The progress and problems of the fundus multimodal imaging_Chinese Journal of Ocular Fundus Diseases

Authors：

 Song Zongming , Guo Xiaohong

Department of Ophthalmology, People's Hospital of Zhengzhou University, Henan Provincial People's Hospital, Henan Eye Institute, Henan Eye Hospital, Zhengzhou 450003, China;

Corresponding author：

Song Zongming, Email: szmeyes@sina.com

Keywords：

Retinal diseases; Choroid diseases; Editorial; Multimodal imaging

DOI：

10.3760/cma.j.cn511434-20220110-00014

Video：

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The update of the cognition of fundus diseases is inseparable from the rapid development of fundus multimodal imaging. Especially in recent years, the application of wide and ultra-wide fundus photography, ultra-wide fundus fluorescein angiography, indocyanine green angiography, fundus autofluorescence and optical coherence tomography angiography contribute to observe the peripheral retinopathy more directly. The application of adaptive optics and fluorescence lifetime imaging ophthalmoscopy contribute to have a further understanding of fundus diseases at the cellular and metabolic level. Multimodal imageing reflect the pathological characteristics of the diseases from different angles and levels. At the same time, the digitization and intelligence of fundus images are also developing rapidly. However, there are some problems that the ophthalmologists needs to consider further, such as the correctly understanding the use of multimodal imaging, the application of artificial intelligence, and how to sum up from the images.

Citation： Song Zongming, Guo Xiaohong. The progress and problems of the fundus multimodal imaging. Chinese Journal of Ocular Fundus Diseases, 2022, 38(2): 93-97. doi: 10.3760/cma.j.cn511434-20220110-00014 Copy

