Reliability of ultrasound versus digital vaginal examination in detecting cervical dilatation during labor: a diagnostic test accuracy systematic review

Digital Examination vs. 4D Transperineal Ultrasound-Do They Compare in Labour Management? A Pilot Study

The aim was to compare transperineal ultrasound (TPU) with parameters of the Bishop Score during the first stage of labour and evaluate how TPU can contribute towards improving labour management. Digital examination (DE) and TPU were performed on 42 women presenting at the labour ward with regular contractions. TPU measurements included the head-symphysis distance, angle of progression, diameter of the cervical wall, cervical dilation (CD) and cervical length (CL). To examine if TPU can monitor labour progress, correlations of TPU parameters were calculated. Agreement of DE and TPU was examined for CL and CD measurements and for two groups divided into latent (CD < 5 cm) and active stages of labour (CD ≥ 5 cm). TPU parameters indicated a moderate negative correlation of CD and CL (Pearson: r = -0.667; Spearman = -0.611). The other parameters showed a weak to moderate correlation. DE and TPU measurements for CD showed better agreement during the latent stage than during the active stage. The results of the present study add to the growing evidence that TPU may contribute towards an improved labour management, suggesting a combined approach of TPU and DE to monitor the latent first stage of labour and using only DE during the active stage of labour.

Multitask Deep Neural Network for the Fully Automatic Measurement of the Angle of Progression

The angle of progression (AoP) for assessing fetal head (FH) descent during labor is measured from the standard plane of transperineal ultrasound images as the angle between a line through the long axis of pubic symphysis (PS) and a second line from the right end of PS tangentially to the contour of the FH. This paper presents a multitask network with a shared feature encoder and three task-special decoders for standard plane recognition (Task1), image segmentation (Task2) of PS and FH, and endpoint detection (Task3) of PS. Based on the segmented FH and two endpoints of PS from standard plane images, we determined the right FH tangent point that passes through the right endpoint of PS and then computed the AoP using the above three points. In this paper, the efficient channel attention unit is introduced into the shared feature encoder for improving the robustness of layer region encoding, while an attention fusion module is used to promote cross-branch interaction between the encoder for Task2 and that for Task3, and a shape-constrained loss function is designed for enhancing the robustness to noise based on the convex shape-prior. We use Pearson's correlation coefficient and the Bland–Altman graph to assess the degree of agreement. The dataset includes 1964 images, where 919 images are nonstandard planes, and the other 1045 images are standard planes including PS and FH. We achieve a classification accuracy of 92.26%, and for the AoP calculation, an absolute mean (STD) value of the difference in AoP (∆AoP) is 3.898° (3.192°), the Pearson's correlation coefficient between manual and automated AoP was 0.964 and the Bland-Altman plot demonstrates they were statistically significant (P < 0.05). In conclusion, our approach can achieve a fully automatic measurement of AoP with good efficiency and may help labor progress in the future.