Automated measurement of foot deformities : flatfoot, high arch, calcaneal fracture

Radiographic measurements of foot deformities are used to determine, among other things, such conditions as flatfoot, high arch, or calcaneal fracture. Those measurements are achieved by estimating four angles. Manual assessment of those angles is time-consuming not to mention inevitable errors of such approximation. To the best of the authors knowledge, currently there is no research focusing on finding those four angles. In this paper an algorithm for automatic assessment of those angles, based on extremely randomized trees, is being proposed. Moreover this diagnostic assisting system was intended to be as generic as possible and could be applied, to some degree, to other similar problems. To demonstrate usefulness of this method, correlations of automated measurements with manual ones against correlations of manual measurements with manual ones are being compared. The significance level for manual-manual measurements comparison is less than 0.001 in case of all four angles. The significance level for automated-manual measurements comparison is also less than 0.001 in all cases. The results show that the search for the aforementioned angles can be automated. Even with the use of a generic algorithm a high degree of precision can be achieved, allowing for a more efficient diagnosis

Harnessing convolutional neural networks to find regions of interest on feet X-rays images

X-rays images of the feet can vary in a number of ways, e.g. resolution of the image or spatial position of the foot on the image. This can be an obstacle when trying to build a software that, after a supervised learning, should be able to automatically search for and find elements of the X-rays images of feet allowing for diagnosis while also making it more efficient. With a small number of described examples, data coherence correlates strongly with the quality of the outcome. In this paper we are proposing a convolutional neural networks based solution, which automatically cuts and resizes feet images, so that not only the data is more consistent, but also the area, on which the artificial intelligence algorithm will work, is reduced

Deep Learning Algorithm for Differentiating Patients with a Healthy Liver from Patients with Liver Lesions Based on MR Images

The problems in diagnosing the state of a vital organ such as the liver are complex and remain unresolved. These problems are underscored by frequently published studies on this issue. At the same time, demand for imaging diagnostics, preferably using a method that can detect the disease at the earliest possible stage, is constantly increasing. In this paper, we present liver diseases in the context of diagnosis, diagnostic problems, and possible elimination. We discuss the dataset and methods and present the stages of the pipeline we developed, leading to multiclass segmentation of the liver in multiparametric MR image into lesions and normal tissue. Finally, based on the processing results, each case is classified as either a healthy liver or a liver with lesions. For the training set, the AUC ROC is 0.925 (standard error 0.013 and a p-value less than 0.001), and for the test set, the AUC ROC is 0.852 (standard error 0.039 and a p-value less than 0.001). Further refinements to the proposed pipeline are also discussed. The proposed approach could be used in the detection of focal lesions in the liver and the description of liver tumors. Practical application of the developed multi-class segmentation method represents a key step toward standardizing the medical evaluation of focal lesions in the liver