Investigative ophthalmology & visual science · 1995
Neural network classification of corneal topography. Preliminary demonstration.
СтатьяПоддержка службы здравоохранения СШАНазвание и аннотация: Перевод пока не подготовлен
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Maeda N, Klyce S D, Smolek M K
Аннотация
PURPOSE: Videokeratography is a powerful tool for the diagnosis of corneal shape abnormalities. However, interpretation of the topographic map is sometimes difficult, especially when pathologies with similar topographic patterns are suspected. The neural networks model, an artificial intelligence approach, was applied for automated pattern interpretation in corneal topography, and its usefulness was assessed.
METHODS: One hundred eighty-three topographic maps were selected and classified by human experts into seven categories: normal, with-the-rule astigmatism, keratoconus (mild, moderate, advanced), postphotorefractive keratectomy, and postkeratoplasty. The maps were divided into a training set (108 maps) and a test set (75 maps). For each map, 11 topography-characterizing indices calculated from the data provided by the TMS-1 videokeratoscope, plus the corresponding diagnosis category, were used to train a neural network.
RESULTS: The correct classification was achieved by a trained neural network for all 108 maps in the training set. In the test set, the neural network correctly classified 60 of 75 maps (80%). For every category, accuracy and specificity were greater than 90%, whereas sensitivity ranged from 44% to 100%.
CONCLUSIONS: With further testing and refinement, the neural networks paradigm for computer-assisted interpretation or objective classification of videokeratography may become a useful tool to aid the clinician in the diagnosis of corneal topographic abnormalities.
METHODS: One hundred eighty-three topographic maps were selected and classified by human experts into seven categories: normal, with-the-rule astigmatism, keratoconus (mild, moderate, advanced), postphotorefractive keratectomy, and postkeratoplasty. The maps were divided into a training set (108 maps) and a test set (75 maps). For each map, 11 topography-characterizing indices calculated from the data provided by the TMS-1 videokeratoscope, plus the corresponding diagnosis category, were used to train a neural network.
RESULTS: The correct classification was achieved by a trained neural network for all 108 maps in the training set. In the test set, the neural network correctly classified 60 of 75 maps (80%). For every category, accuracy and specificity were greater than 90%, whereas sensitivity ranged from 44% to 100%.
CONCLUSIONS: With further testing and refinement, the neural networks paradigm for computer-assisted interpretation or objective classification of videokeratography may become a useful tool to aid the clinician in the diagnosis of corneal topographic abnormalities.