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

Abstract 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...

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Autores:
Perdomo Charry, Oscar Julián
Pérez, Andrés Daniel
De la Pava Rodríguez, Melissa
Ríos Calixto, Hernán Andrés
Arias Vanegas, Víctor Alfonso
Lara Ramírez, Juan Sebastián
Toledo Cortés, Santiago
Camargo Mendoza, Jorge Eliecer
Rodríguez Alvira, Francisco José
González Osorio, Fabio Augusto
Tipo de recurso:
Article of journal
Fecha de publicación:
2020
Institución:
Escuela Colombiana de Ingeniería Julio Garavito
Repositorio:
Repositorio Institucional ECI
Idioma:
eng
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oai:repositorio.escuelaing.edu.co:001/3305
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https://repositorio.escuelaing.edu.co/handle/001/3305
https://repositorio.escuelaing.edu.co/
Palabra clave:
Trastornos de la visión
Vision disorders
Óptica fisiológica
Physiological optics
Imágenes ópticas
Optical images
Apoyo a la decisión clínica
Aprendizaje profundo
Análisis inteligente
Enfermedades oculares
Adquisición de imágenes oftálmicas
Telemedicina
Clinical decision support
Deep learning
Intelligent analysis
Ocular diseases
Ophthalmic image acquisition
Telemedicine
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closedAccess
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http://purl.org/coar/access_right/c_14cb
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dc.title.eng.fl_str_mv SOPHIA: System for Ophthalmic Image Acquisition, Transmission, and Intelligent Analysis
title SOPHIA: System for Ophthalmic Image Acquisition, Transmission, and Intelligent Analysis
spellingShingle SOPHIA: System for Ophthalmic Image Acquisition, Transmission, and Intelligent Analysis
Trastornos de la visión
Vision disorders
Óptica fisiológica
Physiological optics
Imágenes ópticas
Optical images
Apoyo a la decisión clínica
Aprendizaje profundo
Análisis inteligente
Enfermedades oculares
Adquisición de imágenes oftálmicas
Telemedicina
Clinical decision support
Deep learning
Intelligent analysis
Ocular diseases
Ophthalmic image acquisition
Telemedicine
title_short SOPHIA: System for Ophthalmic Image Acquisition, Transmission, and Intelligent Analysis
title_full SOPHIA: System for Ophthalmic Image Acquisition, Transmission, and Intelligent Analysis
title_fullStr SOPHIA: System for Ophthalmic Image Acquisition, Transmission, and Intelligent Analysis
title_full_unstemmed SOPHIA: System for Ophthalmic Image Acquisition, Transmission, and Intelligent Analysis
title_sort SOPHIA: System for Ophthalmic Image Acquisition, Transmission, and Intelligent Analysis
dc.creator.fl_str_mv Perdomo Charry, Oscar Julián
Pérez, Andrés Daniel
De la Pava Rodríguez, Melissa
Ríos Calixto, Hernán Andrés
Arias Vanegas, Víctor Alfonso
Lara Ramírez, Juan Sebastián
Toledo Cortés, Santiago
Camargo Mendoza, Jorge Eliecer
Rodríguez Alvira, Francisco José
González Osorio, Fabio Augusto
dc.contributor.author.none.fl_str_mv Perdomo Charry, Oscar Julián
Pérez, Andrés Daniel
De la Pava Rodríguez, Melissa
Ríos Calixto, Hernán Andrés
Arias Vanegas, Víctor Alfonso
Lara Ramírez, Juan Sebastián
Toledo Cortés, Santiago
Camargo Mendoza, Jorge Eliecer
Rodríguez Alvira, Francisco José
González Osorio, Fabio Augusto
dc.contributor.researchgroup.spa.fl_str_mv GiBiome
dc.subject.armarc.none.fl_str_mv Trastornos de la visión
Vision disorders
Óptica fisiológica
Physiological optics
Imágenes ópticas
Optical images
topic Trastornos de la visión
Vision disorders
Óptica fisiológica
Physiological optics
Imágenes ópticas
Optical images
Apoyo a la decisión clínica
Aprendizaje profundo
Análisis inteligente
Enfermedades oculares
Adquisición de imágenes oftálmicas
Telemedicina
Clinical decision support
Deep learning
Intelligent analysis
Ocular diseases
Ophthalmic image acquisition
Telemedicine
dc.subject.proposal.spa.fl_str_mv Apoyo a la decisión clínica
Aprendizaje profundo
Análisis inteligente
Enfermedades oculares
Adquisición de imágenes oftálmicas
Telemedicina
dc.subject.proposal.eng.fl_str_mv Clinical decision support
Deep learning
Intelligent analysis
Ocular diseases
Ophthalmic image acquisition
Telemedicine
description Abstract 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.
