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Artificial intelligence for the diagnosis of retinopathy of prematurity: A systematic review of current algorithms

Eye volume 37, pages 2518–2526 (2023)Cite this article

Subjects

Abstract

Background/Objectives

With the increasing survival of premature infants, there is an increased demand to provide adequate retinopathy of prematurity (ROP) services. Wide field retinal imaging (WFDRI) and artificial intelligence (AI) have shown promise in the field of ROP and have the potential to improve the diagnostic performance and reduce the workload for screening ophthalmologists. The aim of this review is to systematically review and provide a summary of the diagnostic characteristics of existing deep learning algorithms.

Subject/Methods

Two authors independently searched the literature, and studies using a deep learning system from retinal imaging were included. Data were extracted, assessed and reported using PRISMA guidelines.

Results

Twenty-seven studies were included in this review. Nineteen studies used AI systems to diagnose ROP, classify the staging of ROP, diagnose the presence of pre-plus or plus disease, or assess the quality of retinal images. The included studies reported a sensitivity of 71%–100%, specificity of 74–99% and area under the curve of 91–99% for the primary outcome of the study. AI techniques were comparable to the assessment of ophthalmologists in terms of overall accuracy and sensitivity. Eight studies evaluated vascular severity scores and were able to accurately differentiate severity using an automated classification score.

Conclusion

Artificial intelligence for ROP diagnosis is a growing field, and many potential utilities have already been identified, including the presence of plus disease, staging of disease and a new automated severity score. AI has a role as an adjunct to clinical assessment; however, there is insufficient evidence to support its use as a sole diagnostic tool currently.

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Fig. 1: Diagrammatic representation of deep learning frameworks.
Fig. 2: PRISMA flow diagram.

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Data availability

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

References

  1. Quinn GE. Retinopathy of prematurity blindness worldwide: phenotypes in the third epidemic. Eye Brain. 2016;8:31–6.

    Article  PubMed  PubMed Central  Google Scholar 

  2. National Eye Institute. Retinopathy of Prematurity: National Institutes of Health. 2019. https://www.nei.nih.gov/learn-about-eye-health/eye-conditions-and-diseases/retinopathy-prematurity.

  3. Fierson WM. Screening examination of premature infants for retinopathy of prematurity. Pediatrics. 2018;142:e20183061.

    Article  PubMed  Google Scholar 

  4. Jefferies AL, Society CP. Fetus, Committee N. Retinopathy of prematurity: An update on screening and management. Paediatr Child Health. 2016;21:101–4.

    Article  PubMed  PubMed Central  Google Scholar 

  5. International Committee for the Classification of Retinopathy of Prematurity. An international classification of retinopathy of prematurity. The Committee for the Classification of Retinopathy of Prematurity. Arch Ophthalmol. 1984;102:1130–4.

    Article  Google Scholar 

  6. International Committee for the Classification of Retinopathy of Prematurity. The International classification of retinopathy of prematurity revisited. Arch Ophthalmol. 2005;123:991–9.

    Article  Google Scholar 

  7. Chiang MF, Quinn GE, Fielder AR, Ostmo SR, Chan RV, Berrocal A, et al. International classification of retinopathy of prematurity, Third Edition. Ophthalmology. 2021;128:e51–e68.

    Article  PubMed  Google Scholar 

  8. Gschließer A, Stifter E, Neumayer T, Moser E, Papp A, Pircher N, et al. Inter-expert and intra-expert agreement on the diagnosis and treatment of retinopathy of prematurity. Am J Ophthalmol. 2015;160:553–60.

  9. Athikarisamy SE, Lam GC, Ross S, Rao SC, Chiffings D, Simmer K, et al. Comparison of wide field imaging by nurses with indirect ophthalmoscopy by ophthalmologists for retinopathy of prematurity: A diagnostic accuracy study. BMJ Open. 2020;10:e036483.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Ranschaert ER, Morozov S, Algra PR. Artificial intelligence in medical imaging: Opportunities, applications and risks. 2019, p. 39–48.

  11. Miotto R, Wang F, Wang S, Jiang X, Dudley JT. Deep learning for healthcare: Review, opportunities and challenges. Brief Bioinforma. 2018;19:1236–46.

    Article  Google Scholar 

  12. Choi RY, Coyner AS, Kalpathy-Cramer J, Chiang MF, Campbell JP. Introduction to machine learning, neural networks, and deep learning. Transl Vis Sci Technol. 2020;9:14.

    PubMed  PubMed Central  Google Scholar 

  13. Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffman TC, Mulrow CD, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ. 2021;372:n71. https://doi.org/10.1136/bmj.n71.

    Article  PubMed  PubMed Central  Google Scholar 

  14. Zweig MH, Campbell G. Receiver-operating characteristic (ROC) plots: A fundamental evaluation tool in clinical medicine. Clin Chem. 1993;39:561–77.

    Article  CAS  PubMed  Google Scholar 

  15. Coyner AS, Swan R, Brown JM, Kalpathy-Cramer J, Kim SJ, Campbell JP, et al. Deep learning for image quality assessment of fundus images in retinopathy of prematurity. AMIA Annu Symp Proc. 2018;2018:1224–32.

