Skip to main navigation menu Skip to main content Skip to site footer

SOPHIA: System for Ophthalmic Image Acquisition, Transmission, and Intelligent Analysis


Ocular diseases are one of the main causes of irreversible disability in people in productive age. In 2020, approximately 18% of the worldwide population was estimated to suffer of diabetic retinopathy and diabetic macular edema, but, unfortunately, only half of these people were correctly diagnosed. On the other hand, in Colombia, the diabetic population (8% of the country’s total population) presents or has presented some ocular complication that has led to other associated costs and, in some cases, has caused vision limitation or blindness. Eye fundus images are the fastest and most economical source of ocular information that can provide a full clinical assessment of the retinal condition of patients. However, the number of ophthalmologists is insufficient and the clinical settings, as well as the attention of these experts, are limited to urban areas. Also, the analysis of said images by professionals requires extensive training, and even for experienced ones, it is a cumbersome and error-prone process. Deep learning methods have marked important breakthroughs in medical imaging due to outstanding performance in segmentation, detection, and disease classification tasks. This article presents SOPHIA, a deep learning-based system for ophthalmic image acquisition, transmission, intelligent analysis, and clinical decision support for the diagnosis of ocular diseases. The system is under active development in a project that brings together healthcare provider institutions, ophthalmology specialists, and computer scientists. Finally, the preliminary results in the automatic analysis of ocular images using deep learning are presented, as well as future work necessary for the implementation and validation of the system in Colombia.


clinical decision support, deep learning, intelligent analysis, ocular diseases, ophthalmic image acquisition, telemedicine

PDF PDF (Español) XML

Author Biography

Oscar Julián Perdomo-Charry, Ph. D.

Roles: Formal analysis, Research, Methodology, Writing – original draft.

Andrés Daniel Pérez-Pérez

Roles: Formal analysis, Research, Methodology, Writing – original draft.

Melissa de-la-Pava-Rodríguez

Roles: Formal analysis, Research, Methodology, Writing – original draft.

Hernán Andrés Ríos-Calixto

Roles: Conceptualization, Research, Validation.

Víctor Alfonso Arias-Vanegas

Roles: Formal analysis, Research, Methodology, Writing – original draft.

Juan Sebastián Lara-Ramírez

Roles: Formal analysis, Research, Methodology, Writing – original draft.

Santiago Toledo-Cortés, Ph. D. (c)

Roles: Formal analysis, Research, Methodology, Writing – original draft.

Jorge Eliecer Camargo-Mendoza, Ph. D.

Roles: Conceptualization, Methodology, Supervision, Writing - review & editing.

Francisco José Rodríguez-Alvira

Roles: Conceptualization, Research, Validation.

Fabio Augusto González-Osorio, Ph. D.

Roles: Conceptualization, Methodology, Supervision, Writing - review & editing.


[1] American Diabetes Association, “Classification and diagnosis of diabetes,” Diabetes Care, vol. 39 (1), S13-S22, 2016.

[2] M. Abràmoff, M. Garvin, and M. Sonka, "Retinal imaging and image analysis,” IEEE reviews in biomedical engineering, vol. 3, pp. 169-208, 2010.

[3] J. Köberlein, K. Beifus, C. Schaffert, and R. Finger, “The economic burden of visual impairment and blindness: a systematic review,” BMJ open, vol. 3 (11), e003471, 2013.

[4] G. Labiris, E. Panagiotopoulou, and V. Kozobolis, “A systematic review of teleophthalmological studies in Europe,” International journal of ophthalmology, vol. 11 (2), pp. 314-325, 2018.

[5] R. Gargeya, and T. Leng, “Automated identification of diabetic retinopathy using deep learning,” Ophthalmology, vol. 124 (7), pp. 962-969, 2017.

[6] S. Otálora, O. Perdomo, F. González, and H. Müller, “Training deep convolutional neural networks with active learning for exudate classification in eye fundus images,” In Intravascular Imaging and Computer Assisted Stenting, and Large-Scale Annotation of Biomedical Data and Expert Label Synthesis, pp. 146-154, 2017.

