Nội dung được dịch bởi AI, chỉ mang tính chất tham khảo
Ứng dụng trí tuệ nhân tạo trong quản lý đục thủy tinh thể: hướng đi hiện tại và tương lai
Tóm tắt
Sự phát triển của trí tuệ nhân tạo (AI) đã mang lại những đột phá trong nhiều lĩnh vực của y học. Trong nhãn khoa, AI đã cung cấp những kết quả vững chắc trong việc sàng lọc và phát hiện bệnh võng mạc tiểu đường, thoái hóa điểm vàng liên quan đến tuổi tác, bệnh glaucom và bệnh võng mạc ở trẻ sinh non. Quản lý đục thủy tinh thể là một lĩnh vực khác có thể hưởng lợi từ việc ứng dụng AI nhiều hơn. Đục thủy tinh thể là nguyên nhân hàng đầu gây suy giảm thị lực có thể phục hồi với gánh nặng lâm sàng toàn cầu đang gia tăng. Cần có sự cải thiện trong chẩn đoán, theo dõi và quản lý phẫu thuật để giải quyết thách thức này. Hơn nữa, bệnh nhân ở các quốc gia đang phát triển thường gặp khó khăn trong việc tiếp cận chăm sóc tuyến ba, một vấn đề càng được làm trầm trọng thêm bởi đại dịch COVID-19 vẫn đang diễn ra. Trí tuệ nhân tạo, ngược lại, có thể giúp biến đổi quản lý đục thủy tinh thể bằng cách nâng cao tự động hóa, hiệu quả và vượt qua rào cản địa lý. Đầu tiên, AI có thể được áp dụng như một nền tảng chẩn đoán từ xa để sàng lọc và chẩn đoán bệnh nhân bị đục thủy tinh thể bằng cách sử dụng hình ảnh của đèn khe và đáy mắt. Điều này tận dụng mạng nơ-ron tích chập (CNN) học sâu để phát hiện và phân loại các trường hợp đục thủy tinh thể cần can thiệp. Thứ hai, một số công thức thủy tinh thể nội nhãn mới nhất đã sử dụng AI để nâng cao độ chính xác dự đoán, đạt được kết quả khúc xạ sau phẫu thuật ưu việt so với các công thức truyền thống. Thứ ba, AI có thể được sử dụng để nâng cao đào tạo kỹ năng phẫu thuật đục thủy tinh thể bằng cách xác định các giai đoạn khác nhau của phẫu thuật đục thủy tinh thể trên video và tối ưu hóa quy trình trong phòng mổ bằng cách dự đoán chính xác thời gian thực hiện các thủ tục phẫu thuật. Thứ tư, một số mô hình CNN AI có khả năng dự đoán hiệu quả sự tiến triển của độ đục bao sau và nhu cầu cuối cùng về phẫu thuật laser YAG. Những tiến bộ này trong AI có thể biến đổi quản lý đục thủy tinh thể và cho phép cung cấp các dịch vụ nhãn khoa hiệu quả. Những thách thức chính bao gồm quản lý dữ liệu một cách có đạo đức, đảm bảo an ninh và quyền riêng tư dữ liệu, chứng tỏ hiệu suất được chấp nhận lâm sàng, cải thiện khả năng tổng quát của các mô hình AI trên các dân số đa dạng, và nâng cao niềm tin của người dùng cuối.
Từ khóa
#trí tuệ nhân tạo #quản lý đục thủy tinh thể #nhãn khoa #chẩn đoán từ xa #mạng nơ-ron tích chập #đào tạo kỹ năng phẫu thuật #độ đục bao sauTài liệu tham khảo
Zhang X, Chutatape O. A SVM approach for detection of hemorrhages in background diabetic retinopathy. In: Proceedings 2005 IEEE international joint conference on neural networks. Montreal; 2005. p. 2435–40.
Sadeghzadeh R. Detection of retinal blood vessels using complex wavelet transforms and random forest classification. In: Proceedings of medical image understanding and analysis. 2010; p. 127–31.
Janiesch C, Zschech P, Heinrich K. Machine learning and deep learning. Electron Mark. 2021. https://doi.org/10.1007/s12525-021-00475-2.
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.
