Application of artificial intelligence based on multimodal fundus image data in the diagnosis and treatment of cardiovascular diseases_Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Authors：

WANG Yan ^1,2 , HE Xue ^1,2 , ZHAO Hanpeng ^2,3 , LI Cong ^1,2 , REN Yun ^2,4 , JIANG Jianrong ^2,3 , DU Zhenchao ^2,4 ,  YANG Xiaohong ^1,2

1. School of Medicine, South China University of Technology, Guangzhou, 510006, P. R. China;
2. Guangdong Eye Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, P. R. China;
3. Southern Medical University, Guangzhou, 510515, P. R. China;
4. Medical College of Shantou University, Shantou, 515041, Guangdong, P. R. China;

Corresponding?author：

YANG Xiaohong, Email: syyangxh@scut.edu.cn

Keywords：

Multimodal fundus imaging; artificial intelligence; cardiovascular diseases; diagnosis and treatment; review

DOI：

10.7507/1007-4848.202301021

Video：

Export PDF Favorites Scan Get Citation

Abstract Full text Figures/Tables Video References Cited by

Cardiovascular diseases is the leading cause of threat to human life and health worldwide. Early risk assessment, timely diagnosis, and prognosis evaluation are critical to the treatment of cardiovascular diseases. Currently, the evaluation of diagnosis and prognosis of cardiovascular diseases mainly relies on imaging examinations such as coronary CT and coronary angiography, which are expensive, time-consuming, partly invasive, and require high professional competence of the operator, making it difficult to promote in the community or in areas where medical resources are scarce. The fundus microcirculation is a part of the human microcirculation and has similar embryological origins and physiopathological features to cardiovascular circulation. Several studies have revealed fundus imaging biomarkers associated with cardiovascular diseases, and developed and validated intelligent diagnosis and treatment models for cardiovascular diseases based on fundus imaging data. Fundus imaging is expected to be an important adjunct to cardiovascular disease diagnosis and treatment given its noninvasive and convenient nature. The purpose of this review is to summarize the current research status, challenges, and future prospects of the application of artificial intelligence based on multimodal fundus imaging data in cardiovascular disease diagnosis and treatment.

Citation： WANG Yan, HE Xue, ZHAO Hanpeng, LI Cong, REN Yun, JIANG Jianrong, DU Zhenchao, YANG Xiaohong. Application of artificial intelligence based on multimodal fundus image data in the diagnosis and treatment of cardiovascular diseases. Chinese Journal of Clinical Thoracic and Cardiovascular Surgery, 2023, 30(9): 1344-1350. doi: 10.7507/1007-4848.202301021 Copy

