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...
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Universidad Pedagógica y Tecnológica de Colombia
2020-09-01
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| Series: | Revista Facultad de Ingeniería |
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| Online Access: | https://revistas.uptc.edu.co/index.php/ingenieria/article/view/11769 |
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| author | 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. |
| author_facet | 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. |
| author_sort | Oscar Julián Perdomo-Charry, Ph. D. |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-12-20T07:54:59Z |
| format | Article |
| id | doaj.art-cc49148230694adbb4fda69cb06631fa |
| institution | Directory Open Access Journal |
| issn | 0121-1129 2357-5328 |
| language | English |
| last_indexed | 2024-12-20T07:54:59Z |
| publishDate | 2020-09-01 |
| publisher | Universidad Pedagógica y Tecnológica de Colombia |
| record_format | Article |
| series | Revista Facultad de Ingeniería |
| spelling | doaj.art-cc49148230694adbb4fda69cb06631fa2022-12-21T19:47:43ZengUniversidad Pedagógica y Tecnológica de ColombiaRevista Facultad de Ingeniería0121-11292357-53282020-09-012954e11769e1176910.19053/01211129.v29.n54.2020.1176911769SOPHIA: System for Ophthalmic Image Acquisition, Transmission, and Intelligent AnalysisOscar Julián Perdomo-Charry, Ph. D.0Andrés Daniel Pérez-Pérez1Melissa de-la-Pava-Rodríguez2Hernán Andrés Ríos-Calixto3Víctor Alfonso Arias-Vanegas4Juan Sebastián Lara-Ramírez5Santiago Toledo-Cortés, Ph. D. (c)6Jorge Eliecer Camargo-Mendoza, Ph. D.7Francisco José Rodríguez-Alvira8Fabio Augusto González-Osorio, Ph. D.9Universidad del RosarioUniversidad Nacional de ColombiaUniversidad Nacional de ColombiaFundación Oftalmológica NacionalUniversidad Nacional de ColombiaUniversidad Nacional de ColombiaUniversidad Nacional de ColombiaUniversidad Nacional de ColombiaFundación Oftalmológica NacionalUniversidad Nacional de ColombiaOcular 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.https://revistas.uptc.edu.co/index.php/ingenieria/article/view/11769clinical decision supportdeep learningintelligent analysisocular diseasesophthalmic image acquisitiontelemedicine |
| spellingShingle | 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. SOPHIA: System for Ophthalmic Image Acquisition, Transmission, and Intelligent Analysis Revista Facultad de Ingeniería clinical decision support deep learning intelligent analysis ocular diseases ophthalmic image acquisition telemedicine |
| title | 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_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 |
| url | https://revistas.uptc.edu.co/index.php/ingenieria/article/view/11769 |
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