Schartmüller D, et al. Automatic segmentation of intraocular lens, the retrolental space and Berger’s space using deep learning. Acta Ophthalmol, 2022, 100(8): e1611-e1616.
45. Moriguchi T, Tabuchi H, Mao T, et al. Localization of intraocular lens position in anterior segment optical coherence tomography(AS-OCT) using deep learning. Invest Ophth Vis Sci, 2024, 65(7): 2.
46. Soh ZD, Tan M, Nongpiur ME, et al. Deep learning-based quantification of anterior segment OCT parameters. Ophthalmol Sci, 2023, 4(1): 100360.
47. Fernández-Vigo JI, Macarro-Merino A, De Moura-Ramos JJ, et al. Comparative study of the glistening between four intraocular lens models assessed by OCT and deep learning. J Cataract Refract Surg, 2024, 50(1): 37-42.
48. Xin C, Bian GB, Zhang H, et al. Optical coherence tomography-based deep learning algorithm for quantification of the location of the intraocular lens. Ann Transl Med, 2020, 8(14): 872.
49. Zhang X, Lv J, Zheng H, et al. Attention-based multi-model ensemble for automatic cataract detection in B-scan eye ultrasound images. Glasgow: International Joint Conference on Neural Networks (IJCNN), 2020: 9207696.
50. Caixinha M, Amaro J, Santos M, et al. In-vivo automatic nuclear cataract detection and classification in an animal model by ultrasounds. IEEE Trans Biomed Eng, 2016, 63(11): 2326-2335.
51. Wu H, Lv J, Wang J. Automatic cataract detection with multi-task learning. Shenzhen: International Joint Conference on Neural Networks (IJCNN), 2021: 1-8.
52. Miao Q, Zhou S, Yang J, et al. Keypoint localization and parameter measurement in ultrasound biomicroscopy anterior segment images based on deep learning. Biomed Eng Online, 2025, 24(1): 53.
53. Ting DSJ, Ang M, Mehta JS, et al. Artificial intelligence-assisted telemedicine platform for cataract screening and management: a potential model of care for global eye health. Br J Ophthalmol, 2019, 103(11): 1537-1538.
54. Long E, Chen J, Wu X, et al. Artificial intelligence manages congenital cataract with individualized prediction and telehealth computing. NPJ Digit Med, 2020, 3: 112.
55. Janti SS, Saluja R, Tiwari N, et al. Evaluation of the clinical impact of a smartphone application for cataract detection. Cureus, 2024, 16(10): e71467.
56. Saqib SM, Iqbal M, Zubair Asghar M, et al. Cataract and glaucoma detection based on transfer learning using mobilenet. Heliyon, 2024, 10(17): e36759.
57. Ueno Y, Oda M, Yamaguchi T, et al. Deep learning model for extensive smartphone-based diagnosis and triage of cataracts and multiple corneal diseases. Br J Ophthalmol, 2024, 108(10): 1406-1413.
58. Ignatowicz AA, Marciniak T, Marciniak E. AI-powered mobile app for nuclear cataract detection. Sensors (Basel), 2025, 25(13): 3954.
59. Ahuja AS, Paredes Iii AA, Eisel MLS, et al. Applications of artificial intelligence in cataract surgery: a review. Clin Ophthalmol, 2024, 18: 2969-2975.
60. Yeh HH, Jain AM, Fox O, et al. PhacoTrainer: deep learning for cataract surgical videos to track surgical tools. Transl Vis Sci Technol, 2023, 12(3): 23.
61. Mueller S, Sachdeva B, Prasad SN, et al. Phase recognition in manual small-incision cataract surgery with MS-TCN ^{+ +} on the novel SICS-105 dataset. Sci Rep, 2025, 15(1): 16886.
62. Zeng Z, Giap BD, Kahana E, et al. Evaluation of methods for detection and semantic segmentation of the anterior capsulotomy in cataract surgery video. Clin Ophthalmol, 2024, 18: 647-657.
63. Yeh HH, Jain AM, Fox O, et al. PhacoTrainer: a multicenter study of deep learning for activity recognition in cataract surgical videos. Transl Vis Sci Technol, 2021, 10(13): 23.
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