Pregnancy

Noninvasive Assessment of the Ulnar Nerve in the Upper Extremity Using Machine Learning.

PURPOSE: The purpose of this study was to develop a machine learning algorithm trained on ultrasound images of the cubital tunnel that can be used to automatically identify, segment, and measure the cross-sectional area (CSA) of the ulnar nerve. METHODS: Control subjects and patients were scanned using a Fujifilm-SonoSite ultrasound system, equipped with a high-frequency linear array probe by a tr...

Artificial intelligence meets point-of-care ultrasound: implications for pediatric emergency and critical care.

PURPOSE OF REVIEW: Artificial intelligence has been integrated in nearly all aspects of clinical care to improve patient outcomes, augment human capabilities and improve efficiency. Particularly in the field of radiology and medical imaging, artificial intelligence could revolutionize how care is delivered. In this review, we review the recent literature and provide an assessment of the advantages...

3D deep-learning radiomics from MR-T2WI for predicting placenta accreta spectrum disorders: A multicenter study.

OBJECTIVE: To develop a three-dimensional (3D) deep-learning radiomics from magnetic resonance-T2-weighted imaging (T2WI) for predicting the risk of p...

Automated Segmentation of Stellate Ganglion Block Region in Ultrasound Images Using Deep Learning Model.

BACKGROUND: The stellate ganglion region is densely vascularized and innervated, making the stellate ganglion block (SGB) technically challenging unde...

Estimating the odds ratio from the output scores of machine learning models: possibilities and limitations.

Estimation of exposure-response association is central to epidemiologic research. Although the advantages of machine learning (ML) techniques for mode...

Evaluation of AI language models in answering pregnancy-related questions assessed by obstetrics specialists.

This study aimed to compare the performance of three large language models-ChatGPT-3.5, Gemini, and ChatGPT-4.0-in generating responses to ten frequen...

Small-bowel capsule endoscopy - from clinic to couch: time to go green?

PURPOSE OF REVIEW: Small-bowel capsule endoscopy (SBCE) has transformed small-intestine diagnostics by enabling direct, noninvasive mucosal visualizat...

Artificial intelligence to support ultrasound in the detection of developmental dysplasia of the hip : a scoping review.

AIMS: Ultrasound is a highly sensitive method to detect developmental dysplasia of the hip (DDH). However, the cost of expert sonographers performing ...

Leveraging Machine Learning to Uncover Ethnic-Specific Predictors of Maternal Postpartum Depression.

Maternal postpartum depression (PPD) is a significant mental health issue that affects over 15% of mothers, with adverse effects on their children. Et...

Machine Learning-Assisted Screening of High-Entropy Sub-1 nm Nanowires for Ultrasound-Augmented Pancatalytic Tumor Therapy.

The rational design of catalytic nanomaterials, particularly high-entropy alloy (HEA) nanomaterials with their unique structures, is crucial for advan...

Corrigendum to "Predicting postpartum glucose intolerance in women with gestational diabetes mellitus in primary care: A machine learning approach using XGBoost and SHAP values" [Diabetes Res. Clin. Pract. 232 (2026) 113098].

Use of smart glasses for ultrasound-guided radial arterial catheterisation in obese patients: study protocol for a single-centre randomised clinical trial in China.

INTRODUCTION: Ultrasound guidance improves the success rate and efficiency of radial arterial catheterisation (RAC). However, the procedure remains mo...

Automated Segmentation of Median Nerve Cross-Sectional Area in Healthy Controls and Patients With Carpal Tunnel Syndrome Using a Convolutional Neural Network.

INTRODUCTION/AIMS: The cross-sectional area (CSA) of the median nerve (MN) is a key parameter for confirming carpal tunnel syndrome (CTS) with ultraso...

Enhancing breast cancer diagnostics: Shape-aware angular feature learning for precision in breast cancer classification.

Breast cancer is a life-threatening disease that is very common among women in the world. The early and correct diagnosis is necessary to enhance the ...

Neuroprognostication via Radiomics and Machine Learning Following Neonatal Hypoxic-Ischemic Insult.

OBJECTIVE: To produce objective predictions of neurodevelopmental outcomes after perinatal hypoxic-ischemic insult for the full spectrum of severity o...

Hydrogel-enabled drug delivery for ulcerative colitis.

Ulcerative colitis (UC) represents a chronic, relapsing inflammatory condition primarily affecting the colonic and rectal mucosa, presenting substanti...

Artificial Intelligence-Assisted Point-of-Care Ultrasound for Evaluating Left Ventricular Ejection Fraction: A Systematic Review of Prospective Observational Studies.

Artificial intelligence (AI) embedded in point-of-care ultrasound (POCUS) could reduce operator dependence in left ventricular ejection fraction (LVEF...

Interpretable machine learning based on intratumoral and peritumoral ultrasound radiomics for predicting central lymph node metastasis in papillary thyroid carcinoma.

OBJECTIVES: This retrospective and single-center study aimed to develop machine learning (ML) model integrating clinical features, ultrasound (US) fea...

Early Maturation of Heart Rate Variability in Very Preterm Infants Depends on Neonatal Factors and Is Associated With Neurodevelopmental Risk.

AIM: This study investigated central autonomic network maturation deviations using a previously defined machine learning model set to estimate a funct...

Digital Transformation in Clinical Genetics: A Concept Analysis.

Using technology to radically improve performance or the influence of business, known as digital transformation, could reshape health care by making c...