Related Experiment Video
Updated: Jul 7, 2026
Electromyometrial Imaging of Uterine Contractions in Pregnant Women
Published on: May 26, 2023
Identification of term and preterm labor in rats using artificial neural networks on uterine electromyography signals
Shao-Qing Shi1, William L Maner, Lynette B Mackay
1Division of Reproductive Sciences, Department of Obstetrics and Gynecology, University of Texas Medical Branch, Galveston, TX 77555, USA.
Objective:
This study was undertaken to use artificial neural networks on uterine electromyography data to identify term and preterm labor in rats.
Study Design:
Controls (group 1: n = 4) and preterm labor models (group 2: n = 4, treated with onapristone) were used. Uterine electromyography and intrauterine pressure (IUP) variables were measured by implanted telemetric devices. For each timepoint assessed, either a "labor event" or "nonlabor event" was first assigned by using visual and other means. 112 total labor and nonlabor events were observed. Artificial neural networks were then used with electromyography and intrauterine pressure parameters to attempt algorithmic, objective identification for time of labor in each group.
Results:
For group 1, all 8 (100%) labor events and all 44 (100%) nonlabor events were correctly identified by the artificial neural networks. For group 2, 22 of 24 (92%) labor events and 31 of 36 (86%) nonlabor events were correctly determined by the artificial neural networks.
Conclusion:
Artificial neural networks can effectively predict term and preterm labor during pregnancy with the use of uterine electromyography and intrauterine pressure variables.