1.	GBD 2019 Diseases and Injuries Collaborators. Global burden of 369 diseases and injuries in 204 countries and territories, 1990-2019: A systematic analysis for the Global Burden of Disease Study 2019. Lancet, 2020, 396(10258): 1204-1222.
2.	中國心血管健康與疾病報告編寫組. 中國心血管健康與疾病報告2021概要. 中國循環雜志, 2022, 37(6): 553-578.
3.	Flammer J, Konieczka K, Bruno RM, et al. The eye and the heart. Eur Heart J, 2013, 34(17): 1270-1278.
4.	Allon R, Aronov M, Belkin M, et al. Retinal microvascular signs as screening and prognostic factors for cardiac disease: A systematic review of current evidence. Am J Med, 2021, 134(1): 36-47.e7.
5.	Wang SB, Mitchell P, Liew G, et al. A spectrum of retinal vasculature measures and coronary artery disease. Atherosclerosis, 2018, 268: 215-224.
6.	Chua J, Chin C, Hong J, et al. Impact of hypertension on retinal capillary microvasculature using optical coherence tomographic angiography. J Hypertens, 2019, 37(3): 572-580.
7.	Zhang K, Liu X, Xu J, et al. Deep-learning models for the detection and incidence prediction of chronic kidney disease and type 2 diabetes from retinal fundus images. Nat Biomed Eng, 2021, 5(6): 533-545.
8.	Poplin R, Varadarajan AV, Blumer K, et al. Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning. Nat Biomed Eng, 2018, 2(3): 158-164.
9.	Alexopoulos P, Madu C, Wollstein G, et al. The development and clinical application of innovative optical ophthalmic imaging techniques. Front Med (Lausanne), 2022, 9: 891369.
10.	Vienola KV, Braaf B, Sheehy CK, et al. Real-time eye motion compensation for OCT imaging with tracking SLO. Biomed Opt Express, 2012, 3(11): 2950-2963.
11.	Sun Z, Yang D, Tang Z, et al. Optical coherence tomography angiography in diabetic retinopathy: An updated review. Eye (Lond), 2021, 35(1): 149-161.
12.	Schütze C, Teleky K, Baumann B, et al. Polarisation-sensitive OCT is useful for evaluating retinal pigment epithelial lesions in patients with neovascular AMD. Br J Ophthalmol, 2016, 100(3): 371-377.
13.	Hong YJ, Miura M, Ju MJ, et al. Simultaneous investigation of vascular and retinal pigment epithelial pathologies of exudative macular diseases by multifunctional optical coherence tomography. Invest Ophthalmol Vis Sci, 2014, 55(8): 5016-5031.
14.	Feuer DS, Handberg EM, Mehrad B, et al. Microvascular dysfunction as a systemic disease: A review of the evidence. Am J Med, 2022, 135(9): 1059-1068.
15.	Zekavat SM, Raghu VK, Trinder M, et al. Deep learning of the retina enables phenome- and genome-wide analyses of the microvasculature. Circulation, 2022, 145(2): 134-150.
16.	Cheung CY, Biousse V, Keane PA, et al. Hypertensive eye disease. Nat Rev Dis Primers, 2022, 8(1): 14.
17.	婁瑩, 馬文君, 王子君, 等. 中國高血壓臨床實踐指南計劃書. 中華心血管病雜志, 2022, 50(7): 671-675.
18.	Peng Q, Hu Y, Huang M, et al. Retinal neurovascular impairment in patients with essential hypertension: An optical coherence tomography angiography study. Invest Ophthalmol Vis Sci, 2020, 61(8): 42.
19.	Sun C, Ladores C, Hong J, et al. Systemic hypertension associated retinal microvascular changes can be detected with optical coherence tomography angiography. Sci Rep, 2020, 10(1): 9580.
20.	彭慶晟, 吳櫻, 黃漫清, 等. 不同降壓方案治療高血壓人群的視網膜光學相干斷層掃描血管成像特征. 實用醫學雜志, 2020, 36(7): 930-935.
21.	Ziegler T, Abdel Rahman F, Jurisch V, et al. Atherosclerosis and the capillary network; Pathophysiology and potential therapeutic strategies. Cells, 2019, 9(1): 50.
22.	Zhong P, Li Z, Lin Y, et al. Retinal microvasculature impairments in patients with coronary artery disease: An optical coherence tomography angiography study. Acta Ophthalmol, 2022, 100(2): 225-233.
23.	Aschauer J, Aschauer S, Pollreisz A, et al. Identification of subclinical microvascular biomarkers in coronary heart disease in retinal imaging. Transl Vis Sci Technol, 2021, 10(13): 24.
24.	Matulevi?iūt? I, Sidarait? A, Tatarūnas V, et al. Retinal and choroidal thinning—A predictor of coronary artery occlusion. Diagnostics (Basel), 2022, 12(8) : 2016.
