Automated Approach for Enhancing Fetal Head Station Assessment in Labor with Transperineal Ultrasound.

Rationale And Objectives: Accurate assessment of fetal head station (FHS) is crucial during labor management to reduce the risk of complications and plan the mode of delivery. Although digital vaginal examination (DVE) has been associated with inaccuracies in FHS assessment, ultrasound (US) evaluation remains dependent on sonographer expertise. This study aimed at investigating the reliability and accuracy of an automatic approach to assess the FHS during labor with transperineal US (TPU).

A deep learning approach to identify the fetal head position using transperineal ultrasound during labor.

Objectives: To develop a deep learning (DL)-model using convolutional neural networks (CNN) to automatically identify the fetal head position at transperineal ultrasound in the second stage of labor.

Material And Methods: Prospective, multicenter study including singleton, term, cephalic pregnancies in the second stage of labor. We assessed the fetal head position using transabdominal ultrasound and subsequently, obtained an image of the fetal head on the axial plane using transperineal ultrasound and labeled it according to the transabdominal ultrasound findings.

A new standard scoring for interstitial pneumonia based on quantitative analysis of ultrasonographic data: A study on COVID-19 patients.

Objective: To assess the effectiveness of 3 novel lung ultrasound (LUS)-based parameters: Pneumonia Score and Lung Staging for pneumonia staging and COVID Index, indicating the probability of SARS-CoV-2 infection.

Methods: Adult patients admitted to the emergency department with symptoms potentially related to pneumonia, healthy volunteers and clinical cases from online accessible databases were evaluated. The patients underwent a clinical-epidemiological questionnaire and a LUS acquisition, following a 14-zone protocol.