Pregnancy

Innovation Diffusion Across 13 Specialties and Associated Clinician Characteristics.

Diffusion of innovations, defined as the adoption and implementation of new ideas, processes, products, or services in health care, is both particularly important and especially challenging. One known problem with adoption and implementation of new technologies is that, while organizations often make innovations immediately available, organizational actors are more wary about adopting new technolo...

Deep Learning of Multimodal Ultrasound: Stratifying the Response to Neoadjuvant Chemotherapy in Breast Cancer Before Treatment.

BACKGROUND: Not only should resistance to neoadjuvant chemotherapy (NAC) be considered in patients with breast cancer but also the possibility of achieving a pathologic complete response (PCR) after NAC. Our study aims to develop 2 multimodal ultrasound deep learning (DL) models to noninvasively predict resistance and PCR to NAC before treatment.

PractiCPP: a deep learning approach tailored for extremely imbalanced datasets in cell-penetrating peptide prediction.

MOTIVATION: Effective drug delivery systems are paramount in enhancing pharmaceutical outcomes, particularly through the use of cell-penetrating pepti...

Human-computer interactions and compassionate healthcare: A Wizard of Oz study using a self-administered AI-assisted cognitive assessment

Artificial intelligence (AI) is increasingly transforming healthcare, however, the interaction between the user, AI, and the user’s environment is poo...

Learning technology for detection and grading of cancer tissue using tumour ultrasound images1.

BACKGROUND: Early diagnosis of breast cancer is crucial to perform effective therapy. Many medical imaging modalities including MRI, CT, and ultrasoun...

Deep-learning model for prenatal congenital heart disease screening generalizes to community setting and outperforms clinical detection.

OBJECTIVES: Despite nearly universal prenatal ultrasound screening programs, congenital heart defects (CHD) are still missed, which may result in seve...

Leveraging 3D Echocardiograms to Evaluate AI Model Performance in Predicting Cardiac Function on Out-of-Distribution Data.

Advancements in medical imaging and artificial intelligence (AI) have revolutionized the field of cardiac diagnostics, providing accurate and efficien...

Software that combines deep learning, 3D reconstruction and CFD to analyze the state of carotid arteries from ultrasound imaging.

BACKGROUND: Ultrasound is one of the non-invasive techniques that are used in clinical diagnostics of carotid artery disease.

Machine-learning-guided Directed Evolution for AAV Capsid Engineering.

Target gene delivery is crucial to gene therapy. Adeno-associated virus (AAV) has emerged as a primary gene therapy vector due to its broad host range...

Exploring the Potential of Artificial Intelligence in Breast Ultrasound.

Breast ultrasound has emerged as a valuable imaging modality in the detection and characterization of breast lesions, particularly in women with dense...

Overview of artificial intelligence in point-of-care ultrasound. New horizons for respiratory system diagnoses.

Throughout the past decades ultrasonography did not prove to be a procedure of choice if regarded as part of the routine bedside examination. The reas...

Understanding the Manufacturing Process of Lipid Nanoparticles for mRNA Delivery Using Machine Learning.

Lipid nanoparticles (LNPs), used for mRNA vaccines against severe acute respiratory syndrome coronavirus 2, protect mRNA and deliver it into cells, ma...

Robotic Pills as Innovative Personalized Medicine Tools: A Mini Review.

The most common route for drug administration is the oral route due to the various advantages offered by this route, such as ease of administration, c...

An XGBoost Machine Learning Based Model for Predicting Ki-67 Value ≥ 15% in TNM Stage Primary Breast Cancer Receiving Neoadjuvant Chemotherapy Using Clinical Data and Delta-Radiomic Features on Ultrasound Images and Overall Survival Analysis: A 5-Year Postoperative Follow-Up Study.

To establish a model based on clinical and delta-radiomic features within ultrasound images using XGBoost machine learning to predict proliferation-a...

Modelling a self-defined CNN for effectual classification of PCOS from ultrasound images.

BACKGROUND: Polycystic Ovary Syndrome (PCOS) is a medical condition that causes hormonal disorders in women in their childbearing years. The hormonal ...

Identifying and Visualizing Global Research Trends and Hotspots of Artificial Intelligence in Medical Ultrasound: A Bibliometric Analysis.

BACKGROUND: Applications of artificial intelligence (AI) in medical ultrasound have rapidly grown in recent years. Therefore, it is necessary to ident...

The Growing Impact of Natural Language Processing in Healthcare and Public Health.

Natural Language Processing (NLP) is a subset of Artificial Intelligence, specifically focused on understanding and generating human language. NLP tec...

Predicting Calcein Release from Ultrasound-Targeted Liposomes: A Comparative Analysis of Random Forest and Support Vector Machine.

OBJECTIVE: This study presents a comparative analysis of RF and SVM for predicting calcein release from ultrasound-triggered, targeted liposomes under...

[Artificial intelligence in ultrasound diagnosis of thyroid nodules].

OBJECTIVE: To analyze the efficacy of the S-Detect AI system of the Samsung RS85 ultrasound scanner (South Korea) in stratifying thyroid nodules compa...

Shape-aware Segmentation of the Placenta in BOLD Fetal MRI Time Series.

Blood oxygen level dependent (BOLD) MRI time series with maternal hyperoxia can assess placental oxygenation and function. Measuring precise BOLD chan...