Obstetrics & Gynecology

Weakly and semi-supervised segmentation of uterus using coarse annotations in MRI.

Endometrial cancer seriously threatens women's lives via invasion and metastasis, potentially causing multi-system organ failure. Accurate segmentation of the uterus on MR images has important implications for determining the depth of myometrial infiltration in endometrial cancer, and for benign uterine diseases such as uterine fibroids, it enables efficient quantification of uterine volume and cl...

Radiographic choice for skeletal maturation assessment: a systematic review of Cervical Vertebrae Method and Middle Phalanx Method.

A concept-enhanced, knowledge graph-guided framework for interpretable PCOS prediction: a case study in explainable biomedical artificial intelligence.

CONTEXT: Overcoming the disconnect between efficient Machine Learning (ML) techniques and its therapeutic applicability necessitates frameworks that p...

Machine Learning Prediction of Prevertebral Soft Tissue Swelling after Single-Level Anterior Cervical Surgery : A Proof-of-Concept Study.

OBJECTIVE: Prevertebral soft tissue swelling (PSTS) is a significant complication of anterior cervical spine surgery (ACSS) that causes dysphagia, dys...

Artificial Intelligence in Diabetes Care.

BACKGROUND: Diabetes care requires frequent and high-stakes decisions that must be made in the setting of substantial day-to-day physiologic variabili...

Developing and validating ultrasound-based machine-learning models incorporating radiomics features to predict malignancy in adnexal masses.

OBJECTIVE: The primary aim of this study was to develop and internally validate ultrasound-based radiomics models to discriminate between all types of...

A two-stage deep learning system for cervical spine fracture diagnosis: integrating 3D segmentation and 2.5D classification on CT images.

Automatic Segmentation of Placenta from MR images Using a Novel BiGC U-Net.

Accurate segmentation of the placenta in Magnetic Resonance (MR) images is required for quantitative techniques such as texture and shape analysis, wh...

Can classification strategies improve automated cervical vertebral maturation staging? A comparative study.

The Dawn of PACS 3.0: The Evolution from Image Access to Workflow Intelligence.

Picture Archiving and Communication Systems (PACS) have evolved over decades in response to changes in imaging technology, network and data storage in...

Deep Learning for Medical Ultrasound Image Segmentation: A Systematic Review of the Current Research.

Deep learning (DL) has enabled automated segmentation of ultrasound images, and due to the rapid development of DL models, we want to offer a comprehe...

Key predictive factors of breast cancer based on race using machine learning models.

PURPOSE: In this research, key factors influencing breast cancer risk, a major global issue, are investigated, using machine learning (ML) and explain...

Reinforcement learning for real-time adaptive radiotherapy.

State-of-the-art radiotherapy machines with integrated magnetic resonance (MR) imaging, known as MR-Linacs, provide the capability to track tumors in ...

Trends and disparities in female authorship in surgical publications: A large-scale bibliometric study using natural language processing.

BACKGROUND: Gender disparities in academic surgery persist, particularly in authorship positions that signal leadership, mentorship, and scholarly inf...

Enabling time-aware treatment plan evaluation for clinical proton pencil beam scanning systems.

INTRODUCTION: Clinical Treatment Planning Systems (TPS) for proton pencil beam scanning (PBS) typically do not consider treatment delivery time, limit...

Assessing the Performance and Reliability of Deep Learning Auto-Segmentation in Videofluoroscopic Swallowing Studies: A Systematic Review and Meta-Analysis.

OBJECTIVE: To systematically evaluate the accuracy and reliability of deep learning-based auto-segmentation methods in videofluoroscopic swallowing st...

XGBoost-Cox modeling with SHAP analysis for survival prediction in ovarian cancer patients: a retrospective cohort study.

DWI-derived intratumoral, peritumoral, and habitat features for preoperative prediction of lymph node metastasis in early-stage cervical cancer using machine learning method.

OBJECTIVES: This study aimed to develop a novel radiomic model by incorporating features from both habitat subregions and peritumoral regions to preop...

Employing in-context learning prompts with large language models for drone routing in delivery services.

Autonomous Aerial Vehicles (AAVs) - known as drones - employment in delivery services is one of the promising transformative technologies. The AAV ind...

Toward animal-free approaches in subcutaneous drug delivery: A pain management case study.

Subcutaneous (s.c.) administration offers practical advantages for long-acting drug delivery, yet its complex tissue environment, lack of standardized...