Latest AI and machine learning research in pregnancy for healthcare professionals.
Accurate landmark localization in medical images is a fundamental step for quantitative clinical measurement and downstream analysis. Existing localization methods have advanced, among which multi-stage refinement is a superior solution. Although this strategy mitigates the anatomical ambiguity inherent in single-stage global predictions, its high computational cost limits practical applicability....
Prenatal ultrasound imaging is key for assessing fetal health, but AI progress is limited by scarce, privacy-restricted, and hard-to-annotate datasets. We propose a high-resolution fetal ultrasound synthesis framework based on the EDM2 diffusion architecture, trained on multiple public datasets to generate 512x512 images across six anatomical classes. Our method achieved improved image quality wit...
Facioscapulohumeral muscular dystrophy (FSHD) is a rare neuromuscular disease caused by aberrant re-expression of the embryonic transcription factor D...
A large number of infants with congenital anomalies are born each year globally, especially in areas with underdeveloped medical resources. Currently,...
Fetal brain biometry is essential for quantitative assessment of brain development, supporting gestational age estimation, developmental monitoring, a...
Transcranial focused ultrasound (tFUS) requires accurate estimation of the intracranial acoustic field, which is distorted by skull-induced aberration...
Modern organizations rely on data, machine learning, and software delivery pipelines to move data, train models, deploy applications, refresh dashboar...
Continual learning in clinical imaging faces a dual challenge: a model must assimilate knowledge from new anatomical domains while retaining represent...
Transcranial focused ultrasound (tFUS) is a non-invasive technique that delivers focused acoustic energy through the skull for neuromodulation and the...
Ultrasound imaging has become increasingly widespread in clinical practice due to its portability, low cost and real-time capability, making ultrasoun...
Background Machine learning (ML) has growing potential to support early identification of high-risk pregnancies in resource-constrained settings. Howe...
Background: Healthcare has witnessed administrative staffing roles balloon to twice the number of employed clinicians, resulting in $950 billion per y...
Stigmatizing language in medical documentation may reflect and perpetuate bias, but its prevalence in obstetrics has not been systematically quantifie...
Ovarian cancer is recognized as the deadliest gynecological malignancy. Diagnosis at advanced stages and the lack of effective screening program lead ...
The speed of sound in tissue is a prerequisite for well-focused imaging and has diagnostic value, but recovering it from raw pulse-echo channel data i...
Spatially distributed functional networks are a fundamental property of brain organisation. While these networks are already present at full-term birt...
Medical ultrasound (US) image segmentation faces significant challenges due to speckle noise, low-contrast boundaries, acoustic shadowing, and acquisi...
Affective Image Content Analysis (AICA) aims to recognize and understand emotions elicited by visual content, representing an indispensable step towar...
Concept Bottleneck Models provide interpretable-by-design predictions by mediating diagnosis through human-understandable concepts, but in medical ima...
Lung ultrasound (LUS) is a bedside tool for assessing pulmonary edema in patients at risk due to heart failure or impaired kidney function. However, a...