AI Medical Compendium Topic

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Labor, Obstetric

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Real-Time Detection of Strawberry Ripeness Using Augmented Reality and Deep Learning.

Sensors (Basel, Switzerland)
Currently, strawberry harvesting relies heavily on human labour and subjective assessments of ripeness, resulting in inconsistent post-harvest quality. Therefore, the aim of this work is to automate this process and provide a more accurate and effici...

Segment-Based Unsupervised Learning Method in Sensor-Based Human Activity Recognition.

Sensors (Basel, Switzerland)
Sensor-based human activity recognition (HAR) is a task to recognize human activities, and HAR has an important role in analyzing human behavior such as in the healthcare field. HAR is typically implemented using traditional machine learning methods....

Deep-Orga: An improved deep learning-based lightweight model for intestinal organoid detection.

Computers in biology and medicine
PROBLEM: Organoids are 3D cultures that are commonly used for biological and medical research in vitro due to their functional and structural similarity to source organs. The development of organoids can be assessed by morphological tests. However, m...

Research on Human-Robot Collaboration Method for Parallel Robots Oriented to Segment Docking.

Sensors (Basel, Switzerland)
In the field of aerospace, large and heavy cabin segments present a significant challenge in assembling space engines. The substantial inertial force of cabin segments' mass often leads to unexpected motion during docking, resulting in segment collis...

Machine Learning in Electronic Health Records: Identifying High-Risk Obstetric Patients Pre and During Labor.

Studies in health technology and informatics
Our goal is to apply artificial intelligence (AI) and statistical analysis to understand the relationship between various factors and outcomes during pregnancy and labor and delivery, in order to personalize birth management and reduce complications ...

Artificial intelligence chatbots versus traditional medical resources for patient education on "Labor Epidurals": an evaluation of accuracy, emotional tone, and readability.

International journal of obstetric anesthesia
BACKGROUND: Labor epidural analgesia is a widely used method for pain relief in childbirth, yet information accessibility for expectant mothers remains a challenge. Artificial intelligence (AI) chatbots like Chat Generative Pre-Trained Transformer (C...

DeepCTG® 2.0: Development and validation of a deep learning model to detect neonatal acidemia from cardiotocography during labor.

Computers in biology and medicine
Cardiotocography (CTG) is the main tool available to detect neonatal acidemia during delivery. Presently, obstetricians and midwives primarily rely on visual interpretation, leading to a significant intra-observer variability. In this paper, we build...

Deep learning model using continuous skin temperature data predicts labor onset.

BMC pregnancy and childbirth
BACKGROUND: Changes in body temperature anticipate labor onset in numerous mammals, yet this concept has not been explored in humans. We investigated if continuous body temperature exhibits similar changes in women and whether these changes may be li...

Accurate prediction of mediolateral episiotomy risk during labor: development and verification of an artificial intelligence model.

BMC pregnancy and childbirth
OBJECTIVE: The study developed an intelligent online evaluation system for mediolateral episiotomy, which incorporated machine learning algorithms and integrated maternal physiological data collected during delivery.

Machine learning combined with infrared spectroscopy for detection of hypertension pregnancy: towards newborn and pregnant blood analysis.

BMC pregnancy and childbirth
Biochemical changes in the cervix during labor are not well understood. This gap in knowledge is significant, as understanding the precise biochemical processes can provide critical insights into the mechanisms of labor and potentially inform better ...