    PubMed  PubMed Central  Google Scholar 

  16. Coyner AS, Swan R, Campbell JP, Ostmo S, Brown JM, Kalpathy-Cramer J, et al. Automated fundus image quality assessment in retinopathy of prematurity using deep convolutional neural networks. Ophthalmol Retin. 2019;3:444–50.

    Article  Google Scholar 

  17. Wang J, Ji J, Zhang M, Lin JW, Zhang G, Gong W, et al. Automated explainable multidimensional deep learning platform of retinal images for retinopathy of prematurity screening. JAMA Netw Open 2021;4:e218758.

  18. Huang YP, Vadloori S, Chu HC, Kang EY, Wu WC, Kusaka S, et al. Deep learning models for automated diagnosis of retinopathy of prematurity in preterm infants. Electronics 2020;9:1444.

  19. Huang YP, Basanta H, Kang EY, Chen KJ, Hwang YS, Lai CC, et al. Automated detection of early-stage ROP using a deep convolutional neural network. Br J Ophthalmol. 2021;105:1099–103.

  20. Hu J, Chen Y, Zhong J, Ju R, Yi Z. Automated analysis for retinopathy of prematurity by deep neural networks. IEEE Transactions Med Imaging 2018;38:269–79.

  21. Wang J, Ju R, Chen Y, Zhang L, Hu J, Wu Y, et al. Automated retinopathy of prematurity screening using deep neural networks. EBioMedicine. 2018;35:361–8.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Zhang R, Zhao J, Xie H, Wang T, Chen G, Zhang G, et al. Automatic diagnosis for aggressive posterior retinopathy of prematurity via deep attentive convolutional neural network. Expert Sys Appl. 2021;187:115843.

  23. Attallah O. DIAROP: Automated deep learning-based diagnostic tool for retinopathy of prematurity. Diagnostics (Basel). 2021;11:2034.

    Article  PubMed  Google Scholar 

  24. Zhang Y, Wang L, Wu Z, Zeng J, Chen Y, Tian R, et al. Development of an automated screening system for retinopathy of prematurity using a deep neural network for wide-angle retinal images. IEEE Access. 2018;7:10232–41.

    Article  Google Scholar 

  25. Mao J, Luo Y, Liu L, Lao J, Shao Y, Zhang M, et al. Automated diagnosis and quantitative analysis of plus disease in retinopathy of prematurity based on deep convolutional neural networks. Acta Ophthalmologica. 2020;98:e339–e45.

    Article  CAS  PubMed  Google Scholar 

  26. Tong Y, Lu W, Deng QQ, Chen C, Shen Y. Automated identification of retinopathy of prematurity by image-based deep learning. Eye Vis. 2020;7:40.

    Article  Google Scholar 

  27. Yildiz VM, Tian P, Yildiz I, Brown JM, Kalpathy-Cramer J, Dy J, et al. Plus disease in retinopathy of prematurity: Convolutional neural network performance using a combined neural network and feature extraction approach. Transl Vis Sci Technol. 2020;9:10-.

    Article  PubMed  PubMed Central  Google Scholar 

  28. Tan Z, Simkin S, Lai C, Dai S. Deep learning algorithm for automated diagnosis of retinopathy of prematurity plus disease. Transl Vis Sci Technol. 2019;8:23-.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Brown JM, Campbell JP, Beers A, Chang K, Ostmo S, Chan RV, et al. Automated diagnosis of plus disease in retinopathy of prematurity using deep convolutional neural networks. JAMA Ophthalmol. 2018;136:803–10.

    Article  PubMed  PubMed Central  Google Scholar 

  30. Redd TK, Campbell JP, Brown JM, Kim SJ, Ostmo S, Chan RV, et al. Evaluation of a deep learning image assessment system for detecting severe retinopathy of prematurity. Br J Ophthalmol. 2019;103:580.

    Article  Google Scholar 

  31. Ramachandran S, Niyas P, Vinekar A, John R. A deep learning framework for the detection of Plus disease in retinal fundus images of preterm infants. Biocybern Biomed Eng. 2021;41:362–75.

    Article  Google Scholar 

  32. Worrall DE, Wilson CM, Brostow G. Automated Retinopathy of Prematurity Case Detection with Convolutional Neural Networks. Deep Learning and Data labeling for Medical Applications; 2016, p. 68–76.

  33. Campbell JP, Kim SJ, Brown JM, Ostmo S, Chan RV, Kalpathy-Cramer J, et al. Evaluation of a Deep Learning-Derived Quantitative Retinopathy of Prematurity Severity Scale. Ophthalmology 2021;128:1070–6.

  34. Campbell JP, Singh P, Redd TK, Brown JM, Shah PK, Subramanian P, et al. Applications of Artificial Intelligence for Retinopathy of Prematurity Screening. Pediatrics 2021;147:e2020016618.

  35. Choi RY, Brown JM, Kalpathy-Cramer J, Chan RV, Ostmo S, Chiang MF, et al. Variability in plus disease identified using a deep learning-based retinopathy of prematurity severity scale. Ophthalmol Retin. 2020;4:1016–21.