25.	Kim JH, Kim SE, Kim SH, et al. Relationship between coronary artery calcification and central chorioretinal thickness in patients with subclinical atherosclerosis. Ophthalmologica, 2021, 244(1): 18-26.
26.	Yeung SC, You Y, Howe KL, et al. Choroidal thickness in patients with cardiovascular disease: A review. Surv Ophthalmol, 2020, 65(4): 473-486.
27.	高漫辰, 張鳳文, 潘湘斌. 《中國心血管健康與疾病報告2019》先天性心臟病部分解讀. 中國胸心血管外科臨床雜志, 2021, 28(4): 384-387.
28.	P Vilela MA, Colossi CG, Freitas HP, et al. Ocular alterations associated with primary congenital heart disease—A cross-sectional study. Middle East Afr J Ophthalmol, 2020, 27(1): 28-33.
29.	Li C, Zhong P, Yuan H, et al. Retinal microvasculature impairment in patients with congenital heart disease investigated by optical coherence tomography angiography. Clin Exp Ophthalmol, 2020, 48(9): 1219-1228.
30.	Li C, Zhu Z, Yuan H, et al. Improved retinal microcirculation after cardiac surgery in patients with congenital heart disease. Front Cardiovasc Med, 2021, 8: 712308.
31.	Cordina R, Leaney J, Golzan M, et al. Ophthalmological consequences of cyanotic congenital heart disease: Vascular parameters and nerve fibre layer. Clin Exp Ophthalmol, 2015, 43(2): 115-123.
32.	邱海龍, 郭惠明, 姚澤陽, 等. 人工智能在心血管醫學中的應用. 中國胸心血管外科臨床雜志, 2021, 28(10): 1160-1166.
33.	Xiong J, Li F, Song D, et al. Multimodal machine learning using visual fields and peripapillary circular OCT scans in detection of glaucomatous optic neuropathy. Ophthalmology, 2022, 129(2): 171-180.
34.	Sandhu HS, Elmogy M, Taher Sharafeldeen A, et al. Automated diagnosis of diabetic retinopathy using clinical biomarkers, optical coherence tomography, and optical coherence tomography angiography. Am J Ophthalmol, 2020, 216: 201-206.
35.	Zhu Z, Shi D, Guankai P, et al. Retinal age gap as a predictive biomarker for mortality risk. Br J Ophthalmol, 2023, 107(4): 547-554.
36.	Zhu Z, Chen Y, Wang W, et al. Association of retinal age gap with arterial stiffness and incident cardiovascular disease. Stroke, 2022, 53(11): 3320-3328.
37.	Cheung CY, Xu D, Cheng CY, et al. A deep-learning system for the assessment of cardiovascular disease risk via the measurement of retinal-vessel calibre. Nat Biomed Eng, 2021, 5(6): 498-508.
38.	Rim TH, Lee CJ, Tham YC, et al. Deep-learning-based cardiovascular risk stratification using coronary artery calcium scores predicted from retinal photographs. Lancet Digit Health, 2021, 3(5): e306-e316.
39.	Son J, Shin JY, Chun EJ, et al. Predicting high coronary artery calcium score from retinal fundus images with deep learning algorithms. Transl Vis Sci Technol, 2020, 9(6): 28.
40.	Zhong P, Qin J, Li Z, et al. Development and validation of retinal vasculature nomogram in suspected angina due to coronary artery disease. J Atheroscler Thromb, 2022, 29(5): 579-596.
41.	Al-Absi H, Islam MT, Refaee MA, et al. Cardiovascular disease diagnosis from DXA scan and retinal images using deep learning. Sensors (Basel), 2022, 22(12): 4310.
42.	Seidelmann SB, Claggett B, Bravo PE, et al. Retinal vessel calibers in predicting long-term cardiovascular outcomes: The Atherosclerosis Risk in Communities Study. Circulation, 2016, 134(18): 1328-1338.
43.	Chang J, Ko A, Park SM, et al. Association of cardiovascular mortality and deep learning-funduscopic atherosclerosis score derived from retinal fundus images. Am J Ophthalmol, 2020, 217: 121-130.
44.	Wing WY, Liang HC, Peng QS, et al. An automatic framework for perioperative risks classification from retinal images of complex congenital heart disease patients. Inter J Mach Learn Cybernet, 2021, 1-13.
45.	李聰, 孔令聰, 胡聯亭, 等. 基于光學相干斷層掃描血管成像技術智能預測先天性心臟病圍術期轉歸的臨床研究. 實用醫學雜志, 2022, 38(9): 1136-1140.
46.	Wang Q, Zhou Y. FedSPL: Federated self-paced learning for privacy-preserving disease diagnosis. Brief Bioinform, 2022, 23(1): bbab498.
47.	Yang G, Ye Q, Xia J. Unbox the black-box for the medical explainable AI via multi-modal and multi-centre data fusion: A mini-review, two showcases and beyond. Inf Fusion, 2022, 77: 29-52.