Ting DSW, Cheung CY, Lim G, Tan GSW, Quang ND, Gan A, 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. 2017;318(22):2211–23.
Flaxman SR, Bourne RRA, Resnikoff S, Ackland P, Braithwaite T, Cicinelli MV, et al. Global causes of blindness and distance vision impairment 1990–2020: a systematic review and meta-analysis. Lancet Glob Health. 2017;5(12):e1221–34.
Organization WH. World report on vision. World Health Organization Dataset. https://www.who.int/publications/i/item/9789241516570. Accessed 21 Nov 2021.
Deng Y, Yang D, Yu JM, Xu JX, Hua H, Chen RT, 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):101–9.
Gao X, Lin S, Wong TY. Automatic feature learning to grade nuclear cataracts based on deep learning. IEEE Trans Biomed Eng. 2015;62(11):2693–701.
Li W, Yang Y, Zhang K, Long E, He L, Zhang L, et al. Dense anatomical annotation of slit-lamp images improves the performance of deep learning for the diagnosis of ophthalmic disorders. Nat Biomed Eng. 2020;4(8):767–77.
Wu X, Huang Y, Liu Z, Lai W, Long E, Zhang K, et al. Universal artificial intelligence platform for collaborative management of cataracts. Br J Ophthalmol. 2019;103(11):1553–60.
Xu X, Zhang L, Li J, Guan Y, Zhang L. A hybrid global-local representation CNN model for automatic cataract grading. IEEE J Biomed Health Inf. 2020;24(2):556–67.
Hipólito-Fernandes D, Elisa Luís M, Gil P, Maduro V, Feijão J, Yeo TK, et al. VRF-G, a new intraocular lens power calculation formula: a 13-formulas comparison study. Clin Ophthalmol. 2020;14:4395–402.
Kane JX, Melles RB. Intraocular lens formula comparison in axial hyperopia with a high-power intraocular lens of 30 or more diopters. J Cataract Refract Surg. 2020;46(9):1236–9.
Melles RB, Kane JX, Olsen T, Chang WJ. Update on intraocular lens calculation formulas. Ophthalmology. 2019;126(9):1334–5.
Kane JX, Chang DF. Intraocular lens power formulas, biometry, and intraoperative aberrometry: a review. Ophthalmology. 2021;128(11):e94–114.
Sramka M, Slovak M, Tuckova J, Stodulka P. Improving clinical refractive results of cataract surgery by machine learning. PeerJ. 2019;7:e7202.
Kane Formula. Available from: https://www.iolformula.com. Accessed 2 July 2021.
Wan KH, Lam TCH, Yu MCY, Chan TCY. Accuracy and precision of intraocular lens calculations using the new Hill-RBF version 2.0 in eyes with high axial myopia. Am J Ophthalmol. 2019;205:66–73.
Tsessler M, Cohen S, Wang L, Koch DD, Zadok D, Abulafia A. Evaluating the prediction accuracy of the Hill-RBF 3.0 formula using a heteroscedastic statistical method. J Cataract Refract Surg. 2022;48(1):37–43.
Kane JX, Van Heerden A, Atik A, Petsoglou C. Accuracy of 3 new methods for intraocular lens power selection. J Cataract Refract Surg. 2017;43(3):333–9.
IOLcalc—Ladas Super Formula. https://www.iolcalc.com. Accessed 3 July 2021.
Carmona González D, Palomino Bautista C. Accuracy of a new intraocular lens power calculation method based on artificial intelligence. Eye (Lond). 2021;35(2):517–22.
Yu F, Silva Croso G, Kim TS, Song Z, Parker F, Hager GD, et al. Assessment of automated identification of phases in videos of cataract surgery using machine learning and deep learning techniques. JAMA Netw Open. 2019;2(4):e191860.
Hajj HA, Lamard M, Cochener B, Quellec G. Smart data augmentation for surgical tool detection on the surgical tray. In: Annual international conference of the IEEE engineering in medical and biology society. 2017; p. 4407–10.
Lecuyer G, Ragot M, Martin N, Launay L, Jannin P. Assisted phase and step annotation for surgical videos. Int J Comput Assist Radiol Surg. 2020;15(4):673–80.
