Desarrollo de un sistema automático de discriminación del campo visual glaucomatoso basado en un clasificador Neuro-Fuzzy

• Autores: Julián García Feijoo , Enrique J. Carmona Suárez , L. M. Gallardo, Marta González Hernández, A.M. Fernández-Vidal, Manuel Antonio González de la Rosa, José Mira Mira , Julián García Sánchez
• Localización: Archivos de la Sociedad Española de Oftalmologia, ISSN 0365-6691, Vol. 77, Nº. 12, 2002, págs. 669-676
• Idioma: español
• DOI: 10.4321/s0365-66912002001200006
• Títulos paralelos:
  • Development of an automatic discrimination system for glaucomatous visual fields based on Neuro-Fuzzy nets
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• Resumen
  • español

    Objetivo: Construir un sistema automático de clasificación del campo visual (CV), basado en reglas fuzzy, como herramienta de ayuda al diagnóstico de glaucoma. Método: Se analizaron 212 perimetrías blanco-blanco (OCTUPUS 123 programa G1X) correspondientes a 61 controles y 151 glaucomatosos (incipientes 49,0%, moderados 29,1% y avanzados 21,9%) de 198 pacientes. Criterios de inclusión (glaucomatosos): Agudeza visual >0,5, PIO <20 mm Hg (con tratamiento), refracción (<5D y experiencia perimétrica previa. Criterios de exclusión: mióticos, otra patología ocular que pudiera afectar la realización de la perimetría; Controles: Agudeza visual >0,5 sin patología ocular salvo defectos de refracción (refracción (5D). Un clasificador neuro-fuzzy (NEFCLASS) es un sistema formado por un conjunto de reglas fuzzy, obtenidas a partir de un proceso de aprendizaje, que pretende asignar a una entrada dada la clase de salida correspondiente. Inicialmente, se seleccionan las características de entrada, seguidamente, se construye una base de reglas a partir del conocimiento a priori existente y, finalmente, la etapa de aprendizaje, permite optimizar los parámetros del clasificador. Resultados: Utilizando como características de entrada los defectos medios, calculados en áreas específicas del CV, se obtuvieron cinco reglas que arrojaron unos valores de sensibilidad y especificidad del 96,0% y 93,4%, respectivamente. Conclusiones: La aplicación de técnicas neuro-fuzzy en la discriminación de campos visuales normales y glaucomatosos ofrece resultados muy competitivos frente a técnicas ya utilizadas en esta tarea (análisis discriminante y redes neuronales).

  • English

    Purpose: To provide a useful tool in the diagnosis of glaucoma by developing an automatic system for visual field classification based on neuro-fuzzy rules. Method: A total of 212 visual fields (OCTOPUS 123 program G1X), from 198 patients, were analysed: 61 normal (controls) and 151 with glaucomatous damage (49% with incipient damage, 29.1% with moderate damage, and 21.9% advanced). Inclusion criteria for glaucomatous patients were: Visual acuity >0.5, IOP < 20 mm Hg (with treatment), refraction <5 Dp and previous perimetric experience. Exclusion criteria: miotics, other ocular pathologies which could interfere with visual field examination, and for control subjects: visual acuity >0.5, no ocular pathologies and refraction < 5 Dp. A neuro-fuzzy classifier (NEFCLASS) is a system consisting in a series of fuzzy rules, obtained after a learning process, which attempts to assign to each piece of data input its corresponding output. Initially, the characteristics of each data input are established (input units). Then, based on previous knowledge, a set of rules are defined, and finally, the learning process allows the optimisation of the classifier parameters to generate an output. Results: Input units were defined by using the mean defects calculated at specific areas of the visual field; five rules were then created which generated sensitivity and specificity values of 96.0% and 93.4% respectively. Conclusions: The use of neuro-fuzzy rules for visual field classification in normal vs glaucomatous can provide results which can match the quality of those obtained with other techniques such as discriminatory analysis or neural networks.

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