AIMC Topic: Female

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A despeckling method for ultrasound images utilizing content-aware prior and attention-driven techniques.

Computers in biology and medicine
The despeckling of ultrasound images contributes to the enhancement of image quality and facilitates precise treatment of conditions such as tumor cancers. However, the use of existing methods for eliminating speckle noise can cause the loss of image...

Deep learning for sex determination: Analyzing over 200,000 panoramic radiographs.

Journal of forensic sciences
The objective of this study is to assess the performance of an innovative AI-powered tool for sex determination using panoramic radiographs (PR) and to explore factors affecting the performance of the convolutional neural network (CNN). The study inv...

Examining the Impact of Assistive Technology on Psychological Health, Family Education, and Curriculum Research in Japan: Insights from Artificial Intelligence.

Journal of autism and developmental disorders
This study aims to analyze the effect of psychological health based on artificial intelligence agent technology on the implementation effect of Japanese family education. By combining mobile agent technology and education thought, the system structur...

Analysis of Prospective Genetic Indicators for Prenatal Exposure to Arsenic in Newborn Cord Blood of Using Machine Learning.

Biological trace element research
Using a machine learning methods, we aim to find biological effect biomarkers of prenatal arsenic exposure in newborn cord blood. From the Gene Expression Omnibus (GEO) database, two datasets (GSE48354 and GSE7967) pertaining to cord blood sequencing...

Boosted Additive Angular Margin Loss for breast cancer diagnosis from histopathological images.

Computers in biology and medicine
Pathologists use biopsies and microscopic examination to accurately diagnose breast cancer. This process is time-consuming, labor-intensive, and costly. Convolutional neural networks (CNNs) offer an efficient and highly accurate approach to reduce an...

Deep Learning Can Predict Bevacizumab Therapeutic Effect and Microsatellite Instability Directly from Histology in Epithelial Ovarian Cancer.

Laboratory investigation; a journal of technical methods and pathology
Epithelial ovarian cancer (EOC) remains a significant cause of mortality among gynecologic cancers, with the majority of cases being diagnosed at an advanced stage. Before targeted therapies were available, EOC treatment relied largely on debulking s...

Evaluation of height prediction models: from traditional methods to artificial intelligence.

Pediatric research
BACKGROUND: Traditional methods for predicting adult height (AHP) rely on manual readings of bone age (BA). However, the incorporation of artificial intelligence has recently improved the accuracy of BA readings and their incorporation into AHP model...

Establish and validate the reliability of predictive models in bone mineral density by deep learning as examination tool for women.

Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA
UNLABELLED: While FRAX with BMD could be more precise in estimating the fracture risk, DL-based models were validated to slightly reduce the number of under- and over-treated patients when no BMD measurements were available. The validated models coul...

Forming We-intentions under breakdown situations in human-robot interactions.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: When agents (e.g. a person and a social robot) perform a joint activity to achieve a joint goal, they require sharing a relevant group intention, which has been defined as a We-intention. In forming We-intentions, breakdown ...

Deep learning to predict lymph node status on pre-operative staging CT in patients with colon cancer.

Journal of medical imaging and radiation oncology
INTRODUCTION: Lymph node (LN) metastases are an important determinant of survival in patients with colon cancer, but remain difficult to accurately diagnose on preoperative imaging. This study aimed to develop and evaluate a deep learning model to pr...