A non-invasive approach for diagnosing diabetic retinopathy using stacked ensemble deep learning mechanism ‎incidental towards chronic kidney disease

Authors

  • Kalyani chapa Research Scholar, Department of Computer Science and Engineering, GITAM (Deemed to be University), ‎ Visakhapatnam, Andhra Pradesh, India
  • Dr. Bhramaramba Ravi Professor, Department of Computer Science and Engineering, GITAM (Deemed to be University),‎ Visakhapatnam, Andhra Pradesh, India‎

How to Cite

chapa, K., & Ravi , D. B. . (2025). A non-invasive approach for diagnosing diabetic retinopathy using stacked ensemble deep learning mechanism ‎incidental towards chronic kidney disease. International Journal of Basic and Applied Sciences, 14(1), 65-73. https://doi.org/10.14419/ms00zj36

Received date: March 14, 2025

Accepted date: April 12, 2025

Published date: April 15, 2025

DOI:

https://doi.org/10.14419/ms00zj36

Keywords:

Retina-Based CKD Diagnosis; Diabetic Retinopathy Diagnosis; Non-Invasive Techniques in CKD Prediction.

Abstract

Chronic kidney disease (CKD) is progressive in several parts of the world. The prevalence of CKD is rapidly increasing due to dis-‎eases such as hypertension and diabetes, the two major causes of CKD, and due to the global aging population. Non-invasive methods play ‎an important role in identifying CKD. Retinal photography nowadays is a source that provides vital information related to the eye as well ‎as systemic vasculature and acts as a non-invasive diagnostic test. The physiological, developmental, and pathogenic pathway information ‎shared by the eye and the kidney seems to be similar. With the advent of Deep Learning, the detection of CKD and diabetic retinopathy is taking new ‎horizons. In this paper, a novel mechanism is proposed for identifying Diabetic Retinopathy, which highly influences CKD progression. ‎Also, CKD prediction is done using retina images. To accomplish a notable detection, A lightweight and efficient Transfer Learning based ‎architecture, “RIX Net,” has been employed in classifying diabetic retinopathy severity as well as CKD. Five datasets of various sizes with ‎respect to Diabetic Retinopathy and one corresponding retinal dataset to CKD have been used to measure the performance of the proposed ‎architecture. The architecture performed well with around 90% performance in all cases‎.