Al Hajj H, Lamard M, Conze PH, Cochener B, Quellec G. Monitoring tool usage in surgery videos using boosted convolutional and recurrent neural networks. Med Image Anal. 2018;47:203–18.
Lanza M, Koprowski R, Boccia R, Krysik K, Sbordone S, Tartaglione A, et al. Application of artificial intelligence in the analysis of features affecting cataract surgery complications in a teaching hospital. Front Med (Lausanne). 2020;7:607870.
Jiang J, Liu X, Liu L, Wang S, Long E, Yang H, et al. Predicting the progression of ophthalmic disease based on slit-lamp images using a deep temporal sequence network. PLoS One. 2018;13(7):e0201142.
Mohammadi SF, Sabbaghi M, Z-Mehrjardi H, Hashemi H, Alizadeh S, Majdi M, et al. Using artificial intelligence to predict the risk for posterior capsule opacification after phacoemulsification. J Cataract Refract Surg. 2012;38(3):403–8.
The Vision Council Releases 2019 Vision Watch Cataract Report. Eyewire News. https://eyewire.news/articles/the-vision-council-releases-2019-vision-watch-cataract-report. Accessed 3 July 2021.
Analysis: ophthalmology lost more patient volume due to COVID-19 than any other specialty. Strata decision technology. Eyewire News. https://eyewire.news/articles/analysis-55-percent-fewer-americans-sought-hospital-care-in-march-april-due-to-covid-19. Accessed 3 July 2021.
Ting DSJ, Deshmukh R, Said DG, Dua HS. The impact of COVID-19 pandemic on ophthalmology services: are we ready for the aftermath? Ther Adv Ophthalmol. 2020;12:2515841420964099.
GBD 2019 Blindness and Vision Impairment Collaborators, Vision Loss Expert Group of the Global Burden of Disease Study. Causes of blindness and vision impairment in 2020 and trends over 30 years, and prevalence of avoidable blindness in relation to VISION 2020: the Right to Sight: an analysis for the Global Burden of Disease Study. Lancet Glob Health. 2021;9(2):e144–60.
Liste S. Vision impairment and blindness. World Health Organization. https://www.who.int/news-room/fact-sheets/detail/blindness-and-visual-impairment. Accessed 11 Sept 2021.
Ramke J, Gilbert CE, Lee AC, Ackland P, Limburg H, Foster A. Effective cataract surgical coverage: an indicator for measuring quality-of-care in the context of Universal Health Coverage. PLoS One. 2017;12(3):e0172342.
Palmer JJ, Chinanayi F, Gilbert A, Pillay D, Fox S, Jaggernath J, et al. Mapping human resources for eye health in 21 countries of sub-Saharan Africa: current progress towards VISION 2020. Hum Resour Health. 2014;12:44.
Ladas J, Ladas D, Lin SR, Devgan U, Siddiqui AA, Jun AS. Improvement of multiple generations of intraocular lens calculation formulae with a novel approach using artificial intelligence. Trans Vis Sci Technol. 2021;10(3):7.
Debellemanière G, Dubois M, Gauvin M, Wallerstein A, Brenner Luis F, Rampat R, et al. The PEARL-DGS formula: the development of an open-source machine learning-based thick IOL calculation formula. Am J Ophthalmol. 2021;232:58–69.
Jin GJ, Crandall AS, Jones JJ. Intraocular lens exchange due to incorrect lens power. Ophthalmology. 2007;114(3):417–24.
Savini G, Hoffer KJ, Balducci N, Barboni P, Schiano-Lomoriello D. Comparison of formula accuracy for intraocular lens power calculation based on measurements by a swept-source optical coherence tomography optical biometer. J Cataract Refract Surg. 2020;46(1):27–33.
Ladas JG, Siddiqui AA, Devgan U, Jun AS. A 3-D “Super Surface” combining modern intraocular lens formulas to generate a “Super Formula” and maximize accuracy. JAMA Ophthalmol. 2015;133(12):1431–6.
Patel RH, Karp CL, Yoo SH, Amescua G, Galor A. Cataract surgery after refractive surgery. Int Ophthalmol Clin. 2016;56(2):171–82.
Wang L, Tang M, Huang D, Weikert MP, Koch DD. Comparison of newer intraocular lens power calculation methods for eyes after corneal refractive surgery. Ophthalmology. 2015;122(12):2443–9.
