Pregnancy

Stratifying High-Risk Thyroid Nodules Using a Novel Deep Learning System.

INTRODUCTION: The current ultrasound scan classification system for thyroid nodules is time-consuming, labor-intensive, and subjective. Artificial intelligence (AI) has been shown to increase the accuracy of predicting the malignancy rate of thyroid nodules. This study aims to demonstrate the state-of-the-art Swin Transformer to classify thyroid nodules.

Incremental learning for an evolving stream of medical ultrasound images via counterfactual thinking.

Despite the fact that traditional deep learning (DL) approaches provide promising accuracy and efficiency in medical ultrasound image analysis, they cannot replace the physician in making a diagnosis since the DL model is only appropriate in static application scenarios. Currently, most DL-based models are incapable of learning new tasks in the dynamic clinical environments due to the catastrophic...

Applying deep learning to recognize the properties of vitreous opacity in ophthalmic ultrasound images.

BACKGROUND: To explore the feasibility of artificial intelligence technology based on deep learning to automatically recognize the properties of vitre...

Multimodality deep learning radiomics nomogram for preoperative prediction of malignancy of breast cancer: a multicenter study.

. Breast cancer is the most prevalent cancer diagnosed in women worldwide. Accurately and efficiently stratifying the risk is an essential step in ach...

Africa's readiness for artificial intelligence in clinical radiotherapy delivery: Medical physicists to lead the way.

BACKGROUND: There have been several proposals by researchers for the introduction of Artificial Intelligence (AI) technology due to its promising role...

Deep learning-based assessment of CT markers of sarcopenia and myosteatosis for outcome assessment in patients with advanced pancreatic cancer after high-intensity focused ultrasound treatment.

OBJECTIVES: To evaluate the prognostic value of CT-based markers of sarcopenia and myosteatosis in comparison to the Eastern Cooperative Oncology Grou...

Evaluation of the effectiveness of artificial intelligence for ultrasound guided peripheral nerve and plane blocks in recognizing anatomical structures.

BACKGROUND: We aimed to assess the accuracy of artificial intelligence (AI) based real-time anatomy identification for ultrasound-guided peripheral ne...

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 seve...

Fetal electrocardiography and artificial intelligence for prenatal detection of congenital heart disease.

INTRODUCTION: This study aims to investigate non-invasive electrocardiography as a method for the detection of congenital heart disease (CHD) with the...

Fetal MRI: what's new? A short review.

Fetal magnetic resonance imaging (fetal MRI) is usually performed as a second-level examination following routine ultrasound examination, generally ex...

Deep learning-based ultrasound transducer induced CT metal artifact reduction using generative adversarial networks for ultrasound-guided cardiac radioablation.

In US-guided cardiac radioablation, a possible workflow includes simultaneous US and planning CT acquisitions, which can result in US transducer-induc...

Diffusion-Limited Processes in Hydrogels with Chosen Applications from Drug Delivery to Electronic Components.

Diffusion is one of the key nature processes which plays an important role in respiration, digestion, and nutrient transport in cells. In this regard,...

Interaction between maintenance variables of medical ultrasound scanners through multifactor dimensionality reduction.

BACKGROUND: Proper maintenance of electro-medical devices is crucial for the quality of care to patients and the economic performance of healthcare or...

Real-time Classification of Fetal Status Based on Deep Learning and Cardiotocography Data.

This study uses convolutional neural networks (CNNs) and cardiotocography data for the real-time classification of fetal status in the mobile applicat...

Robot-assisted Laparoscopic Excision of Abdominal Wall Endometrioma Utilizing Intraoperative Ultrasound and Transabdominal Needle Placement.

OBJECTIVE: In patients with endometriosis, extra pelvic endometriosis is estimated to have an incidence of 11% and a rare subset of extra pelvic lesio...

Improved pregnancy prediction performance in an updated deep-learning embryo selection model: a retrospective independent validation study.

RESEARCH QUESTION: What is the effect of increasing training data on the performance of ongoing pregnancy prediction after single vitrified-warmed bla...

Microrobots for Biomedicine: Unsolved Challenges and Opportunities for Translation.

Microrobots are being explored for biomedical applications, such as drug delivery, biological cargo transport, and minimally invasive surgery. However...

Frequency constraint-based adversarial attack on deep neural networks for medical image classification.

The security of AI systems has gained significant attention in recent years, particularly in the medical diagnosis field. To develop a secure medical ...

Highly Flexible Deep-Learning-Based Automatic Analysis for Graphically Encoded Hydrogel Microparticles.

Graphically encoded hydrogel microparticle (HMP)-based bioassay is a diagnostic tool characterized by exceptional multiplex detectability and robust s...

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

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