Panoramic Nailfold Flow Velocity Measurement Method Based on Enhanced Plasma Gap Information

Nailfold microcirculation examination is crucial for the early differential diagnosis of diseases and indicating their severity. In particular, panoramic nailfold flow velocity measurements can provide direct quantitative indicators for the study of vascular diseases and technical support to assess vascular health. Previously, nailfold imaging equipment was limited by a small field of view. Therefore, research on nailfold flow velocity measurement primarily focused on improving the accuracy of single-vessel flow velocity results, while there were few studies on nailfold panoramic flow velocity. Furthermore, with improvements in the imaging field of view and the increasing clinical demand for speed in obtaining nailfold parameter results, doctors do not have time to crop videos to obtain flow velocity results. Therefore, research on nailfold panoramic flow velocity measurement is crucial. This study presents a panoramic nailfold flow velocity measurement method based on enhanced plasma gap information. In contrast to previous methods, the use of a deep learning model to decompose the panoramic flow velocity measurement task into several vessel flow velocity measurement tasks is proposed herein. For improved accuracy, a plasma gap information enhancement method is proposed, using the frame difference to enhance the position movement information of plasma gaps in videos. The t-test results show that the Pearson correlation coefficient between the results of the proposed method and those manually calculated by experts is 0.992 (t = − 0.0889, p = 0.929; > 0.05), with an average error of 2.137%. Therefore, there is no significant difference between the results obtained by the proposed method proposed and the manually calculated results. The feasibility experiment demonstrates that the proposed method can concurrently obtain the flow rate results of 13 nailfold blood vessels. Finally, the proposed method provides an efficient solution for panoramic flow velocity measurement of large-field nailfold multi-vessel videos.