Koch D, Wang L. Calculating IOL power in eyes that have had refractive surgery. J Cataract Refract Surg. 2003;29(11):2039–42.
LaHood BR, Goggin M. Measurement of posterior corneal astigmatism by the IOLMaster 700. J Refract Surg. 2018;34(5):331–6.
Yeo TK, Heng WJ, Pek D, Wong J, Fam HB. Accuracy of intraocular lens formulas using total keratometry in eyes with previous myopic laser refractive surgery. Eye (Lond). 2021;35(6):1705–11.
Koprowski R, Lanza M, Irregolare C. Corneal power evaluation after myopic corneal refractive surgery using artificial neural networks. Biomed Eng Online. 2016;15(1):121.
Sheeladevi S, Lawrenson JG, Fielder AR, Suttle CM. Global prevalence of childhood cataract: a systematic review. Eye (Lond). 2016;30(9):1160–9.
Gilbert C, Foster A. Childhood blindness in the context of VISION 2020—the right to sight. Bull World Health Organ. 2001;79(3):227–32.
Lin D, Chen J, Lin Z, Li X, Zhang K, Wu X, et al. A practical model for the identification of congenital cataracts using machine learning. EBioMedicine. 2020;51:102621.
Lin H, Li R, Liu Z, Chen J, Yang Y, Chen H, et al. Diagnostic efficacy and therapeutic decision-making capacity of an artificial intelligence platform for childhood cataracts in eye clinics: a multicentre randomized controlled trial. EClinicalMedicine. 2019;9:52–9.
Liu X, Jiang J, Zhang K, Long E, Cui J, Zhu M, 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.
Long E, Lin H, Liu Z, Wu X, Wang L, Jiang J, et al. An artificial intelligence platform for the multihospital collaborative management of congenital cataracts. Nat Biomed Eng. 2017;1(2):1–8.
Long E, Chen J, Wu X, Liu Z, Wang L, Jiang J, et al. Artificial intelligence manages congenital cataract with individualized prediction and telehealth computing. NPJ Digit Med. 2020;3(1):112.
Navarrete-Welton AJ, Hashimoto DA. Current applications of artificial intelligence for intraoperative decision support in surgery. Front Med. 2020;14(4):369–81.
Mirchi N, Bissonnette V, Yilmaz R, Ledwos N, Winkler-Schwartz A, Del Maestro RF. The Virtual Operative Assistant: an explainable artificial intelligence tool for simulation-based training in surgery and medicine. PLoS One. 2020;15(2):e0229596.
Menozzi M, Ropelat S, Köfler J, Huang YY. Development of ophthalmic microsurgery training in augmented reality. Klin Monbl Augenheilkd. 2020;237(4):388–91.
Ursell PG, Dhariwal M, Majirska K, Ender F, Kalson-Ray S, Venerus A, et al. Three-year incidence of Nd:YAG capsulotomy and posterior capsule opacification and its relationship to monofocal acrylic IOL biomaterial: a UK Real World Evidence study. Eye (Lond). 2018;32(10):1579–89.
Thompson AM, Sachdev N, Wong T, Riley AF, Grupcheva CN, McGhee CN. The Auckland Cataract Study: 2 year postoperative assessment of aspects of clinical, visual, corneal topographic and satisfaction outcomes. Br J Ophthalmol. 2004;88(8):1042–8.
Your Electronic Medical Records Could Be Worth $1000 To Hackers. https://www.forbes.com/sites/mariyayao/2017/04/14/your-electronic-medical-records-can-be-worth-1000-to-hackers/?sh=5eca6d6f50cf. Accessed 21 July 2021.
Singleton C. X-Force Threat Intelligence Index 2021. IBM Security. 2021 Feb; p. 43–5.
IBM, Ponemon I. Cost of a data breach report 2020. IBM Security. 2020 June; p. 82.
Truong L, Jones C, Hutchinson B, August A, Praggastis B, Jasper R, et al. Systematic evaluation of backdoor data poisoning attacks on image classifiers. In: 2020 IEEE/CVF conference on computer vision and pattern recognition workshops. 2020. p. 788–9.
