Pregnancy

Advancing breast cancer diagnosis through ultrasound imaging: A cross-attention multi-scale vision transformer approach with comparative analysis against ResNet architectures.

In recent years, the field of medical imaging has witnessed substantial progress due to the integration of advanced machine learning techniques, particularly in the diagnosis of critical conditions such as breast cancer. This study aims to improve the predictive accuracy of breast cancer diagnosis using ultrasound images by employing the cross-attention multi-scale vision transformer (CrossViT). T...

MARTP: a multi-agent simulation framework for automated radiation therapy planning based on LLMs.

Objective.High-quality radiotherapy requires accurate dose delivery to target volumes while protecting organs-at-risk. However, current clinical workflows remain constrained by labor-intensive multidisciplinary collaboration, prolonged planning cycles, and limited scalability. Intelligent automation capable of integrating clinical knowledge and real-world decision patterns is needed to enhance pre...

Machine learning prediction of axillary lymph node metastasis using multimodal ultrasound in breast cancer.

OBJECTIVES: Accurate prediction of axillary lymph node metastasis (ALNM) is crucial for tailoring breast cancer treatments, this study aimed to develo...

Prostatic artery embolization and imaging-based biomarkers: current tools in monitoring treatment response and emerging strategies.

Prostatic artery embolization (PAE) is a safe and effective minimally invasive treatment for lower urinary tract symptoms (LUTS) attributed to benign ...

NurtureNest: An IoT-wearable predictive analytics framework for real-time maternal risk assessment.

Pregnancy-related complications are increasing globally, necessitating timely and accurate risk prediction for effective clinical intervention. This p...

Automated neonatal sleep positioning assessment by video monitoring and machine learning.

BACKGROUND: Proper positioning during sleep is critical for musculoskeletal and neurological development of preterm neonates, yet current manual asses...

Near-perfect genome sequencing in medical genetics.

Medical genetics currently operates through a fragmented diagnostic cascade built around short-read sequencing technologies that carry well-documented...

Repurposing Non-oncologic Drugs via Targeted Nanocarriers for Cancer Therapy: Mechanisms, Synergistic Combinations, and Clinical Translation.

The urgent need for innovative cancer therapies has driven increasing interest in repurposing drugs originally developed for non-oncological diseases....

Prenatal Cerium Exposure and Neural Tube Defect Risk: A Case-control Study in Shanxi, China.

Neural tube defects (NTDs) exhibit a multifaceted etiology. Limited research has assessed the association between exposure to metallic and non-metalli...

Maternal circulating lipid metabolic heterogeneity in preeclampsia: machine learning identifies clinically distinct subtypes.

BACKGROUND: Preeclampsia is associated with dyslipidemia. Maternal circulating lipid profiles may reflect underlying placental-metabolic interactions....

A two-stage deep learning model with segmentation-guided top-K slice selection for patient-level PAS prediction on MRI.

OBJECTIVE: Placenta accreta spectrum (PAS) can cause severe obstetric complications, and accurate preoperative assessment is critical. However, hetero...

Transvaginal ultrasound-based radiomics and integrated clinical indicators via multimodal deep learning for prediction of endometrial polyp recurrence after hysteroscopic surgery.

OBJECTIVES: To evaluate a multimodal deep learning model integrating preoperative transvaginal ultrasound (TVUS)-based radiomics features and clinical...

[Deep learning-driven high-frequency ultrasound method for skin aging assessment].

Ultraviolet radiation is a primary external factor contributing to skin photoaging, as it induces cellular deoxyribonucleic acid damage and collagen d...

3D machine learning-based complexity variability and fluidity quantification of preterm and writhing general movements.

General Movement Assessment (GMA) is a reliable non-invasive method for the early detection of neurodevelopmental disorders in infants, based on the q...

Development of a prediction model for infant hospitalisation and death using clinical features assessed by community health workers during routine postnatal home visits in Dhaka, Bangladesh.

BACKGROUND: To improve upon the WHO 8 danger signs used to identify young infants (<2 months) requiring referral during community health worker (CHW) ...

Diagnosis of Small Focal Liver Lesions (≤2 cm): A Deep Learning Approach Based on B-Mode Ultrasound and Contrast-Enhanced Ultrasound.

BACKGROUND: To develop and validate a deep learning (DL) model based on feature fusion with B-mode ultrasound (BMUS) and contrast enhanced ultrasound ...

Deep transfer learning for detection of placenta-mediated diseases from ultrasound images.

Placenta-mediated diseases, such as preeclampsia (PE) and small-for-gestational-age (SGA) neonates, are associated with structural and functional chan...

Roles and applications of artificial intelligence in fetal and placental MRI: a literature review.

BACKGROUND: Artificial intelligence (AI) has been increasingly integrated with fetal and placental magnetic resonance imaging (MRI) to enhance the det...

Development and validation of an interpretable ultrasound radiomics model for benign and malignant classification of breast lesions: a multicenter large-sample study.

OBJECTIVES: To develop and validate a combined ultrasound-based radiomics-clinical model for differentiating benign and malignant breast lesions. MATE...

An intelligent point-of-care photoacoustic and ultrasound dual-modality imaging system for Rheumatoid arthritis in fingers: Technical development and initial clinical test.

Aiming at an intelligent point-of-care imaging technology for rheumatology clinics, a fully automatic 3D photoacoustic (PA) and ultrasound (US) dual-m...