Pregnancy

Early gestational diabetes mellitus risk predictor using neural network with NearMiss.

BACKGROUND: Gestational diabetes mellitus (GDM) is globally recognized as a significant pregnancy-related condition, contributing to complex complications for both mothers and infants. Traditional glucose tolerance tests lack the ability to identify the risk of GDM in early pregnancy, hindering effective prevention and timely intervention during the initial stages.

Bridging gaps in artificial intelligence adoption for maternal-fetal and obstetric care: Unveiling transformative capabilities and challenges.

PURPOSE: This review aims to comprehensively explore the application of Artificial Intelligence (AI) to an area that has not been traditionally explored in depth: the continuum of maternal-fetal health. In doing so, the intent was to examine this physiologically continuous spectrum of mother and child health, as well as to highlight potential pitfalls, and suggest solutions for the same.

A new era of psoriasis treatment: Drug repurposing through the lens of nanotechnology and machine learning.

Psoriasis is a persistent inflammatory skin disorder characterized by hyper-proliferation and abnormal epidermal differentiation. Conventional treatme...

Machine Learning in Intravascular Ultrasound: Validating Automated Lesion Assessment for Complex Coronary Interventions.

BACKGROUND: Intravascular ultrasound (IVUS) is essential for assessing complex coronary lesions, but remains underutilized in part due to difficulties...

TRUSWorthy: toward clinically applicable deep learning for confident detection of prostate cancer in micro-ultrasound.

PURPOSE: While deep learning methods have shown great promise in improving the effectiveness of prostate cancer (PCa) diagnosis by detecting suspiciou...

A multi-task self-supervised approach for mass detection in automated breast ultrasound using double attention recurrent residual U-Net.

Breast cancer is the most common and lethal cancer among women worldwide. Early detection using medical imaging technologies can significantly improve...

Artificial intelligence algorithm for preoperative prediction of FIGO stage in ovarian cancer based on clinical features integrated 18F-FDG PET/CT metabolic and radiomics features.

PURPOSE: The International Federation of Gynecology and Obstetric (FIGO) stage is critical to guiding the treatments of ovarian cancer (OC). We tried ...

Using mathematical modelling and AI to improve delivery and efficacy of therapies in cancer.

Mathematical modelling has proven to be a valuable tool in predicting the delivery and efficacy of molecular, antibody-based, nano and cellular therap...

SegFormer3D: Improving the Robustness of Deep Learning Model-Based Image Segmentation in Ultrasound Volumes of the Pediatric Hip.

Developmental dysplasia of the hip (DDH) is a painful orthopedic malformation diagnosed at birth in 1-3% of all newborns. Left untreated, DDH can lead...

Utilizing machine learning for predicting drug release from polymeric drug delivery systems.

Polymeric drug delivery systems (PDDS) play a crucial role in controlled drug release, providing improved therapeutic outcomes. However, formulating P...

Artificial intelligence in the diagnosis of uveal melanoma: advances and applications.

Advancements in machine learning and deep learning have the potential to revolutionize the diagnosis of melanocytic choroidal tumors, including uveal ...

Cooperative Magnetic Interfacial Microrobot Couple for Versatile Non-Contact Biomedical Applications.

Magnetic interfacial microrobots are increasingly recognized as a promising approach for potential biomedical applications ranging from electronic fun...

Integrating ultrasound radiomics and clinicopathological features for machine learning-based survival prediction in patients with nonmetastatic triple-negative breast cancer.

OBJECTIVE: This study aimed to evaluate the predictive value of implementing machine learning models based on ultrasound radiomics and clinicopatholog...

An 8-point scale lung ultrasound scoring network fusing local detail and global features.

Manual lung ultrasound (LUS) scoring is influenced by clinicians' subjective interpretation, leading to potential inconsistencies and misdiagnoses due...

Diagnosis of Benign and Malignant Newly Developed Nodules on the Surgical Side After Breast Cancer Surgery Based on Machine Learning.

To enhance the diagnostic accuracy of new nodules on the surgical side after breast cancer surgery using machine learning techniques and to explore t...

Predicting preterm birth using machine learning methods.

Preterm birth is a significant public health concern, given its correlation with neonatal mortality and morbidity. The aetiology of preterm birth is c...

A Bi-modal Temporal Segmentation Network for Automated Segmentation of Focal Liver Lesions in Dynamic Contrast-enhanced Ultrasound.

OBJECTIVE: To develop and validate an automated deep learning-based model for focal liver lesion (FLL) segmentation in a dynamic contrast-enhanced ult...

The diagnostic performance of automatic B-lines detection for evaluating pulmonary edema in the emergency department among novice point-of-care ultrasound practitioners.

PURPOSE: B-lines in lung ultrasound have been a critical clue for detecting pulmonary edema. However, distinguishing B-lines from other artifacts is a...

AI and early diagnostics: mapping fetal facial expressions through development, evolution, and 4D ultrasound.

The development of facial musculature and expressions in the human fetus represents a critical intersection of developmental biology, neurology, and e...

Ontology-guided machine learning outperforms zero-shot foundation models for cardiac ultrasound text reports.

Big data can revolutionize research and quality improvement for cardiac ultrasound. Text reports are a critical part of such analyses. Cardiac ultraso...