AIMC Topic: Prenatal Diagnosis

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Expert system supporting an early prediction of the bronchopulmonary dysplasia.

Computers in biology and medicine
This work presents a decision support system which uses machine learning to support early prediction of bronchopulmonary dysplasia (BPD) for extremely premature infants after their first week of life. For that purpose a knowledge database was created...

First Trimester Noninvasive Prenatal Diagnosis: A Computational Intelligence Approach.

IEEE journal of biomedical and health informatics
The objective of this study is to examine the potential value of using machine learning techniques such as artificial neural network (ANN) schemes for the noninvasive estimation, at 11-13 weeks of gestation, the risk for euploidy, trisomy 21 (T21), a...

SegQC: a segmentation network-based framework for multi-metric segmentation quality control and segmentation error detection in volumetric medical images.

Medical image analysis
Quality control (QC) of structures segmentation in volumetric medical images is important for identifying segmentation errors in clinical practice and for facilitating model development by enhancing network performance in semi-supervised and active l...

Deep Learning-based Brain Age Prediction Using MRI to Identify Fetuses with Cerebral Ventriculomegaly.

Radiology. Artificial intelligence
Fetal ventriculomegaly (VM) and its severity and associated central nervous system (CNS) abnormalities are important indicators of high risk for impaired neurodevelopmental outcomes. Recently, a novel fetal brain age prediction method using a two-dim...

Noninvasive fetal genotyping using deep neural networks.

Briefings in bioinformatics
Circulating cell-free DNA (cfDNA) is a powerful diagnostics tool that is widely studied in the context of liquid biopsy in oncology and other fields. In obstetrics, maternal plasma cfDNA have already proven its utility, enabling noninvasive prenatal ...

Data for AI in Congenital Heart Defects: Systematic Review.

Studies in health technology and informatics
Congenital heart disease (CHD) represents a significant challenge in prenatal care due to low prenatal detection rates. Artificial Intelligence (AI) offers promising avenues for precise CHD prediction. In this study we conducted a systematic review a...

Deep-learning model for prenatal congenital heart disease screening generalizes to community setting and outperforms clinical detection.

Ultrasound in obstetrics & gynecology : the official journal of the International Society of Ultrasound in Obstetrics and Gynecology
OBJECTIVES: Despite nearly universal prenatal ultrasound screening programs, congenital heart defects (CHD) are still missed, which may result in severe morbidity or even death. Deep machine learning (DL) can automate image recognition from ultrasoun...

Development and clinical validation of real-time artificial intelligence diagnostic companion for fetal ultrasound examination.

Ultrasound in obstetrics & gynecology : the official journal of the International Society of Ultrasound in Obstetrics and Gynecology
OBJECTIVE: Prenatal diagnosis of a rare disease on ultrasound relies on a physician's ability to remember an intractable amount of knowledge. We developed a real-time decision support system (DSS) that suggests, at each step of the examination, the n...

A machine learning technology to improve the risk of non-invasive prenatal tests.

Technology and health care : official journal of the European Society for Engineering and Medicine
BACKGROUND: Timely and accurate diagnosis of genetic diseases can lead to proper action and prevention of irreparable events.

Interactive Medical Image Segmentation Using Deep Learning With Image-Specific Fine Tuning.

IEEE transactions on medical imaging
Convolutional neural networks (CNNs) have achieved state-of-the-art performance for automatic medical image segmentation. However, they have not demonstrated sufficiently accurate and robust results for clinical use. In addition, they are limited by ...