Pregnancy

Machine learning for predicting neoadjuvant chemotherapy effectiveness using ultrasound radiomics features and routine clinical data of patients with breast cancer.

BACKGROUND: This study explores the clinical value of a machine learning (ML) model based on ultrasound radiomics features of primary foci, combined with clinicopathologic factors to predict the pathological complete response (pCR) of neoadjuvant chemotherapy (NAC) for patients with breast cancer (BC).

Deep Learning Technology for Classification of Thyroid Nodules Using Multi-View Ultrasound Images: Potential Benefits and Challenges in Clinical Application.

BACKGRUOUND: This study aimed to evaluate the applicability of deep learning technology to thyroid ultrasound images for classification of thyroid nodules.

Weakly-supervised thyroid ultrasound segmentation: Leveraging multi-scale consistency, contextual features, and bounding box supervision for accurate target delineation.

Weakly-supervised learning (WSL) methods have gained significant attention in medical image segmentation, but they often face challenges in accurately...

Robotic Microcapsule Assemblies with Adaptive Mobility for Targeted Treatment of Rugged Biological Microenvironments.

Microrobots are poised to transform biomedicine by enabling precise, noninvasive procedures. However, current magnetic microrobots, composed of solid ...

The Future of Parenthood? Examining the Promise and Complexity of Pregnancy Robots in Reproductive Health.

Advancements in reproductive technology are now approaching an unprecedented frontier: the pregnancy robot, a potential artificial womb capable of car...

Artificial intelligence performance in ultrasound-based lymph node diagnosis: a systematic review and meta-analysis.

BACKGROUND AND OBJECTIVES: Accurate classification of lymphadenopathy is essential for determining the pathological nature of lymph nodes (LNs), which...

Predictive value of machine learning for the progression of gestational diabetes mellitus to type 2 diabetes: a systematic review and meta-analysis.

BACKGROUND: This systematic review aims to explore the early predictive value of machine learning (ML) models for the progression of gestational diabe...

Development of immune-derived molecular markers for preeclampsia based on multiple machine learning algorithms.

Preeclampsia (PE) is a major pregnancy-specific cardiovascular complication posing latent life-threatening risks to mothers and neonates. The contribu...

The ANFIS-RSM based multi-objective optimization and modelling of ultrasound-assisted extraction of polyphenols from jamun fruit (Syzygium cumini).

Given their potential as natural substitutes for artificial additives and their health advantages, the extraction of bioactive substances like polyphe...

Clinical Application of Deep Learning-Assisted Needles Reconstruction in Prostate Ultrasound Brachytherapy.

PURPOSE: High dose rate (HDR) prostate brachytherapy (BT) procedure requires image-guided needle insertion. Given that general anesthesia is often emp...

Improving spleen segmentation in ultrasound images using a hybrid deep learning framework.

This paper introduces a novel method for spleen segmentation in ultrasound images, using a two-phase training approach. In the first phase, the SegFor...

'Applications of machine learning in liposomal formulation and development'.

Machine learning (ML) has emerged as a transformative tool in drug delivery, particularly in the design and optimization of liposomal formulations. Th...

Machine learning-based prediction model integrating ultrasound scores and clinical features for the progression to rheumatoid arthritis in patients with undifferentiated arthritis.

OBJECTIVES: Predicting rheumatoid arthritis (RA) progression in undifferentiated arthritis (UA) patients remains a challenge. Traditional approaches c...

DP-CLAM: A weakly supervised benign-malignant classification study based on dual-angle scanning ultrasound images of thyroid nodules.

In this paper, a two-stage task weakly supervised learning algorithm is proposed. It accurately achieved patient-level classification task of benign a...

High-resolution hemodynamic estimation from ultrafast ultrasound image velocimetry using a physics-informed neural network.

Estimating the high-resolution (HR) blood flow velocity and pressure fields for the diagnosis and treatment of vascular diseases remains challenging.....

Performance of Artificial Intelligence Chatbots on Ultrasound Examinations: Cross-Sectional Comparative Analysis.

BACKGROUND: Artificial intelligence chatbots are being increasingly used for medical inquiries, particularly in the field of ultrasound medicine. Howe...

PADS-Net: GAN-based radiomics using multi-task network of denoising and segmentation for ultrasonic diagnosis of Parkinson disease.

Parkinson disease (PD) is a prevalent neurodegenerative disorder, and its accurate diagnosis is crucial for timely intervention. We propose the PArkin...

Toward Resolving Heterogeneous Mixtures of Nanocarriers in Drug Delivery Systems through Light Scattering and Machine Learning.

Nanocarriers (NCs) have emerged as a revolutionary approach in targeted drug delivery, promising to enhance drug efficacy and reduce toxicity through ...

Effective BCDNet-based breast cancer classification model using hybrid deep learning with VGG16-based optimal feature extraction.

PROBLEM: Breast cancer is a leading cause of death among women, and early detection is crucial for improving survival rates. The manual breast cancer ...

ULM-MbCNRT: In Vivo Ultrafast Ultrasound Localization Microscopy by Combining Multibranch CNN and Recursive Transformer.

Ultrasound localization microscopy (ULM) overcomes the acoustic diffraction limit by localizing tiny microbubbles (MBs), thus enabling the microvascul...