References

  1. Farrah, T. E., Dhillon, B., Keane, P. A., Webb, D. J., & Dhaun, N, “The eye, the kidney, and cardiovascular disease: old concepts, better tools, and new horizons”, Kidney International, 98(2), 2020, 323–342. https://doi.org/10.1016/j.kint.2020.01.039.
  2. Grzybowski, A., Jin, K., Zhou, J., Pan, X., Wang, M., Ye, J., & Wong, T. Y,“Retina Fundus Photograph-Based Artificial Intelligence Algorithms in Medicine: A Systematic Review”, Ophthalmology and Therapy, 13(8), 2024, 2125–2149. https://doi.org/10.1007/s40123-024-00981-4.
  3. Mitani, A., Hammel, N., & Liu, Y, “Retinal detection of kidney disease and diabetes”, Nature Biomedical Engineering, 5(6), 2021, 487–489. https://doi.org/10.1038/s41551-021-00747-4.
  4. Wen, J., Liu, D., Wu, Q., Zhao, L., Iao, W. C., & Lin, H, “Retinal image‐based artificial intelligence in detecting and predicting kidney diseases: Current advances and future perspectives”, View, 2023, 4(3). https://doi.org/10.1002/VIW.20220070.
  5. Haq, N. U., Waheed, T., Ishaq, K., Hassan, M. A., Safie, N., Elias, N. F., & Shoaib, M, “Computationally efficient deep learning models for diabet-ic retinopathy detection: a systematic literature review”, Artificial Intelligence Review, 2024, 57(11). https://doi.org/10.1007/s10462-024-10942-9.
  6. Rim, T. H., Lee, G., Kim, Y., Tham, Y., Lee, C. J., Baik, S. J., Kim, Y. A., Yu, M., Deshmukh, M., Lee, B. K., Park, S., Kim, H. C., Sabayanagam, C., Ting, D. S. W., Wang, Y. X., Jonas, J. B., Kim, S. S., Wong, T. Y., & Cheng, C, “Prediction of systemic biomarkers from retinal photographs: development and validation of deep-learning algorithms” The Lancet Digital Health, 2(10), 2020, e526–e536. https://doi.org/10.1016/S2589-7500(20)30216-8.
  7. Amir Hamzah NA, Wan Zaki WMD, Wan Abdul Halim WH, Mustafar R, Saad AH, “Evaluating the potential of retinal photography in chronic kidney disease detection: a review’, PeerJ. 12, 2024, e17786. https://doi.org/10.7717/peerj.17786.
  8. Young Su Joo, Tyler Hyungtaek Rim,Hee Byung Koh, Joseph Yi, Hyeonmin Kim, Geunyoung Lee, Young Ah Kim, Shin-Wook Kang, Sung Soo Kim and Jung Tak Park, “Non-invasive chronic kidney disease risk stratification tool derived from retina-based deep learning and clinical factors”, Npj Digital Medicine, 2023, 6(1). https://doi.org/10.1038/s41746-023-00860-5.
  9. Sabanayagam, C., Xu, D., Ting, D. S. W., Nusinovici, S., Banu, R., Hamzah, H., Lim, C., Tham, Y., Cheung, C. Y., Tai, E. S., Wang, Y. X., Jonas, J. B., Cheng, C., Lee, M. L., Hsu, W., & Wong, T. Y, “A deep learning algorithm to detect chronic kidney disease from retinal photographs in community-based populations”, The Lancet Digital Health, 2(6), 2020, e295–e302. https://doi.org/10.1016/S2589-7500(20)30063-7.
  10. Betzler, B. K., Chee, E. Y. L., He, F., Lim, C. C., Ho, J., Hamzah, H., Tan, N. C., Liew, G., McKay, G. J., Hogg, R. E., Young, I. S., Cheng, C., Lim, S. C., Lee, A. Y., Wong, T. Y., Lee, M. L., Hsu, W., Tan, G. S. W., & Sabanayagam, C, “Deep learning algorithms to detect diabetic kidney disease from retinal photographs in multiethnic populations with diabetes”, Journal of the American Medical Informatics Association, 30(12), 2023, 1904–1914. https://doi.org/10.1093/jamia/ocad179.
  11. An, S., Vaghefi, E., Yang, S., Xie, L., & Squirrell, D, “Examination of alternative eGFR definitions on the performance of deep learning models for detection of chronic kidney disease from fundus photographs”, PLoS ONE, 18(11), 2023, e0295073. https://doi.org/10.1371/journal.pone.0295073.
  12. Zhang, K., Liu, X., Xu, J., Yuan, J., Cai, W., Chen, T., Wang, K., Gao, Y., Nie, S., Xu, X., Qin, X., Su, Y., Xu, W., Olvera, A., Xue, K., Li, Z., Zhang, M., Zeng, X., Zhang, C. L., . . . Wang, G, “Deep-learning models for the detection and incidence prediction of chronic kidney disease and type 2 diabetes from retinal fundus images”, Nature Biomedical Engineering, 5(6), 2021, 533–545. https://doi.org/10.1038/s41551-021-00745-6.
  13. Nadeem, M. W., Goh, H. G., Hussain, M., Liew, S., Andonovic, I., & Khan, M. A, “Deep Learning for Diabetic Retinopathy Analysis: A review, research challenges, and future directions”, Sensors, 22(18), 2022, 6780. https://doi.org/10.3390/s22186780.
  14. Bhandari, S., Pathak, S., & Jain, S. A, “A literature review of Early-Stage Diabetic Retinopathy Detection using deep learning and Evolutionary Computing techniques”, Archives of Computational Methods in Engineering, 30(2), 2022, 799–810. https://doi.org/10.1007/s11831-022-09816-6.
  15. Vij, R., & Arora, S, “A Systematic Review on Diabetic Retinopathy Detection using Deep Learning techniques”, Archives of Computational Meth-ods in Engineering, 30(3), 2022, 2211–2256. https://doi.org/10.1007/s11831-022-09862-0.
  16. Grzybowski, A., Brona, P., Lim, G., Ruamviboonsuk, P., Tan, G. S. W., Abramoff, M., & Ting, D. S. W, “Artificial intelligence for diabetic reti-nopathy screening: a review”, Eye, 34(3), 2019, 451–460. https://doi.org/10.1038/s41433-019-0566-0.
  17. Nanegrungsunk, O., Ruamviboonsuk, P., & Grzybowski, A, “Prospective studies on artificial intelligence (AI)-based diabetic retinopathy screen-ing”, Annals of Translational Medicine, 10(24), 2022, 1297. https://doi.org/10.21037/atm-2022-71.