[7] C. Lam, C. Yu, L. Huang, and D. Rubin, “Retinal lesion detection with deep learning using image patches,” Investigative ophthalmology & visual science, vol. 59 (1), pp. 590-596, 2018.

[8] O. Perdomo, S. Otálora, F. Rodríguez, J. Arévalo, and F. González, “A novel machine learning model based on exudate localization to detect diabetic macular edema,” in Proceedings of the Ophthalmic Medical Image Analysis Third International Workshop, pp. 137-144, 2016.

[9] B. Host, A. Turner, and J. Muir, “Real‐time teleophthalmology video consultation: an analysis of patient satisfaction in rural Western Australia,” Clinical and Experimental Optometry, vol. 101 (1), pp. 129-134, 2018.

[10] J. Micheletti, A. Hendrick, F. Khan, D. Ziemer, and F. Pasquel, “Current and next generation portable screening devices for diabetic retinopathy,” Journal of diabetes science and technology, vol. 10 (2), pp. 295-300, 2016.

[11] W. Alyoubi, W. Shalash, and M. Abulkhair, “Diabetic retinopathy detection through deep learning techniques: A review,” Informatics in Medicine Unlocked, vol. 20, e100377, 2016.

[12] K. Stebbins, “Diabetic Retinal Examinations in Frontline Care Using RetinaVue Care Delivery Model,” Point of Care, vol. 18 (1), pp. 37-39, 2019.

[13] O. Perdomo, J. Arévalo, and F. González, “Convolutional network to detect exudates in eye fundus images of diabetic subjects,” in 12th International Symposium on Medical Information Processing and Analysis, 2017, e101600T.

[14] O. Perdomo, V. Andrearczyk, F. Meriaudeau, H. Müller, and F. González, “Glaucoma diagnosis from eye fundus images based on deep morphometric feature estimation,” in Computational pathology and ophthalmic medical image analysis, pp. 319-327, 2018.

[15] B. Graham, “Kaggle diabetic retinopathy detection competition report,” Master Thesis, University of Warwick, United Kingdom, 2015.

[16] K. Zhou, Z. Gu, A. Li, J. Cheng, S. Gao, and J. Liu, “Fundus image quality-guided diabetic retinopathy grading,” in Computational Pathology and Ophthalmic Medical Image Analysis, pp. 245-252, 2018.

[17] H. Fu, B. Wang, J. Shen, S. Cui, Y. Xu, J. Liu, and L. Shao, “Evaluation of retinal image quality assessment networks in different color-spaces,” in International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 48-56, 2019.

[18] E. Decencière, X. Zhang, G. Cazuguel, B. Lay, B. Cochener, C. Trone, P. Gain, R. Ordonez, P. Massin, A. Erginay, B. Charton and J-C. Klein, “Feedback on a publicly distributed image database: the Messidor database,” Image Analysis & Stereology, vol. 33 (3), pp. 231-234, 2014.

[19] C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, “Rethinking the inception architecture for computer vision,” in Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 2818-2826, 2016.

[20] O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. Berg and L. Fei-Fei, “Imagenet large scale visual recognition challenge,” International journal of computer vision, vol. 115 (3), pp. 211-252, 2015.

[21] T. Vu, C. Van Nguyen, T. Pham, T. Luu, and C. Yoo, “Fast and efficient image quality enhancement via desubpixel convolutional neural networks,” in Proceedings of the European Conference on Computer Vision (ECCV), 2018.

[22] J. Wan, D. Wang, S. Hoi, P. Wu, J. Zhu, Y. Zhang, and J. Li, “Deep learning for content-based image retrieval: A comprehensive study,” in Proceedings of the 22nd ACM international conference on Multimedia, pp. 157-166, 2014.

[23] Y. Cao, S. Steffey, J. He, D. Xiao, C. Tao, P. Chen, and H. Müller, “Medical image retrieval: a multimodal approach,” Cancer informatics, vol. 13, e14053, 2014.


Download data is not yet available.