Use of genetic algorithms and neural networks in the analysis of foot deformities

The work is devoted to the current problem of diagnosing foot deformities, which are characterized by a high incidence among all age groups. Among the objective quantitative methods for diagnosing flatfoot, plantography, based on the assessment of prints of the plantar surface of the foot, has become widespread in clinical practice. The purpose of the study was to evaluate and analyze the effectiveness of methods for automatic assessment of footprints using “computer vision”. The study examines methods for automatic recognition and marking of photoplantograms of the foot using genetic algorithms and neural networks to construct control points of the foot using the example of calculating the indices of the longitudinal and transverse arches of the foot. A comparison was made of the results of calculating flatfoot indices and photoplantograms using manual and automatic markings. It was found that the accuracy of automatic methods for analyzing photoplantograms using genetic algorithms and neural networks is 92–97% in relation to manual marking. At the same time, the time spent on manual marking exceeded the duration of automatic image analysis by 2 - 2.5 times. The results obtained confirmed the possibility of optimizing the diagnostic process when conducting mass (screening) examinations of the condition of the arches of the feet.