Ma X, Niu Y, Gu L, Wang Y, Zhao Y, Bailey J, et al. Understanding adversarial attacks on deep learning based medical image analysis systems. arXiv preprint arXiv:190710456.
Sarma KV, Harmon S, Sanford T, Roth HR, Xu Z, Tetreault J, et al. Federated learning improves site performance in multicenter deep learning without data sharing. J Am Med Inf Assoc. 2021;28(6):1259–64.
Warnat-Herresthal S, Schultze H, Shastry KL, Manamohan S, Mukherjee S, Garg V, et al. Swarm learning for decentralized and confidential clinical machine learning. Nature. 2021;594(7862):265–70.
Lim G, Thombre P, Lee ML, Hsu W. Generative data augmentation for diabetic retinopathy classification. In: 2020 IEEE 32nd international conference on tools with artificial intelligence (ICTAI). 2020. p. 1096–103.
Sundararajan M, Taly A, Yan Q. Axiomatic attribution for deep networks. In: Proceedings of the 34th international conference on machine learning. PMLR; 2017. p. 3319–28.
Varol E, Sotiras A, Zeng K, Davatzikos C. Generative discriminative models for multivariate inference and statistical mapping in medical imaging. In: Frangi AF, Schnabel JA, Davatzikos C, Alberola-López C, Fichtinger G, editors. Medical image computing and computer assisted intervention—MICCAI 2018. Cham: Springer International Publishing; 2018. p. 540–8.
Zhu Y, Suri S, Kulkarni P, Chen Y, Duan J, Kuo C-CJ. An interpretable generative model for handwritten digit image synthesis. arXiv preprint arXiv:1811.04507.
Danso SO, Muniz-Terrera G, Luz S, Ritchie C; Global Dementia Prevention Program (GloDePP). Application of big data and artificial intelligence technologies to dementia prevention research: an opportunity for low- and-middle-income countries. J Glob Health. 2019;9(2):020322.
Kshetri N. Artificial intelligence in developing countries. IEEE Ann Hist Comput. 2020;22(04):63–8.
CONSORT-AI and SPIRIT-AI Steering Group. Reporting guidelines for clinical trials evaluating artificial intelligence interventions are needed. Nat Med. 2019;25(10):1467–8.
Zhang H, Niu K, Xiong Y, Yang W, He Z, Song H. Automatic cataract grading methods based on deep learning. Comput Methods Prog Biomed. 2019;182:104978.
Xiong L, Li H, Xu L. An approach to evaluate blurriness in retinal images with vitreous opacity for cataract diagnosis. J Healthc Eng. 2017;2017:5645498.
Yang JJ, Li J, Shen R, Zeng Y, He J, Bi J, Li Y, Zhang Q, Peng L, Wang Q. Exploiting ensemble learning for automatic cataract detection and grading. Comput Methods Prog Biomed. 2016;124:45–57.
Guo L, Yang JJ, Peng L, Li J, Liang Q. A computer-aided healthcare system for cataract classification and grading based on fundus image analysis. Comput Ind. 2015;69:72–80.
Xu Y, Gao X, Lin S, Wong DWK, Liu J, Xu D, et al. Automatic grading of nuclear cataracts from slit-lamp lens images using group sparsity regression. In: International conference on medical image computing and computer-assisted intervention. Berlin: Springer; 2013. p. 468–75.
Gao X, Wong DWK, Ng TT, Cheung CYL, Cheng CY, Wong TY. Automatic grading of cortical and PSC cataracts using retroillumination lens images. In: Asian conference on computer vision. Berlin: Springer; 2012 Nov. p. 256–67.
Cheung CY, Li H, Lamoureux EL, Mitchell P, Wang JJ, Tan AG, et al. Validity of a new computer-aided diagnosis imaging program to quantify nuclear cataract from slit-lamp photographs. Invest Ophthalmol Vis Sci. 2011;52(03):1314–9.
Acharya RU, Yu W, Zhu K, Nayak J, Lim TC, Chan JY. Identification of cataract and post-cataract surgery optical images using artificial intelligence techniques. J Med Syst. 2010;34(4):619–28.
Connell BJ, Kane JX. Comparison of the Kane formula with existing formulas for intraocular lens power selection. BMJ Open Ophthalmol. 2019;4(1):e000251.