  18. Bermejo, S., González, E., López-Revuelta, K., Ibernon, M., López, D., Martín-Gómez, A., Garcia-Osuna, R., Linares, T., Díaz, M., Martín, N., Barros, X., Marco, H., Navarro, M. I., Esparza, N., Elias, S., Coloma, A., Robles, N. R., Agraz, I., Poch, E., . . . Soler, M. J, “The coexistence of di-abetic retinopathy and diabetic nephropathy is associated with worse kidney outcomes”, Clinical Kidney Journal, 16(10), 2023, 1656–1663. https://doi.org/10.1093/ckj/sfad142.
  19. Lee, G. W., Lee, C. H., & Kim, S. G. (2022), “Association of advanced chronic kidney disease with diabetic retinopathy severity in older patients with diabetes: a retrospective cross-sectional study”, Journal of Yeungnam Medical Science, 40(2), 146–155. https://doi.org/10.12701/jyms.2022.00206.
  20. Wang, D., Fan, K., He, Z., Guo, X., Gong, X., Xiong, K., Wei, D., Chen, B., Kong, F., Liao, M., Wang, W., Huang, W., & Liu, H, “The relation-ship between renal function and diabetic retinopathy in patients with type 2 diabetes: A three-year prospective study”, Heliyon, 9(4), 2023, e14662. https://doi.org/10.1016/j.heliyon.2023.e14662.
  21. Yan, Y., Yu, L., Sun, C., Zhao, H., Zhang, H., & Wang, Z, “Retinal microvascular changes in diabetic patients with diabetic nephropathy”, BMC Endocrine Disorders, 2023, 23(1). https://doi.org/10.1186/s12902-022-01250-w.
  22. Nusinovici, S., Sabanayagam, C., Lee, K. E., Zhang, L., Cheung, C. Y., Tai, E. S., Tan, G. S. W., Cheng, C. Y., Klein, B. E. K., & Wong, T. Y, “Retinal microvascular signs and risk of diabetic kidney disease in asian and white populations”, Scientific Reports, 11(1), 2021. https://doi.org/10.1038/s41598-021-84464-7.
  23. Ramasamy, L. K., Padinjappurathu, S. G., Kadry, S., & Damaševičius, R, “Detection of diabetic retinopathy using a fusion of textural and ridgelet features of retinal images and sequential minimal optimization classifier”, PeerJ Computer Science, 7, 2021, e456. https://doi.org/10.7717/peerj-cs.456.
  24. Kang, E. Y., Hsieh, Y., Li, C., Huang, Y., Kuo, C., Kang, J., Chen, K., Lai, C., Wu, W., & Hwang, Y, “Deep Learning–Based detection of early renal function impairment using Retinal Fundus images: model development and validation”, JMIR Medical Informatics, 8(11), 2020, e23472. https://doi.org/10.2196/23472.
  25. Zhao, L., Ren, H., Zhang, J., Cao, Y., Wang, Y., Meng, D., Wu, Y., Zhang, R., Zou, Y., Xu, H., Li, L., Zhang, J., Cooper, M. E., Tong, N., & Liu, F, “Diabetic retinopathy, classified using the Lesion-Aware deep learning System, predicts diabetic End-Stage renal disease in Chinese patients”, Endocrine Practice, 26(4), 2020, 429–443. https://doi.org/10.4158/EP-2019-0512.
  26. Yamanouchi, M., Mori, M., Hoshino, J., Kinowaki, K., Fujii, T., Ohashi, K., Furuichi, K., Wada, T., & Ubara, Y, “Retinopathy progression and the risk of end-stage kidney disease: results from a longitudinal Japanese cohort of 232 patients with type 2 diabetes and biopsy-proven diabetic kidney disease”, BMJ Open Diabetes Research & Care, 7(1), 2019, e000726. https://doi.org/10.1136/bmjdrc-2019-000726.
  27. Shi, S., Gao, L., Zhang, J., Zhang, B., Xiao, J., Xu, W., Tian, Y., Ni, L., & Wu, X, “The automatic detection of diabetic kidney disease from retinal vascular parameters combined with clinical variables using artificial intelligence in type-2 diabetes patients”, BMC Medical Informatics and Decision Making, 23(1), 2023. https://doi.org/10.1186/s12911-023-02343-9.
  28. Zhao, X., Gu, X., Meng, L., Chen, Y., Zhao, Q., Cheng, S., Zhang, W., Cheng, T., Wang, C., Shi, Z., Jiao, S., Jiang, C., Jiao, G., Teng, D., Sun, X., Zhang, B., Li, Y., Lu, H., Chen, C., . . . Chen, Y, “Screening chronic kidney disease through deep learning utilizing ultra-wide-field fundus images”, Npj Digital Medicine, 7(1), 2024. https://doi.org/10.1038/s41746-024-01271-w.
  29. Zhang, M., Ye, Z., Yuan, E., Lv, X., Zhang, Y., Tan, Y., Xia, C., Tang, J., Huang, J., & Li, Z, “Imaging-based deep learning in kidney diseases: recent progress and future prospects”, Insights Into Imaging, 15(1), 2024. https://doi.org/10.1186/s13244-024-01636-5.
  30. J, M. S., & Sharat, S, “The renal-retinal connection - The eye in chronic kidney disease”, IP International Journal of Ocular Oncology and Oculo-plasty, 10(3), 2024, 158–165. https://doi.org/10.18231/j.ijooo.2024.030.
  31. P. K. Sekharamantry, M. S. Rao, Y. Srinivas, and A. Uriti, “PSR-LeafNet: a deep learning framework for identifying medicinal plant leaves using support vector machines,” Big Data and Cognitive Computing, vol. 8, no. 12, p. 176, Dec. 2024. https://doi.org/10.3390/bdcc8120176.

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How to Cite

chapa, K., & Ravi , D. B. . (2025). A non-invasive approach for diagnosing diabetic retinopathy using stacked ensemble deep learning mechanism ‎incidental towards chronic kidney disease. International Journal of Basic and Applied Sciences, 14(1), 65-73. https://doi.org/10.14419/ms00zj36

Received date: March 14, 2025

Accepted date: April 12, 2025

Published date: April 15, 2025