Evaluation of the accuracy (trueness, precision) and processing time of different 3-dimensional CAD software programs and algorithms for virtual cast alignment.

Objectives: This in vitro study aimed to evaluate the impact of different alignment algorithms and CAD software programs on alignment accuracy (trueness and precision) and processing time.

Methods: A mandibular typodont was digitized using a laboratory scanner (L2i) to obtain a reference standard tesselletion language (STLr) file. It was then scanned with an intraoral scanner (Primescan) and digitally duplicated ten times (n = 10).

External validation of a non-invasive vaginal tool to assess the risk of intra-amniotic inflammation in pregnant women with preterm labor and intact membranes.

Objectives: To prospectively validate the diagnostic performance of a non-invasive point-of-care tool (Rapid IAI System), including vaginal alpha-fetoprotein and interleukin-6, to predict the occurrence of intra-amniotic inflammation in a Spanish cohort of patients admitted with a diagnosis of preterm labor and intact membranes.

Methods: From 2017 to 2022, we prospectively evaluated a cohort of pregnant women diagnosed with preterm labor and intact membranes admitted below 34+0 weeks who underwent amniocentesis to rule-in/out intra-amniotic infection and/or inflammation. Vaginal sampling was performed at the time of amniocentesis or within 24-48 h.

METAPHOR: Metabolic evaluation through phasor-based hyperspectral imaging and organelle recognition for mouse blastocysts and oocytes.

Only 30% of embryos from in vitro fertilized oocytes successfully implant and develop to term, leading to repeated transfer cycles. To reduce time-to-pregnancy and stress for patients, there is a need for a diagnostic tool to better select embryos and oocytes based on their physiology. The current standard employs brightfield imaging, which provides limited physiological information.

Automatic Deep Learning-Based Pipeline for Automatic Delineation and Measurement of Fetal Brain Structures in Routine Mid-Trimester Ultrasound Images.

Introduction: The aim of this study was to develop a pipeline using state-of-the-art deep learning methods to automatically delineate and measure several of the most important brain structures in fetal brain ultrasound (US) images.

Methods: The dataset was composed of 5,331 images of the fetal brain acquired during the routine mid-trimester US scan. Our proposed pipeline automatically performs the following three steps: brain plane classification (transventricular, transthalamic, or transcerebellar plane); brain structures delineation (9 different structures); and automatic measurement (from the structure delineations).

Noninvasive prediction models of intra-amniotic infection in women with preterm labor.

Background: Among women with preterm labor, those with intra-amniotic infection present the highest risk of early delivery and the most adverse outcomes. The identification of intra-amniotic infection requires amniocentesis, perceived as too invasive by women and physicians. Noninvasive methods for identifying intra-amniotic infection and/or early delivery are crucial to focus early efforts on high-risk preterm labor women while avoiding unnecessary interventions in low-risk preterm labor women.

Quantitative ultrasound image analysis of axillary lymph nodes to differentiate malignancy from reactive benign changes due to COVID-19 vaccination.

Purpose: The aim of this study is to assess the potential of quantitative image analysis and machine learning techniques to differentiate between malignant lymph nodes and benign lymph nodes affected by reactive changes due to COVID-19 vaccination.

Method: In this institutional review board-approved retrospective study, we improved our previously published artificial intelligence model, by retraining it with newly collected images and testing its performance on images containing benign lymph nodes affected by COVID-19 vaccination. All the images were acquired and selected by specialized breast-imaging radiologists and the nature of each node (benign or malignant) was assessed through a strict clinical protocol using ultrasound-guided biopsies.

Concordance of the risk of neonatal respiratory morbidity assessed by quantitative ultrasound lung texture analysis in fetuses of twin pregnancies.

To evaluate the concordance of the risk of neonatal respiratory morbidity (NRM) assessed by quantitative ultrasound lung texture analysis (QuantusFLM) between twin fetuses of the same pregnancy. Prospective study conducted in twin pregnancies. Fetal ultrasound lung images were obtained at 26.

Low levels of and high levels of predict COVID-19 disease severity in children and adults.

It is unclear why COVID-19 ranges from asymptomatic to severe. When SARS-CoV-2 is detected, interferon (IFN) response is activated. When it is insufficient or delayed, it might lead to overproduction of cytokines and severe COVID-19.

Generative Adversarial Networks to Improve Fetal Brain Fine-Grained Plane Classification.

Generative adversarial networks (GANs) have been recently applied to medical imaging on different modalities (MRI, CT, X-ray, etc). However there are not many applications on ultrasound modality as a data augmentation technique applied to downstream classification tasks. This study aims to explore and evaluate the generation of synthetic ultrasound fetal brain images via GANs and apply them to improve fetal brain ultrasound plane classification.

Analysis of maturation features in fetal brain ultrasound via artificial intelligence for the estimation of gestational age.