1. GBD 2019 Diseases and Injuries Collaborators. Global burden of 369 diseases and injuries in 204 countries and territories, 1990-2019: A systematic analysis for the Global Burden of Disease Study 2019. Lancet, 2020, 396(10258): 1204-1222.
2. 中國心血管健康與疾病報告編寫組. 中國心血管健康與疾病報告2021概要. 中國循環雜志, 2022, 37(6): 553-578.
3. Flammer J, Konieczka K, Bruno RM, et al. The eye and the heart. Eur Heart J, 2013, 34(17): 1270-1278.
4. Allon R, Aronov M, Belkin M, et al. Retinal microvascular signs as screening and prognostic factors for cardiac disease: A systematic review of current evidence. Am J Med, 2021, 134(1): 36-47.e7.
5. Wang SB, Mitchell P, Liew G, et al. A spectrum of retinal vasculature measures and coronary artery disease. Atherosclerosis, 2018, 268: 215-224.
6. Chua J, Chin C, Hong J, et al. Impact of hypertension on retinal capillary microvasculature using optical coherence tomographic angiography. J Hypertens, 2019, 37(3): 572-580.
7. Zhang K, Liu X, Xu J, et al. Deep-learning models for the detection and incidence prediction of chronic kidney disease and type 2 diabetes from retinal fundus images. Nat Biomed Eng, 2021, 5(6): 533-545.
8. Poplin R, Varadarajan AV, Blumer K, et al. Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning. Nat Biomed Eng, 2018, 2(3): 158-164.
9. Alexopoulos P, Madu C, Wollstein G, et al. The development and clinical application of innovative optical ophthalmic imaging techniques. Front Med (Lausanne), 2022, 9: 891369.
10. Vienola KV, Braaf B, Sheehy CK, et al. Real-time eye motion compensation for OCT imaging with tracking SLO. Biomed Opt Express, 2012, 3(11): 2950-2963.
11. Sun Z, Yang D, Tang Z, et al. Optical coherence tomography angiography in diabetic retinopathy: An updated review. Eye (Lond), 2021, 35(1): 149-161.
12. Schütze C, Teleky K, Baumann B, et al. Polarisation-sensitive OCT is useful for evaluating retinal pigment epithelial lesions in patients with neovascular AMD. Br J Ophthalmol, 2016, 100(3): 371-377.
13. Hong YJ, Miura M, Ju MJ, et al. Simultaneous investigation of vascular and retinal pigment epithelial pathologies of exudative macular diseases by multifunctional optical coherence tomography. Invest Ophthalmol Vis Sci, 2014, 55(8): 5016-5031.
14. Feuer DS, Handberg EM, Mehrad B, et al. Microvascular dysfunction as a systemic disease: A review of the evidence. Am J Med, 2022, 135(9): 1059-1068.
15. Zekavat SM, Raghu VK, Trinder M, et al. Deep learning of the retina enables phenome- and genome-wide analyses of the microvasculature. Circulation, 2022, 145(2): 134-150.
16. Cheung CY, Biousse V, Keane PA, et al. Hypertensive eye disease. Nat Rev Dis Primers, 2022, 8(1): 14.
17. 婁瑩, 馬文君, 王子君, 等. 中國高血壓臨床實踐指南計劃書. 中華心血管病雜志, 2022, 50(7): 671-675.
18. Peng Q, Hu Y, Huang M, et al. Retinal neurovascular impairment in patients with essential hypertension: An optical coherence tomography angiography study. Invest Ophthalmol Vis Sci, 2020, 61(8): 42.
19. Sun C, Ladores C, Hong J, et al. Systemic hypertension associated retinal microvascular changes can be detected with optical coherence tomography angiography. Sci Rep, 2020, 10(1): 9580.
20. 彭慶晟, 吳櫻, 黃漫清, 等. 不同降壓方案治療高血壓人群的視網膜光學相干斷層掃描血管成像特征. 實用醫學雜志, 2020, 36(7): 930-935.
21. Ziegler T, Abdel Rahman F, Jurisch V, et al. Atherosclerosis and the capillary network; Pathophysiology and potential therapeutic strategies. Cells, 2019, 9(1): 50.
22. Zhong P, Li Z, Lin Y, et al. Retinal microvasculature impairments in patients with coronary artery disease: An optical coherence tomography angiography study. Acta Ophthalmol, 2022, 100(2): 225-233.
23. Aschauer J, Aschauer S, Pollreisz A, et al. Identification of subclinical microvascular biomarkers in coronary heart disease in retinal imaging. Transl Vis Sci Technol, 2021, 10(13): 24.
24. Matulevi?iūt? I, Sidarait? A, Tatarūnas V, et al. Retinal and choroidal thinning—A predictor of coronary artery occlusion. Diagnostics (Basel), 2022, 12(8) : 2016.
25. Kim JH, Kim SE, Kim SH, et al. Relationship between coronary artery calcification and central chorioretinal thickness in patients with subclinical atherosclerosis. Ophthalmologica, 2021, 244(1): 18-26.