publishDate 2020
dc.date.issued.none.fl_str_mv 2020-09
dc.date.accessioned.none.fl_str_mv 2024-10-09T23:42:15Z
dc.date.available.none.fl_str_mv 2024-10-09T23:42:15Z
dc.type.spa.fl_str_mv Artículo de revista
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dc.identifier.instname.spa.fl_str_mv Universidad Escuela Colombiana de Ingeniería Julio Garavito
dc.identifier.reponame.spa.fl_str_mv Repositorio Digital
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Repositorio Digital
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dc.relation.citationedition.spa.fl_str_mv Vol. 29 No. 54 de 2020
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dc.relation.citationstartpage.spa.fl_str_mv 1
dc.relation.citationvolume.spa.fl_str_mv 29
dc.relation.ispartofjournal.spa.fl_str_mv Revista Facultad De Ingeniería
dc.relation.references.spa.fl_str_mv American Diabetes Association, “Classification and diagnosis of diabetes,” Diabetes Care, vol. 39 (1), S13S22, 2016. https://doi.org/10.2337/dc16-S005
M. Abràmoff, M. Garvin, and M. Sonka, "Retinal imaging and image analysis,” IEEE reviews in biomedical engineering, vol. 3, pp. 169-208, 2010. https://doi.org/10.1109/RBME.2010.2084567
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. https://doi.org/10.1136/bmjopen2013-003471
G. Labiris, E. Panagiotopoulou, and V. Kozobolis, “A systematic review of teleophthalmological studies in Europe,” journal of https://doi.org/10.18240/ijo.2018.02.22
R. Gargeya, and T. Leng, “Automated identification of diabetic retinopathy using deep learning,” Ophthalmology, vol. 124 (7), pp. 962-969, 2017. https://doi.org/10.1016/j.ophtha.2017.02.008
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. 146154, 2017. https://doi.org/10.1007/978-3-319-67534-3_16
C. Lam, C. Yu, L. Huang, and D. Rubin, “Retinal lesion detection with deep learning using image patches,” Investigative ophthalmology & https://doi.org/10.1167/iovs.17-22721
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. https://doi.org/10.17077/omia.1057
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. https://doi.org/10.1111/cxo.12535
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. https://doi.org/10.1177/1932296816629158
W. Alyoubi, W. Shalash, and M. Abulkhair, “Diabetic retinopathy detection through deep learning techniques: A review,” Informatics in Medicine Unlocked, vol. 20, e100377, 2016. https://doi.org/10.1016/j.imu.2020.100377
K. Stebbins, “Diabetic Retinal Examinations in Frontline Care Using RetinaVue Care Delivery Model,” Point of Care, vol. 18 (1), pp. 37-39, 2019. https://doi.org/10.1097/POC.0000000000000183
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. https://doi.org/10.1117/12.2256939
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. https://doi.org/10.1007/978-3-030-00949-6_38
B. Graham, “Kaggle diabetic retinopathy detection competition report,” Master Thesis, University of Warwick, United Kingdom, 2015.
