AIMC Topic: Pregnancy
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Deep learning-based differentiation of ventricular septal defect from tetralogy of Fallot in fetal echocardiography images.
Technology and health care : official journal of the European Society for Engineering and Medicine
Jan 1, 2024
BACKGROUND: Congenital heart disease (CHD) seriously affects children's health and quality of life, and early detection of CHD can reduce its impact on children's health. Tetralogy of Fallot (TOF) and ventricular septal defect (VSD) are two types of ...
Common on "comparison of cesarean delivery outcome after robotic and laparoscopic myomectomy".
Taiwanese journal of obstetrics & gynecology
Jan 1, 2024
Artificial intelligence as a new answer to old challenges in maternal-fetal medicine and obstetrics.
Technology and health care : official journal of the European Society for Engineering and Medicine
Jan 1, 2024
BACKGROUND: Following the latest trends in the development of artificial intelligence (AI), the possibility of processing an immense amount of data has created a breakthrough in the medical field. Practitioners can now utilize AI tools to advance dia...
Health Disinformation Use Case Highlighting the Urgent Need for Artificial Intelligence Vigilance: Weapons of Mass Disinformation.
JAMA internal medicine
Jan 1, 2024
IMPORTANCE: Although artificial intelligence (AI) offers many promises across modern medicine, it may carry a significant risk for the mass generation of targeted health disinformation. This poses an urgent threat toward public health initiatives and...
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
Jan 1, 2024
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...
Identify gestational diabetes mellitus by deep learning model from cell-free DNA at the early gestation stage.
Briefings in bioinformatics
Nov 22, 2023
Gestational diabetes mellitus (GDM) is a common complication of pregnancy, which has significant adverse effects on both the mother and fetus. The incidence of GDM is increasing globally, and early diagnosis is critical for timely treatment and reduc...
Evaluating the Effects of Misinformation on Public Sentiments Surrounding Access to Abortion Through Social Media Sentiment Analytics.
Studies in health technology and informatics
Oct 20, 2023
As social media use has grown in recent years, ease of access and rapid data collection through online social media has permitted researchers to measure and track sentiments related to emerging public health threats. Herein, we explore the possibilit...
Effects of 0.15% ropivacaine alone and combination with sufentanil on epidural labor analgesia and adverse reactions.
African health sciences
Sep 1, 2023
OBJECTIVE: The aim of this study was to compare the impacts of 0.15% ropivacaine alone and 0.15% ropivacaine combined with sufentanil on epidural labor analgesia.
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
Sep 1, 2023
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...
The Effect of Fetal Heart Rate Segment Selection on Deep Learning Models for Fetal Compromise Detection.
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Jul 1, 2023
Monitoring the fetal heart rate (FHR) is common practice in obstetric care to assess the risk of fetal compromise. Unfortunately, human interpretation of FHR recordings is subject to inter-observer variability with high false positive rates. To impro...
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