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...
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| Format: | Online |
| Language: | eng spa |
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Universidad Pedagógica y Tecnológica de Colombia
2020
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| Online Access: | https://revistas.uptc.edu.co/index.php/ingenieria/article/view/11769 |
| _version_ | 1801706090375675904 |
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| author | Perdomo-Charry, Oscar Julián Pérez-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 |
| author_facet | Perdomo-Charry, Oscar Julián Pérez-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 |
| author_sort | Perdomo-Charry, Oscar Julián |
| collection | OJS |
| description | 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. |
| format | Online |
| id | oai:oai.revistas.uptc.edu.co:article-11769 |
| institution | Revista Facultad de Ingeniería |
| language | eng spa |
| publishDate | 2020 |
| publisher | Universidad Pedagógica y Tecnológica de Colombia |
| record_format | ojs |
| spelling | oai:oai.revistas.uptc.edu.co:article-117692021-07-13T02:22:51Z SOPHIA: System for Ophthalmic Image Acquisition, Transmission, and Intelligent Analysis SOPHIA: Sistema para adquisición, transmisión, y análisis inteligente de imágenes oftálmicas Perdomo-Charry, Oscar Julián Pérez-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 clinical decision support deep learning intelligent analysis ocular diseases ophthalmic image acquisition telemedicine adquisición de imágenes oftálmicas análisis inteligente apoyo a la decisión clínica aprendizaje profundo enfermedades oculares telemedicina 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. Las enfermedades oculares son una de las principales causas de incapacidad irreversible en personas en edad productiva. En 2020, la población mundial con retinopatía diabética y edema macular diabético está estimada como el 18% de la población mundial, aproximadamente, desafortunadamente, 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 llevado a otros costos asociados y, en algunos casos, ha provocado limitación de la visión o ceguera. Las imágenes de fondo de ojo son la fuente de información ocular más rápida y económica que puede proveer una valoración clínica del estado de la retina de los pacientes. Sin embargo, el número de oftalmólogos es insuficiente, la atención de estos expertos está limitada a zonas urbanas, y el análisis de dichas imágenes por parte de profesionales requiere una amplia formación; incluso para los más experimentados, es un proceso engorroso y propenso a errores. Los métodos de aprendizaje profundo han marcado avances importantes en imágenes médicas debido al desempeño sobresaliente en tareas de segmentación, detección y clasificación de enfermedades. Este artículo presenta SOPHIA, un sistema basado en el aprendizaje profundo para la adquisición, transmisión, análisis inteligente y soporte de decisiones clínicas para el diagnóstico de enfermedades oculares. El sistema se encuentra en desarrollo activo en un proyecto que reúne a instituciones proveedoras de salud, especialistas en oftalmología e informáticos. Finalmente, los resultados preliminares en el análisis automático de imágenes oculares utilizando el aprendizaje profundo son presentados, y se discute el trabajo futuro necesario para la implementación y validación del sistema en Colombia. Universidad Pedagógica y Tecnológica de Colombia 2020-09-29 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion application/pdf application/pdf application/xml https://revistas.uptc.edu.co/index.php/ingenieria/article/view/11769 10.19053/01211129.v29.n54.2020.11769 Revista Facultad de Ingeniería; Vol. 29 No. 54 (2020): Continuos Publication; e11769 Revista Facultad de Ingeniería; Vol. 29 Núm. 54 (2020): Publicación Continua; e11769 2357-5328 0121-1129 eng spa https://revistas.uptc.edu.co/index.php/ingenieria/article/view/11769/9638 https://revistas.uptc.edu.co/index.php/ingenieria/article/view/11769/9679 https://revistas.uptc.edu.co/index.php/ingenieria/article/view/11769/10018 Copyright (c) 2020 Oscar Julián Perdomo-Charry, Ph. D., Andrés Daniel Pérez-Pérez, Melissa de-la-Pava-Rodríguez, Hernán Andrés Ríos-Calixto, Víctor Alfonso Arias-Vanegas, Juan Sebastián Lara-Ramírez, Santiago Toledo-Cortés, Ph. D. (c), Jorge Eliecer Camargo-Mendoza, Ph. D., Francisco José Rodríguez-Alvira, Fabio Augusto González-Osorio, Ph. D. |
| spellingShingle | clinical decision support deep learning intelligent analysis ocular diseases ophthalmic image acquisition telemedicine adquisición de imágenes oftálmicas análisis inteligente apoyo a la decisión clínica aprendizaje profundo enfermedades oculares telemedicina Perdomo-Charry, Oscar Julián Pérez-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 SOPHIA: System for Ophthalmic Image Acquisition, Transmission, and Intelligent Analysis |
| title | SOPHIA: System for Ophthalmic Image Acquisition, Transmission, and Intelligent Analysis |
| title_alt | SOPHIA: Sistema para adquisición, transmisión, y análisis inteligente de imágenes oftálmicas |
| 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_short | SOPHIA: System for Ophthalmic Image Acquisition, Transmission, and Intelligent Analysis |
| title_sort | sophia system for ophthalmic image acquisition transmission and intelligent analysis |
| topic | clinical decision support deep learning intelligent analysis ocular diseases ophthalmic image acquisition telemedicine adquisición de imágenes oftálmicas análisis inteligente apoyo a la decisión clínica aprendizaje profundo enfermedades oculares telemedicina |
| topic_facet | clinical decision support deep learning intelligent analysis ocular diseases ophthalmic image acquisition telemedicine adquisición de imágenes oftálmicas análisis inteligente apoyo a la decisión clínica aprendizaje profundo enfermedades oculares telemedicina |
| url | https://revistas.uptc.edu.co/index.php/ingenieria/article/view/11769 |
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