AIMC Topic: Female

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Deep Learning Approaches for the Assessment of Germinal Matrix Hemorrhage Using Neonatal Head Ultrasound.

Sensors (Basel, Switzerland)
Germinal matrix hemorrhage (GMH) is a critical condition affecting premature infants, commonly diagnosed through cranial ultrasound imaging. This study presents an advanced deep learning approach for automated GMH grading using the YOLOv8 model. By a...

MR_NET: A Method for Breast Cancer Detection and Localization from Histological Images Through Explainable Convolutional Neural Networks.

Sensors (Basel, Switzerland)
Breast cancer is the most prevalent cancer among women globally, making early and accurate detection essential for effective treatment and improved survival rates. This paper presents a method designed to detect and localize breast cancer using deep ...

Prediction of femoral head collapse in osteonecrosis using deep learning segmentation and radiomics texture analysis of MRI.

BMC medical informatics and decision making
BACKGROUND: Femoral head collapse is a critical pathological change and is regarded as turning point in disease progression in osteonecrosis of the femoral head (ONFH). In this study, we aim to build an automatic femoral head collapse prediction pipe...

Pharmacy students' perception and knowledge of chat-based artificial intelligence tools at a Nigerian University.

BMC medical education
BACKGROUND: Chat-based Artificial Intelligence (AI) tools, such as ChatGPT, are becoming integral to various aspects of pharmacy education. However, their integration into the curriculum faces challenges due to students' varying levels of knowledge a...

Medical students and house officers' perception, attitude and potential barriers towards artificial intelligence in Egypt, cross sectional survey.

BMC medical education
BACKGROUND: Artificial intelligence (AI) is one of the sectors of medical research that is expanding the fastest right now in healthcare. AI has rapidly advanced in the field of medicine, helping to treat a variety of illnesses and reducing the numbe...

Construction of a combined prognostic model for pancreatic ductal adenocarcinoma based on deep learning and digital pathology images.

BMC gastroenterology
BACKGROUND: Deep learning has made significant advancements in the field of digital pathology, and the integration of multiple models has further improved accuracy. In this study, we aimed to construct a combined prognostic model using deep learning-...

Innovative label-free lymphoma diagnosis using infrared spectroscopy and machine learning on tissue sections.

Communications biology
The diagnosis of lymphomas is challenging due to their diverse histological presentations and clinical manifestations. There is a need for inexpensive tools that require minimal expertise and are accessible for routine laboratories. Contrastingly, cu...

Development and validation of preeclampsia predictive models using key genes from bioinformatics and machine learning approaches.

Frontiers in immunology
BACKGROUND: Preeclampsia (PE) poses significant diagnostic and therapeutic challenges. This study aims to identify novel genes for potential diagnostic and therapeutic targets, illuminating the immune mechanisms involved.

Detecting the impact of diagnostic procedures in Pap-positive women on anxiety using artificial neural networks.

PloS one
INTRODUCTION: Women who receive a result of an abnormal Papanicolaou (Pap) smear can fail to participate in follow up procedures, and this is often due to anxiety. This study aimed to apply artificial neural networks (ANN) in prediction of anxiety in...