Methods: We used 3458 foot radiographs of patients with longitudinal flatfoot and 1726 humans without the foot deformity aged 17-75. Each radiograph used for neural network training was labeled by one radiologist while at testing stage of the study each X-ray image was labeled independently by two radiologists chosen blindly. Diagnostic algorithm was designed on a base of detecting three anatomical points forming the foot arch angle. The artificial intelligence workflow consisted of three-step sequence: a) data preprocessing and preparation for neural network segmentation; b) segments three areas as bounding boxes around required three points; c) location of each of the required points was found inside the relevant area, and appropriate angle measure and flatfoot degree were calculated. The segmentation network was encoder-decoder type convolutional neural network based on U-Net architecture with skip-connections, where ResNet50 is used as encoder, and transposed convolutions were used in decoder for upsampling the result after bottle-neck.
Results: We created effective, robust and fast artificial intelligence-based method, that shows the results in general not worse than radiologists and requires about 6000 times less time.
Conclusions: the artificial intelligence developed is an effective tool for longitudinal flatfoot determination by X-ray image segmentation and the foot arch angle calculation. It may be considered as a rapid assistant as accurate as experienced radiologist.