Aims: The purpose of this study is to develop a quantitative and objective method of diagnosing uterine peristalsis by using AI technology, which is currently evaluated visually by experienced physicians.
Methods: The coordinates at which uterine peristalsis occurs were estimated using DeepLabCut, a marker-less coordinate estimation library, on Cine MRI images of the uterus of female infertility patients. Local maximum and local minimum values were calculated from the obtained coordinate changes to detect the occurrence of uterine peristalsis.
Results: We were able to track uterine peristalsis and mechanically measure the timing and frequency of its occurrence by manually labeling 19 frames out of 90 frames of the target video image.
Conclusion: The ability to quantitatively and objectively diagnose uterine peristalsis, which was previously subjective, may lead to the development of new diagnostic methods and a reduction in the burden on physicians.
