Automated assessment of endometrial receptivity for screening recurrent pregnancy loss risk using deep learning-enhanced ultrasound and clinical data.

Background: Recurrent pregnancy loss (RPL) poses significant challenges in clinical management due to an unclear etiology in over half the cases. Traditional screening methods, including ultrasonographic evaluation of endometrial receptivity (ER), have been debated for their efficacy in identifying high-risk individuals. Despite the potential of artificial intelligence, notably deep learning (DL), to enhance medical imaging analysis, its application in ER assessment for RPL risk stratification remains underexplored.

Optimizing evaluation of endometrial receptivity in recurrent pregnancy loss: a preliminary investigation integrating radiomics from multimodal ultrasound via machine learning.

Background: Recurrent pregnancy loss (RPL) frequently links to a prolonged endometrial receptivity (ER) window, leading to the implantation of non-viable embryos. Existing ER assessment methods face challenges in reliability and invasiveness. Radiomics in medical imaging offers a non-invasive solution for ER analysis, but complex, non-linear radiomic-ER relationships in RPL require advanced analysis.

Automatic motion analysis system for pyloric flow in ultrasonic videos.

Ultrasonography has been widely used to evaluate duodenogastric reflux (DGR). But to the best of our knowledge, no automatic analysis system was developed to realize the quantitative computer-aided analysis. In this paper, we propose a system to perform the automatic detection of DGR in the ultrasonic image sequences by applying the automatic motion analysis.

Weighted cross-correlation based variational optical flow for gastric flow analysis in ultrasonic videos.

Purpose: Estimating the fluid motion in ultrasonic videos is a crucial step in the analysis of duodenogastric reflux. Severe image noise and illumination changes in the pyloric region (the region of interest) challenge the accurate estimation of gastric flow. In this paper, the authors propose an illumination-robust optical flow method based on the weighted cross-correlation.

Tracking pylorus in ultrasonic image sequences with edge-based optical flow.

Tracking pylorus in ultrasonic image sequences is an important step in the analysis of duodenogastric reflux (DGR). We propose a joint prediction and segmentation method (JPS) which combines optical flow with active contour to track pylorus. The goal of the proposed method is to improve the pyloric tracking accuracy by taking account of not only the connection information among edge points but also the spatio-temporal information among consecutive frames.