26. Yeung SC, You Y, Howe KL, et al. Choroidal thickness in patients with cardiovascular disease: A review. Surv Ophthalmol, 2020, 65(4): 473-486.
27. 高漫辰, 張鳳文, 潘湘斌. 《中國心血管健康與疾病報告2019》先天性心臟病部分解讀. 中國胸心血管外科臨床雜志, 2021, 28(4): 384-387.
28. P Vilela MA, Colossi CG, Freitas HP, et al. Ocular alterations associated with primary congenital heart disease—A cross-sectional study. Middle East Afr J Ophthalmol, 2020, 27(1): 28-33.
29. Li C, Zhong P, Yuan H, et al. Retinal microvasculature impairment in patients with congenital heart disease investigated by optical coherence tomography angiography. Clin Exp Ophthalmol, 2020, 48(9): 1219-1228.
30. Li C, Zhu Z, Yuan H, et al. Improved retinal microcirculation after cardiac surgery in patients with congenital heart disease. Front Cardiovasc Med, 2021, 8: 712308.
31. Cordina R, Leaney J, Golzan M, et al. Ophthalmological consequences of cyanotic congenital heart disease: Vascular parameters and nerve fibre layer. Clin Exp Ophthalmol, 2015, 43(2): 115-123.
32. 邱海龍, 郭惠明, 姚澤陽, 等. 人工智能在心血管醫學中的應用. 中國胸心血管外科臨床雜志, 2021, 28(10): 1160-1166.
33. Xiong J, Li F, Song D, et al. Multimodal machine learning using visual fields and peripapillary circular OCT scans in detection of glaucomatous optic neuropathy. Ophthalmology, 2022, 129(2): 171-180.
34. Sandhu HS, Elmogy M, Taher Sharafeldeen A, et al. Automated diagnosis of diabetic retinopathy using clinical biomarkers, optical coherence tomography, and optical coherence tomography angiography. Am J Ophthalmol, 2020, 216: 201-206.
35. Zhu Z, Shi D, Guankai P, et al. Retinal age gap as a predictive biomarker for mortality risk. Br J Ophthalmol, 2023, 107(4): 547-554.
36. Zhu Z, Chen Y, Wang W, et al. Association of retinal age gap with arterial stiffness and incident cardiovascular disease. Stroke, 2022, 53(11): 3320-3328.
37. Cheung CY, Xu D, Cheng CY, et al. A deep-learning system for the assessment of cardiovascular disease risk via the measurement of retinal-vessel calibre. Nat Biomed Eng, 2021, 5(6): 498-508.
38. Rim TH, Lee CJ, Tham YC, et al. Deep-learning-based cardiovascular risk stratification using coronary artery calcium scores predicted from retinal photographs. Lancet Digit Health, 2021, 3(5): e306-e316.
39. Son J, Shin JY, Chun EJ, et al. Predicting high coronary artery calcium score from retinal fundus images with deep learning algorithms. Transl Vis Sci Technol, 2020, 9(6): 28.
40. Zhong P, Qin J, Li Z, et al. Development and validation of retinal vasculature nomogram in suspected angina due to coronary artery disease. J Atheroscler Thromb, 2022, 29(5): 579-596.
41. Al-Absi H, Islam MT, Refaee MA, et al. Cardiovascular disease diagnosis from DXA scan and retinal images using deep learning. Sensors (Basel), 2022, 22(12): 4310.
42. Seidelmann SB, Claggett B, Bravo PE, et al. Retinal vessel calibers in predicting long-term cardiovascular outcomes: The Atherosclerosis Risk in Communities Study. Circulation, 2016, 134(18): 1328-1338.
43. Chang J, Ko A, Park SM, et al. Association of cardiovascular mortality and deep learning-funduscopic atherosclerosis score derived from retinal fundus images. Am J Ophthalmol, 2020, 217: 121-130.
44. Wing WY, Liang HC, Peng QS, et al. An automatic framework for perioperative risks classification from retinal images of complex congenital heart disease patients. Inter J Mach Learn Cybernet, 2021, 1-13.
45. 李聰, 孔令聰, 胡聯亭, 等. 基于光學相干斷層掃描血管成像技術智能預測先天性心臟病圍術期轉歸的臨床研究. 實用醫學雜志, 2022, 38(9): 1136-1140.
46. Wang Q, Zhou Y. FedSPL: Federated self-paced learning for privacy-preserving disease diagnosis. Brief Bioinform, 2022, 23(1): bbab498.
47. Yang G, Ye Q, Xia J. Unbox the black-box for the medical explainable AI via multi-modal and multi-centre data fusion: A mini-review, two showcases and beyond. Inf Fusion, 2022, 77: 29-52.

Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Application of artificial intelligence based on multimodal fundus image data in the diagnosis and treatment of cardiovascular diseases

Abstract Full text Figures/Tables Video References Cited by

Previous Article

Next Article

Format

Content