1.	Novais EA, Baumal CR, Sarraf D, et al. Multimodal imaging in retinal disease: a consensus definition[J]. Ophthalmic Surg Lasers Imaging Retina, 2016, 47(3): 201-205. DOI: 10.3928/23258160-20160229-01.
2.	Choudhry N, Duker JS, Freund KB, et al. Classification and guidelines for widefield imaging: recommendations from the International Widefield Imaging Study Group[J]. Ophthalmol Retina, 2019, 3(10): 843-849. DOI: 10.1016/j.oret.2019.05.007.
3.	Moriyama M, Cao K, Ogata S, et al. Detection of posterior vortex veins in eyes with pathologic myopia by ultra-widefield indocyanine green angiography[J]. Br J Ophthalmol, 2017, 101(9): 1179-1184. DOI: 10.1136/bjophthalmol-2016-309877.
4.	Hirahara S, Yasukawa T, Kominami A, et al. Densitometry of choroidal vessels in eyes with and without central serous chorioretinopathy by wide-field indocyanine green angiography[J]. Am J Ophthalmol, 2016, 166: 103-111. DOI: 10.1016/j.ajo.2016.03.040.
5.	Fujimoto J, Swanson E. The development, commercialization, and impact of optical coherence tomography[J]. Invest Ophthalmol Vis Sci, 2016, 57(9): OCT1-OCT13. DOI: 10.1167/iovs.16-19963.
6.	Duncker T, Marsiglia M, Lee W, et al. Correlations among near-infrared and short-wavelength autofluorescence and spectral-domain optical coherence tomography in recessive Stargardt disease[J]. Invest Ophthalmol Vis Sci, 2014, 55(12): 8134-8143. DOI: 10.1167/iovs.14-14848.
7.	Mrejen S, Khan S, Gallego-Pinazo R, et al. Acute zonal occult outer retinopathy: a classification based on multimodal imaging[J]. JAMA Ophthalmol, 2014, 132(9): 1089-1098. DOI: 10.1001/jamaophthalmol.2014.1683.
8.	范文强, 王志臣, 陈宝刚, 等. 自适应光学相干层析在视网膜高分辨成像中的应用[J]. 红外与激光工程, 2020, 49(10): 58-70. DOI: 10.3788/IRLA20200333.Fan WQ, Wang ZC, Chen BG, et al. Application of adaptive optics coherence tomography in retinal high resolution imaging[J]. Infrared and Laser Engineering, 2020, 49(10): 58-70. DOI: 10.3788/IRLA20200333.
9.	Wynne N, Carroll J, Duncan JL. Promises and pitfalls of evaluating photoreceptor-based retinal disease with adaptive optics scanning light ophthalmoscopy (AOSLO)[J/OL]. Prog Retin Eye Res, 2021, 83: 100920[2020-11-06]. https://pubmed.ncbi.nlm.nih.gov/33161127/. DOI: 10.1016/j.preteyeres.2020.100920.
10.	Dysli C, Wolf S, Berezin MY, et al. Fluorescence lifetime imaging ophthalmoscopy[J]. Prog Retin Eye Res, 2017, 60: 120-143. DOI: 10.1016/j.preteyeres.2017.06.005.
11.	Dysli C, Quellec G, Abegg M, et al. Quantitative analysis of fluorescence lifetime measurements of the macula using the fluorescence lifetime imaging ophthalmoscope in healthy subjects[J]. Invest Ophthalmol Vis Sci, 2014, 55(4): 2106-2113. DOI: 10.1167/iovs.13-13627.
12.	Dysli C, Berger L, Wolf S, et al. Fundus autofluorescence lifetimes and central serous chorrioretinopathy[J]. Retina, 2017, 37(11): 2151-2161. DOI: 10.1097/IAE.0000000000001452.
13.	Chandran K, Shenoy SB, Kulkarni C, et al. Bilateral simultaneous central retinal artery occlusion (CRAO) in a patient with systemic lupus erythematosus (SLE)[J/OL]. Am J Ophthalmol Case Rep, 2020, 19: 100833[2020-07-20]. https://pubmed.ncbi.nlm.nih.gov/32904183/. DOI: 10.1016/j.ajoc.2020.100833.
14.	Ruggeri A, Poletti E, Fiorin D, et al. From laboratory to clinic: the development of web-based tools for the estimation of retinal diagnostic parameters[J]. Annu Int Conf IEEE Eng Med Biol Soc, 2011, 2011: 3379-3382. DOI: 10.1109/IEMBS.2011.6090915.
15.	Fiorin D, Ruggeri A. Computerized analysis of narrow-field ROP images for the assessment of vessel caliber and tortuosity[J]. Annu Int Conf IEEE Eng Med Biol Soc, 2011, 2011: 2622-2625. DOI: 10.1109/IEMBS.2011.6090723.
16.	Zekavat SM, Raghu VK, Trinder M, et al. Deep learning of the retina enables phenome-and genome-wide analyses of the microvasculature[J]. Circulation, 2022, 145(2): 134-150. DOI: 10.1161/CIRCULATIONAHA.121.057709.
17.	陈有信, 张碧磊, 张弘哲. 眼科人工智能技术的现状与问题[J]. 中华眼底病杂志, 2019, 35(2): 119-123. DOI: 10.3760/cma.j.issn.1005-1015.2019.02.003.Chen YX, Zhang BL, Zhang HZ. Insights and prospectives of ophthalmologic artificial intelligence technology[J]. Chin J Ocul Fundus Dis, 2019, 35(2): 119-123. DOI: 10.3760/cma.j.issn.1005-1015.2019.02.003.
18.	Ryu G, Lee K, Park D, et al. A deep learning model for identifying diabetic retinopathy using optical coherence tomography angiography[J/OL]. Sci Rep, 2021, 11(1): 23024[2021-11-26]. https://pubmed.ncbi.nlm.nih.gov/34837030/. DOI: 10.1038/s41598-021-02479-6.
19.	Lin D, Xiong J, Liu C, et al. Application of comprehensive artificial intelligence retinal expert (CARE) system: a national real-world evidence study[J/OL]. Lancet Digit Health, 2021, 3(8): e486-e495[2021-08-01]. https://pubmed.ncbi.nlm.nih.gov/34325853/. DOI: 10.1016/S2589-7500(21)00086-8.