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. https://doi.org/10.1007/978-3-030-00949-6_29
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. https://doi.org/10.1007/978-3-030-32239-7_6
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. https://doi.org/10.5566/ias.1155
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. https://doi.org/10.1109/CVPR.2016.308
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. https://doi.org/10.1007/s11263-015-0816-y
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. https://doi.org/10.1007/978-3-030-11021-5_16
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. https://doi.org/10.1145/2647868.2654948
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. https://doi.org/10.4137/CIN.S14053
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spelling Perdomo Charry, Oscar Julián0257d02fec95a5e32cb46abe673774b2Pérez, Andrés Daniel6b85ae26b406d5b6fff27ee61cf9335eDe la Pava Rodríguez, Melissa0719eac3eacaa3055d9fa69f47e2489eRíos Calixto, Hernán Andrés3d4b8c41548a45339910669df636c88aArias Vanegas, Víctor Alfonsoab2097ed5827cf9cdd2608227ed7d9fbLara Ramírez, Juan Sebastiánf99523010bf986c42b2f594e40277403Toledo Cortés, Santiagoaacc1c99e2c7e404d2f99a7a954b57c8Camargo Mendoza, Jorge Eliecer5348a4327d4ddf28ddd4bd4b01fcbff6Rodríguez Alvira, Francisco Joséc698688279105fbb6b4f656eab8ff64bGonzález Osorio, Fabio Augusto35912f60905ba6e179208c70e6024e80GiBiome2024-10-09T23:42:15Z2024-10-09T23:42:15Z2020-090121-1129https://repositorio.escuelaing.edu.co/handle/001/33052357-5328Universidad Escuela Colombiana de Ingeniería Julio GaravitoRepositorio Digitalhttps://repositorio.escuelaing.edu.co/Abstract 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.Resumen Las enfermedades oculares son una de las principales causas de discapacidad irreversible en personas en edad productiva. En 2020, se estimaba que aproximadamente el 18% de la población mundial padecía retinopatía diabética y edema macular diabético, pero, lamentablemente, solo la mitad de estas personas fueron diagnosticadas correctamente. Por otro lado, en Colombia, la población diabética (8% de la población total del país) presenta o ha presentado alguna complicación ocular que ha conllevado otros costos asociados y, en algunos casos, ha provocado limitación de la visión o ceguera. Las imágenes del fondo de ojo son la fuente más rápida y económica de información ocular que puede proporcionar una evaluación clínica completa del estado de la retina de los pacientes. Sin embargo, el número de oftalmólogos es insuficiente y los entornos clínicos, así como la atención de estos expertos, se limitan a las zonas urbanas. Además, el análisis de dichas imágenes por parte de profesionales requiere una amplia formación, e incluso para los experimentados, es un proceso engorroso y propenso a errores. Los métodos de aprendizaje profundo han marcado importantes avances en imágenes médicas debido a su excelente desempeño en tareas de segmentación, detección y clasificación de enfermedades. Este artículo presenta SOPHIA, un sistema basado en aprendizaje profundo para la adquisición, transmisión, análisis inteligente y soporte de decisiones clínicas de imágenes oftálmicas para el diagnóstico de enfermedades oculares. El sistema se encuentra en desarrollo activo en un proyecto que reúne a instituciones proveedoras de atención médica, especialistas en oftalmología e informáticos. Finalmente, se presentan los resultados preliminares en el análisis automático de imágenes oculares mediante aprendizaje profundo, así como los trabajos futuros necesarios para la implementación y validación del sistema en Colombia.15 páginasapplication/pdfengUniversidad Pedagógica y Tecnológica de Colombia (UTPTC)Tunja, Boyacá (Colombia)https://revistas.uptc.edu.co/index.php/ingenieria/article/view/11769SOPHIA: System for Ophthalmic Image Acquisition, Transmission, and Intelligent AnalysisArtículo de revistainfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/coar/version/c_970fb48d4fbd8a85Vol. 29 No. 54 de 20201554129Revista Facultad De IngenieríaAmerican Diabetes Association, “Classification and diagnosis of diabetes,” Diabetes Care, vol. 39 (1), S13S22, 2016. https://doi.org/10.2337/dc16-S005M. Abràmoff, M. Garvin, and M. Sonka, "Retinal imaging and image analysis,” IEEE reviews in biomedical engineering, vol. 3, pp. 169-208, 2010. https://doi.org/10.1109/RBME.2010.2084567J. 