Pregnancy

Predicting Placenta Accreta Spectrum Disorder Through Machine Learning Using Metabolomic and Lipidomic Profiling and Clinical Characteristics.

OBJECTIVE: To perform metabolomic and lipidomic profiling with plasma samples from patients with placenta accreta spectrum (PAS) to identify possible biomarkers for PAS and to predict PAS with machine learning methods that incorporated clinical characteristics with metabolomic and lipidomic profiles.

Automated whole-breast ultrasound tumor diagnosis using attention-inception network.

PURPOSE: Automated Whole-Breast Ultrasound (ABUS) has been widely used as an important tool in breast cancer diagnosis due to the ability of this technique to provide complete three-dimensional (3D) images of breasts. To eliminate the risk of misdiagnosis, computer-aided diagnosis (CADx) systems have been proposed to assist radiologists. Convolutional neural networks (CNNs), renowned for the autom...

Predicting long-term patency of radiocephalic arteriovenous fistulas with machine learning and the PREDICT-AVF web app.

The goal of this study was to expand our previously created prediction tool (PREDICT-AVF) and web app by estimating long-term primary and secondary pa...

Developmental toxicity: artificial intelligence-powered assessments.

Regulatory agencies require comprehensive toxicity testing for prenatal drug exposure, including new drugs in development, to reduce concerns about de...

Comparative analysis of deep learning methods for breast ultrasound lesion detection and classification.

PURPOSE: Breast ultrasound (BUS) computer-aided diagnosis (CAD) systems aims to perform two major steps: detecting lesions and classifying them as ben...

Temporal validation of machine learning models for pre-eclampsia prediction using routinely collected maternal characteristics: A validation study.

BACKGROUND: Pre-eclampsia (PE) contributes to more than one-fourth of all maternal deaths and half a million newborn deaths worldwide every year. Earl...

Paving the way for bacteria-based drug delivery: biohybrid microrobots emerging from microrobotics and synthetic biology.

Advances in microrobotics and synthetic biology are paving the way for innovative solutions to long-standing challenges in drug delivery. Both fields ...

Towards robust multimodal ultrasound classification for liver tumor diagnosis: A generative approach to modality missingness.

BACKGROUND AND OBJECTIVE: In medical image analysis, combining multiple imaging modalities enhances diagnostic accuracy by providing complementary inf...

Predicting pregnancy-related pelvic girdle pain using machine learning.

BACKGROUND: Pregnancy-related pelvic girdle pain (PPGP) is a common complication during gestation which negatively influences pregnant women's quality...

ChatGPT-4's Accuracy in Estimating Thyroid Nodule Features and Cancer Risk From Ultrasound Images.

OBJECTIVE: To evaluate the performance of GPT-4 and GPT-4o in accurately identifying features and categories from thyroid nodule ultrasound images fol...

Artificial intelligence in fetal brain imaging: Advancements, challenges, and multimodal approaches for biometric and structural analysis.

Artificial intelligence (AI) is transforming fetal brain imaging by addressing key challenges in diagnostic accuracy, efficiency, and data integration...

Artificial intelligence-assisted endoscopic ultrasound diagnosis of esophageal subepithelial lesions.

BACKGROUND: Endoscopic ultrasound (EUS) is one of the most accurate methods for determining the originating layer of subepithelial lesions (SELs). How...

MR2US-Pro: Prostate MR to Ultrasound Image Translation and Registration Based on Diffusion Models

The diagnosis of prostate cancer increasingly depends on multimodal imaging, particularly magnetic resonance imaging (MRI) and transrectal ultrasoun...

Contrast-Invariant Self-supervised Segmentation for Quantitative Placental MRI

Accurate placental segmentation is essential for quantitative analysis of the placenta. However, this task is particularly challenging in T2*-weight...

EgoExOR: An Ego-Exo-Centric Operating Room Dataset for Surgical Activity Understanding

Operating rooms (ORs) demand precise coordination among surgeons, nurses, and equipment in a fast-paced, occlusion-heavy environment, necessitating ...

Deep learning based motion correction in ultrasound microvessel imaging approach improves thyroid nodule classification.

To address inter-frame motion artifacts in ultrasound quantitative high-definition microvasculature imaging (qHDMI), we introduced a novel deep learni...

Intelligent deep learning model for targeted cancer drug delivery.

Nanotechnology and information communication technology (ICT) are being combined to develop innovative drug delivery systems for targeted sites, such ...

PCA for Enhanced Cross-Dataset Generalizability in Breast Ultrasound Tumor Segmentation

In medical image segmentation, limited external validity remains a critical obstacle when models are deployed across unseen datasets, an issue parti...

Prediction of clinical stages of cervical cancer via machine learning integrated with clinical features and ultrasound-based radiomics.

To investigate the prediction of a model constructed by combining machine learning (ML) with clinical features and ultrasound radiomics in the clinica...

High Volume Rate 3D Ultrasound Reconstruction with Diffusion Models

Three-dimensional ultrasound enables real-time volumetric visualization of anatomical structures. Unlike traditional 2D ultrasound, 3D imaging reduc...