    Article  Google Scholar 

  36. Bellsmith KN, Brown J, Kim SJ, Goldstein IH, Coyner A, Ostmo S, et al. Aggressive posterior retinopathy of prematurity: Clinical and quantitative imaging features in a large North American Cohort. Ophthalmology. 2020;127:1105–12.

    Article  PubMed  Google Scholar 

  37. Taylor S, Brown JM, Gupta K, Campbell JP, Ostmo S, Chan RV, et al. Monitoring disease progression with a quantitative severity scale for retinopathy of prematurity using deep learning. JAMA Ophthalmol. 2019;137:1022–8.

    Article  PubMed  PubMed Central  Google Scholar 

  38. Gupta K, Campbell JP, Taylor S, Brown JM, Ostmo S, Chan RV, et al. A quantitative severity scale for retinopathy of prematurity using deep learning to monitor disease regression after treatment. JAMA Ophthalmol. 2019;137:1029–36.

    Article  PubMed  PubMed Central  Google Scholar 

  39. Brown JM, Campbell JP, Beers A, Chang K, Donohue K, Ostmo S, et al. Fully automated disease severity assessment and treatment monitoring in retinopathy of prematurity using deep learning. Med Imaging 2018;10579:149–55.

  40. Greenwald MF, Danford ID, Shahrawat M, Ostmo S, Brown JM, Kalpathy-Cramer J, et al. Evaluation of artificial intelligence-based telemedicine screening for retinopathy of prematurity. J AAPOS. 2020;24:160–2.

    Article  PubMed  PubMed Central  Google Scholar 

  41. Lepore D, Ji MH, Pagliara MM, Lenkowicz J, Capocchiano ND, Tagliaferri L, et al. Convolutional neural network based on fluorescein angiography images for retinopathy of prematurity management. Transl Vis Sci Technol. 2020;9:37.

    Article  PubMed  PubMed Central  Google Scholar 

  42. Cryotherapy for Retinopathy of Prematurity Cooperative Group. Multicenter trial of cryotherapy for retinopathy of prematurity: ophthalmological outcomes at 10 years. Arch Ophthalmol. 2001;119:1110–8.

    Article  Google Scholar 

  43. Good WV. Final results of the Early Treatment for Retinopathy of Prematurity (ETROP) randomized trial. Trans Am Ophthalmol Soc. 2004;102:233–50.

    PubMed  PubMed Central  Google Scholar 

  44. Chiang MF, Jiang L, Gelman R, Du YE, Flynn JT. Interexpert agreement of plus disease diagnosis in retinopathy of prematurity. Arch Ophthalmol. 2007;125:875–80.

    Article  PubMed  Google Scholar 

  45. Quinn GE, Ying GS, Daniel E, Hildebrand PL, Ells A, Baumritter A, et al. Validity of a telemedicine system for the evaluation of acute-phase retinopathy of prematurity. JAMA Ophthalmol. 2014;132:1178–84.

    Article  PubMed  Google Scholar 

  46. Li J, Huang K, Ju R, Chen Y, Li M, Yang S, et al. Evaluation of artificial intelligence-based quantitative analysis to identify clinically significant severe retinopathy of prematurity. Retina. 2022;42:195–203.

    CAS  PubMed  Google Scholar 

  47. Coyner AS, Chen JS, Singh P, Schelonka RL, Jordan BK, McEvoy CT, et al. Single-examination risk prediction of severe retinopathy of prematurity. Pediatrics 2021;148:e2021051772.

    Article  PubMed  Google Scholar 

  48. Reddy S, Allan S, Coghlan S, Cooper P. A governance model for the application of AI in health care. J Am Med Inf Assoc. 2020;27:491–7.

    Article  Google Scholar 

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Funding

We declare that no author received any specific funding for this study.

Author information

Authors and Affiliations

  1. Department of Paediatrics, Perth Children’s Hospital, Perth, Australia

    Ashwin Ramanathan

  2. Department of Neonatology, Perth Children’s Hospital, Perth, Australia

    Sam Ebenezer Athikarisamy

  3. School of Medicine, University of Western Australia, Crawley, Australia

    Sam Ebenezer Athikarisamy

  4. Department of Ophthalmology, Perth Children’s Hospital, Perth, Australia

    Geoffrey C. Lam

  5. Centre for Ophthalmology and Visual Science, University of Western Australia, Crawley, Australia

    Geoffrey C. Lam

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  1. Ashwin Ramanathan
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Contributions

AR and SA collected the data. AR, SA, and GL analysed and interpreted the data. All authors (AR, SA, and GL) drafted the paper, revised it, and approved the final version.

Corresponding author

Correspondence to Sam Ebenezer Athikarisamy.

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The authors declare no competing interests.

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Ramanathan, A., Athikarisamy, S.E. & Lam, G.C. Artificial intelligence for the diagnosis of retinopathy of prematurity: A systematic review of current algorithms. Eye 37, 2518–2526 (2023). https://doi.org/10.1038/s41433-022-02366-y

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