Objective Dyschromatopsia is a pathology that afflicts many people even if, in most cases, they are not aware of it. The pathology, in fact, is not disabling in everyday life even if it is limiting from some points of view. Once diagnosed, dyschromatopsia is generally not investigated further: it is not known exactly how it manifests itself and with what extent. Furthermore, since it is a genetic pathology, it is “condemned” not to be resolvable. Biological neural networks have shown the capability to readapt their structure in order to overcome sensory malfunctions or neuronal damage. We propose a diagnostic algorithm capable of qualitatively and quantitatively assessing the degree of visual impairment due to the presence of congenital or acquired dyschromatopsia. The algorithm can also be easily integrated for its possible therapeutic use. Methods The application of a novel approach based on an innovative algorithm for the diagnosis of dyschromatopsia and plastic reeducation training of the eye is proposed. Results Our algorithm provides an accurate measure of the degree of dyschromatopsia severity in patients quickly and noninvasively. In addition, it can be used for a reeducational training process. Conclusions Dyschromatopsia is an increasingly common disease in the world. The method we developed can diagnose dyschromatopsia. The algorithm also develops a metric scale for recognizing the degree of severity. The algorithm can be used independently by specilized and non-specilized people. In addition, the algorithm can be integrated with Machine Learning techniques to create a customized eye retrainer based on the plasticity and adaptability of neural tissue.