AIMC Topic: Placenta

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Robot assisted Fetoscopic Laser Coagulation: Improvements in navigation, re-location and coagulation.

Artificial intelligence in medicine
Fetoscopic Laser Coagulation (FLC) for Twin to Twin Transfusion Syndrome is a challenging intervention due to the working conditions: low quality images acquired from a 3 mm fetoscope inside a turbid liquid environment, local view of the placental su...

Associations of maternal stress, gene expression, and newborn birthweight in the Democratic Republic of Congo.

American journal of biological anthropology
OBJECTIVES: Maternal stress has long been associated with lower birthweight, which is associated with adverse health outcomes including many adult diseases. The underlying mechanisms remain elusive although changes in gene expression may play a role....

Deep Learning Model Based on Multisequence MRI Images for Assessing Adverse Pregnancy Outcome in Placenta Accreta.

Journal of magnetic resonance imaging : JMRI
BACKGROUND: Preoperative assessment of adverse outcomes risk in placenta accreta spectrum (PAS) disorders is of high clinical relevance for perioperative management and prognosis.

A multimodal deep learning model for predicting severe hemorrhage in placenta previa.

Scientific reports
Placenta previa causes life-threatening bleeding and accurate prediction of severe hemorrhage leads to risk stratification and optimum allocation of interventions. We aimed to use a multimodal deep learning model to predict severe hemorrhage. Using M...

Automated stereological image analysis approach of the human placenta: Surface areas and vascularization.

Placenta
Detecting and quantifying surface densities of placental villi and their vasculature adds important information on the development of the placenta under different exposures and pathological conditions. Today, a larger number of samples and tissue are...

Perceiving placental ultrasound image texture evolution during pregnancy with normal and adverse outcome through machine learning prism.

Placenta
INTRODUCTION: The objective was to perform placental ultrasound image texture (UPIA) in first (T1), second(T2) and third(T3) trimesters of pregnancy using machine learning( ML).

A Deep Learning Pipeline Using Prior Knowledge for Automatic Evaluation of Placenta Accreta Spectrum Disorders With MRI.

Journal of magnetic resonance imaging : JMRI
BACKGROUND: The diagnosis of prenatal placenta accreta spectrum (PAS) with magnetic resonance imaging (MRI) is highly dependent on radiologists' experience. A deep learning (DL) method using the prior knowledge that PAS-related signs are generally fo...

Automatic placental and fetal volume estimation by a convolutional neural network.

Placenta
INTRODUCTION: We aimed to develop an artificial intelligence (AI) deep learning algorithm to efficiently estimate placental and fetal volumes from magnetic resonance (MR) scans.