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

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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
Abstract (Spanish)
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.
Abstract (English)
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.
Extent
15 páginas
URI: https://repositorio.escuelaing.edu.co/handle/001/3305
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