Latest AI and machine learning research in obstetrics & gynecology for healthcare professionals.
In semantic segmentation, the accuracy of models heavily depends on the high-quality annotations. However, in many practical scenarios such as medical imaging and remote sensing, obtaining true annotations is not straightforward and usually requires significant human labor. Relying on human labor often introduces annotation errors, including mislabeling, omissions, and inconsistency between anno...
Pap smear image segmentation is crucial for cervical cancer diagnosis. However, traditional segmentation models often struggle with complex cellular structures and variations in pap smear images. This study proposes a hybrid Dense-UNet201 optimization approach that integrates a pretrained DenseNet201 as the encoder for the U-Net architecture and optimizes it using the spider monkey optimization ...
White matter hyperintensities (WMH) are neuroimaging markers linked to an elevated risk of cognitive decline. WMH severity is typically assessed via v...
Transvaginal ultrasound is a critical imaging modality for evaluating cervical anatomy and detecting physiological changes. However, accurate segmen...
Frequent and long-term exposure to hyperglycemia (i.e., high blood glucose) increases the risk of chronic complications such as neuropathy, nephropa...
This paper explores how artificial intelligence (AI) and robotics are transforming the global labor market. Human workers, limited to a 33% duty cyc...
Unmanned aerial vehicles (UAVs), initially developed for military applications, are now used in various fields. As UAVs become more common across mu...
This study presents a vision-guided robotic control system for automated fruit tree pruning applications. Traditional agricultural practices rely on...
Integration of artificial intelligence (AI) in health and healthcare, especially for older adults, has significantly advanced healthcare delivery. AI ...
Endometriosis (EM) significantly impacts the quality of life, and its diagnosis currently relies on surgery, which carries risks and may miss early le...
LLMs increasingly serve as tools for knowledge acquisition, yet users cannot effectively specify how they want information presented. When users req...
OBJECTIVE: This study aims to develop and validate a machine learning model for identifying individuals within the nursing population experiencing sev...
This integrative literature review examines the evolving role of artificial intelligence (AI) and machine learning (ML) based clinical decision suppor...
Although most physicians are interested in the use of augmented or artificial intelligence (AI) in health care, only 38% are using AI in their practic...
Background Ovarian-Adnexal Reporting and Data System (O-RADS) for MRI helps assign malignancy risk, but radiologist adoption is inconsistent. Automati...
Effective physician-patient communications in pre-diagnostic environments, and most specifically in complex and sensitive medical areas such as infe...
Menstrual health is a critical yet often overlooked aspect of women's healthcare. Despite its clinical relevance, detailed data on menstrual charact...
The growing worldwide incidence of diabetes requires more effective approaches for managing blood glucose levels. Insulin delivery systems have adva...
This study aims to create a deep learning-based classification model for cervical cancer biopsy before and during radiotherapy, visualize the results ...
Data scarcity is a long-standing challenge in the Vision-Language Navigation (VLN) field, which extremely hinders the generalization of agents to un...