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. https://doi.org/10.1136/bmjopen2013-003471G. Labiris, E. Panagiotopoulou, and V. Kozobolis, “A systematic review of teleophthalmological studies in Europe,” journal of https://doi.org/10.18240/ijo.2018.02.22R. Gargeya, and T. Leng, “Automated identification of diabetic retinopathy using deep learning,” Ophthalmology, vol. 124 (7), pp. 962-969, 2017. https://doi.org/10.1016/j.ophtha.2017.02.008S. 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. 146154, 2017. https://doi.org/10.1007/978-3-319-67534-3_16C. Lam, C. Yu, L. Huang, and D. Rubin, “Retinal lesion detection with deep learning using image patches,” Investigative ophthalmology & https://doi.org/10.1167/iovs.17-22721O. 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. https://doi.org/10.17077/omia.1057B. 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. https://doi.org/10.1111/cxo.12535J. 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. https://doi.org/10.1177/1932296816629158W. Alyoubi, W. Shalash, and M. Abulkhair, “Diabetic retinopathy detection through deep learning techniques: A review,” Informatics in Medicine Unlocked, vol. 20, e100377, 2016. https://doi.org/10.1016/j.imu.2020.100377K. Stebbins, “Diabetic Retinal Examinations in Frontline Care Using RetinaVue Care Delivery Model,” Point of Care, vol. 18 (1), pp. 37-39, 2019. https://doi.org/10.1097/POC.0000000000000183O. 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. https://doi.org/10.1117/12.2256939O. 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. https://doi.org/10.1007/978-3-030-00949-6_38B. Graham, “Kaggle diabetic retinopathy detection competition report,” Master Thesis, University of Warwick, United Kingdom, 2015.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. https://doi.org/10.1007/978-3-030-00949-6_29H. 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. https://doi.org/10.1007/978-3-030-32239-7_6E. 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. https://doi.org/10.5566/ias.1155C. 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. https://doi.org/10.1109/CVPR.2016.308O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. Berg and L. 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Müller, “Medical image retrieval: a multimodal approach,” Cancer informatics, vol. 13, e14053, 2014. https://doi.org/10.4137/CIN.S14053info:eu-repo/semantics/closedAccesshttp://purl.org/coar/access_right/c_14cbTrastornos de la visiónVision disordersÓptica fisiológicaPhysiological opticsImágenes ópticasOptical imagesApoyo a la decisión clínicaAprendizaje profundoAnálisis inteligenteEnfermedades ocularesAdquisición de imágenes oftálmicasTelemedicinaClinical decision supportDeep learningIntelligent analysisOcular diseasesOphthalmic image acquisitionTelemedicineTEXTSOPHIA System for Ophthalmic Image Acquisition, Transmission, and Intelligent Analysis.pdf.txtSOPHIA System for Ophthalmic Image Acquisition, Transmission, and Intelligent Analysis.pdf.txtExtracted texttext/plain33279https://repositorio.escuelaing.edu.co/bitstream/001/3305/4/SOPHIA%20System%20for%20Ophthalmic%20Image%20Acquisition%2c%20Transmission%2c%20and%20Intelligent%20Analysis.pdf.txt2220c6dd75da6f5f1da076700bb985b8MD54metadata only accessTHUMBNAILPortada SOPHIA System for Ophthalmic Image Acquisition, Transmission, and Intelligent Analysis.PNGPortada SOPHIA System for Ophthalmic Image Acquisition, Transmission, and Intelligent Analysis.PNGimage/png94494https://repositorio.escuelaing.edu.co/bitstream/001/3305/3/Portada%20SOPHIA%20System%20for%20Ophthalmic%20Image%20Acquisition%2c%20Transmission%2c%20and%20Intelligent%20Analysis.PNG0badcd2137f51797ca67a9aceaf93fa7MD53open accessSOPHIA System for Ophthalmic Image Acquisition, Transmission, and Intelligent Analysis.pdf.jpgSOPHIA System for Ophthalmic Image Acquisition, Transmission, and Intelligent Analysis.pdf.jpgGenerated Thumbnailimage/jpeg13485https://repositorio.escuelaing.edu.co/bitstream/001/3305/5/SOPHIA%20System%20for%20Ophthalmic%20Image%20Acquisition%2c%20Transmission%2c%20and%20Intelligent%20Analysis.pdf.jpg88aa59f6717f9e0e4288ffe2eb31911aMD55metadata only accessLICENSElicense.txtlicense.txttext/plain; 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