1. Novais EA, Baumal CR, Sarraf D, et al. Multimodal imaging in retinal disease: a consensus definition[J]. Ophthalmic Surg Lasers Imaging Retina, 2016, 47(3): 201-205. DOI: 10.3928/23258160-20160229-01.
2. Choudhry N, Duker JS, Freund KB, et al. Classification and guidelines for widefield imaging: recommendations from the International Widefield Imaging Study Group[J]. Ophthalmol Retina, 2019, 3(10): 843-849. DOI: 10.1016/j.oret.2019.05.007.
3. Moriyama M, Cao K, Ogata S, et al. Detection of posterior vortex veins in eyes with pathologic myopia by ultra-widefield indocyanine green angiography[J]. Br J Ophthalmol, 2017, 101(9): 1179-1184. DOI: 10.1136/bjophthalmol-2016-309877.
4. Hirahara S, Yasukawa T, Kominami A, et al. Densitometry of choroidal vessels in eyes with and without central serous chorioretinopathy by wide-field indocyanine green angiography[J]. Am J Ophthalmol, 2016, 166: 103-111. DOI: 10.1016/j.ajo.2016.03.040.
5. Fujimoto J, Swanson E. The development, commercialization, and impact of optical coherence tomography[J]. Invest Ophthalmol Vis Sci, 2016, 57(9): OCT1-OCT13. DOI: 10.1167/iovs.16-19963.
6. Duncker T, Marsiglia M, Lee W, et al. Correlations among near-infrared and short-wavelength autofluorescence and spectral-domain optical coherence tomography in recessive Stargardt disease[J]. Invest Ophthalmol Vis Sci, 2014, 55(12): 8134-8143. DOI: 10.1167/iovs.14-14848.
7. Mrejen S, Khan S, Gallego-Pinazo R, et al. Acute zonal occult outer retinopathy: a classification based on multimodal imaging[J]. JAMA Ophthalmol, 2014, 132(9): 1089-1098. DOI: 10.1001/jamaophthalmol.2014.1683.
8. 范文强, 王志臣, 陈宝刚, 等. 自适应光学相干层析在视网膜高分辨成像中的应用[J]. 红外与激光工程, 2020, 49(10): 58-70. DOI: 10.3788/IRLA20200333.Fan WQ, Wang ZC, Chen BG, et al. Application of adaptive optics coherence tomography in retinal high resolution imaging[J]. Infrared and Laser Engineering, 2020, 49(10): 58-70. DOI: 10.3788/IRLA20200333.
9. Wynne N, Carroll J, Duncan JL. Promises and pitfalls of evaluating photoreceptor-based retinal disease with adaptive optics scanning light ophthalmoscopy (AOSLO)[J/OL]. Prog Retin Eye Res, 2021, 83: 100920[2020-11-06]. https://pubmed.ncbi.nlm.nih.gov/33161127/. DOI: 10.1016/j.preteyeres.2020.100920.
10. Dysli C, Wolf S, Berezin MY, et al. Fluorescence lifetime imaging ophthalmoscopy[J]. Prog Retin Eye Res, 2017, 60: 120-143. DOI: 10.1016/j.preteyeres.2017.06.005.
11. Dysli C, Quellec G, Abegg M, et al. Quantitative analysis of fluorescence lifetime measurements of the macula using the fluorescence lifetime imaging ophthalmoscope in healthy subjects[J]. Invest Ophthalmol Vis Sci, 2014, 55(4): 2106-2113. DOI: 10.1167/iovs.13-13627.
12. Dysli C, Berger L, Wolf S, et al. Fundus autofluorescence lifetimes and central serous chorrioretinopathy[J]. Retina, 2017, 37(11): 2151-2161. DOI: 10.1097/IAE.0000000000001452.
13. Chandran K, Shenoy SB, Kulkarni C, et al. Bilateral simultaneous central retinal artery occlusion (CRAO) in a patient with systemic lupus erythematosus (SLE)[J/OL]. Am J Ophthalmol Case Rep, 2020, 19: 100833[2020-07-20]. https://pubmed.ncbi.nlm.nih.gov/32904183/. DOI: 10.1016/j.ajoc.2020.100833.
14. Ruggeri A, Poletti E, Fiorin D, et al. From laboratory to clinic: the development of web-based tools for the estimation of retinal diagnostic parameters[J]. Annu Int Conf IEEE Eng Med Biol Soc, 2011, 2011: 3379-3382. DOI: 10.1109/IEMBS.2011.6090915.
15. Fiorin D, Ruggeri A. Computerized analysis of narrow-field ROP images for the assessment of vessel caliber and tortuosity[J]. Annu Int Conf IEEE Eng Med Biol Soc, 2011, 2011: 2622-2625. DOI: 10.1109/IEMBS.2011.6090723.
16. Zekavat SM, Raghu VK, Trinder M, et al. Deep learning of the retina enables phenome-and genome-wide analyses of the microvasculature[J]. Circulation, 2022, 145(2): 134-150. DOI: 10.1161/CIRCULATIONAHA.121.057709.
17. 陈有信, 张碧磊, 张弘哲. 眼科人工智能技术的现状与问题[J]. 中华眼底病杂志, 2019, 35(2): 119-123. DOI: 10.3760/cma.j.issn.1005-1015.2019.02.003.Chen YX, Zhang BL, Zhang HZ. Insights and prospectives of ophthalmologic artificial intelligence technology[J]. Chin J Ocul Fundus Dis, 2019, 35(2): 119-123. DOI: 10.3760/cma.j.issn.1005-1015.2019.02.003.
18. Ryu G, Lee K, Park D, et al. A deep learning model for identifying diabetic retinopathy using optical coherence tomography angiography[J/OL]. Sci Rep, 2021, 11(1): 23024[2021-11-26]. https://pubmed.ncbi.nlm.nih.gov/34837030/. DOI: 10.1038/s41598-021-02479-6.
19. Lin D, Xiong J, Liu C, et al. Application of comprehensive artificial intelligence retinal expert (CARE) system: a national real-world evidence study[J/OL]. Lancet Digit Health, 2021, 3(8): e486-e495[2021-08-01]. https://pubmed.ncbi.nlm.nih.gov/34325853/. DOI: 10.1016/S2589-7500(21)00086-8.

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Chinese Journal of Ocular Fundus Diseases

The progress and problems of the fundus multimodal imaging

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