Background: Optimal prenatal care relies on accurate gestational age dating. After the first trimester, the accuracy of current gestational age estimation methods diminishes with increasing gestational age. Considering that, in many countries, access to first trimester crown rump length is still difficult owing to late booking, infrequent access to prenatal care, and unavailability of early ultrasound examination, the development of accurate methods for gestational age estimation in the second and third trimester of pregnancy remains an unsolved challenge in fetal medicine.

Mid-trimester prediction of spontaneous preterm birth with automated cervical quantitative ultrasound texture analysis and cervical length: a prospective study.

The objective of this study was to evaluate a novel automated test based on ultrasound cervical texture analysis to predict spontaneous Preterm Birth (sPTB) alone and in combination with Cervical Length (CL). General population singleton pregnancies between 18 + 0 and 24 + 6 weeks' gestation were assessed prospectively at two centers. Cervical ultrasound images were evaluated and the occurrence of sPTB before weeks 37 + 0 and 34 + 0 were recorded.

Evaluation of deep convolutional neural networks for automatic classification of common maternal fetal ultrasound planes.

The goal of this study was to evaluate the maturity of current Deep Learning classification techniques for their application in a real maternal-fetal clinical environment. A large dataset of routinely acquired maternal-fetal screening ultrasound images (which will be made publicly available) was collected from two different hospitals by several operators and ultrasound machines. All images were manually labeled by an expert maternal fetal clinician.

The Predictive Value of the Cervical Consistency Index to Predict Spontaneous Preterm Birth in Asymptomatic Twin Pregnancies at the Second-Trimester Ultrasound Scan: A Prospective Cohort Study.

Novel transvaginal ultrasound (TVU) markers have been proposed to improve spontaneous preterm birth (sPTB) prediction. Preliminary results of the cervical consistency index (CCI), uterocervical angle (UCA), and cervical texture (CTx) have been promising in singletons. However, in twin pregnancies, the results have been inconsistent.

Intra- and interobserver reproducibility of second trimester ultrasound cervical length measurement in a general population.

Objectives: To evaluate the reproducibility of ultrasound cervical length (CL) measurement at the second trimester.

Methods: A set of 565 cervical ultrasound images were collected at 19 + 0-24 + 6 weeks' gestation. Two senior maternal-fetal specialists measured CL in each image on three occasions 2 weeks apart.

Quantitative Ultrasound Image Analysis of Axillary Lymph Nodes to Diagnose Metastatic Involvement in Breast Cancer.

This study aimed to assess the potential of state-of-the-art ultrasound analysis techniques to non-invasively diagnose axillary lymph nodes involvement in breast cancer. After exclusion criteria, 105 patients were selected from two different hospitals. The 118 lymph node ultrasound images taken from these patients were divided into 53 cases and 65 controls, which made up the study series.

Evaluation of an improved tool for non-invasive prediction of neonatal respiratory morbidity based on fully automated fetal lung ultrasound analysis.

The objective of this study was to evaluate the performance of a new version of quantusFLM®, a software tool for prediction of neonatal respiratory morbidity (NRM) by ultrasound, which incorporates a fully automated fetal lung delineation based on Deep Learning techniques. A set of 790 fetal lung ultrasound images obtained at 24 + 0-38 + 6 weeks' gestation was evaluated. Perinatal outcomes and the occurrence of NRM were recorded.

Automated measurement of mouse social behaviors using depth sensing, video tracking, and machine learning.

A lack of automated, quantitative, and accurate assessment of social behaviors in mammalian animal models has limited progress toward understanding mechanisms underlying social interactions and their disorders such as autism. Here we present a new integrated hardware and software system that combines video tracking, depth sensing, and machine learning for automatic detection and quantification of social behaviors involving close and dynamic interactions between two mice of different coat colors in their home cage. We designed a hardware setup that integrates traditional video cameras with a depth camera, developed computer vision tools to extract the body "pose" of individual animals in a social context, and used a supervised learning algorithm to classify several well-described social behaviors.

Mapping wide row crops with video sequences acquired from a tractor moving at treatment speed.

This paper presents a mapping method for wide row crop fields. The resulting map shows the crop rows and weeds present in the inter-row spacing. Because field videos are acquired with a camera mounted on top of an agricultural vehicle, a method for image sequence stabilization was needed and consequently designed and developed.

An image segmentation based on a genetic algorithm for determining soil coverage by crop residues.

Determination of the soil coverage by crop residues after ploughing is a fundamental element of Conservation Agriculture. This paper presents the application of genetic algorithms employed during the fine tuning of the segmentation process of a digital image with the aim of automatically quantifying the residue coverage. In other words, the objective is to achieve a segmentation that would permit the discrimination of the texture of the residue so that the output of the segmentation process is a binary image in which residue zones are isolated from the rest.