AIMC Topic: Female

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Prediction of malignancy upgrade rate in high-risk breast lesions using an artificial intelligence model: a retrospective study.

Diagnostic and interventional radiology (Ankara, Turkey)
PURPOSE: High-risk breast lesions (HRLs) are associated with future risk of breast cancer. Considering the pathological subtypes, malignancy upgrade rate differs according to each subtype and depends on various factors such as clinical and radiologic...

Application of Artificial Intelligence in Anatomical Structure Recognition of Standard Section of Fetal Heart.

Computational and mathematical methods in medicine
Congenital heart defect (CHD) refers to the overall structural abnormality of the heart or large blood vessels in the chest cavity. It is the most common type of fetal congenital defects. Prenatal diagnosis of congenital heart disease can improve the...

Do artificial neural networks love sex? How the combination of artificial neural networks with evolutionary algorithms may help to identify gender influence in rheumatic diseases.

Clinical and experimental rheumatology
Although medical research has been performed predominantly on men both in preclinical and clinical studies, continuous efforts have been made to overcome this gender bias. Examining retrospectively 21 data sets containing sex as one of the descriptiv...

Automatic mammographic breast density classification in Chinese women: clinical validation of a deep learning model.

Acta radiologica (Stockholm, Sweden : 1987)
BACKGROUND: High breast density is a strong risk factor for breast cancer. As such, high consistency and accuracy in breast density assessment is necessary.

Robot-assisted versus conventional laparoscopic radical hysterectomy in cervical cancer stage IB1.

International journal of medical sciences
The aim of this study was to compare survival outcomes of robot-assisted laparoscopic radical hysterectomy (RRH) and conventional laparoscopic radical hysterectomy (LRH) in cervical cancer stage IB1. This is a retrospective study of patients with c...

Machine learning-based approach reveals essential features for simplified TSPO PET quantification in ischemic stroke patients.

Zeitschrift fur medizinische Physik
INTRODUCTION: Neuroinflammation evaluation after acute ischemic stroke is a promising option for selecting an appropriate post-stroke treatment strategy. To assess neuroinflammation in vivo, translocator protein PET (TSPO PET) can be used. However, t...

Prediction of pathologic complete response to neoadjuvant systemic therapy in triple negative breast cancer using deep learning on multiparametric MRI.

Scientific reports
Triple-negative breast cancer (TNBC) is an aggressive subtype of breast cancer. Neoadjuvant systemic therapy (NAST) followed by surgery are currently standard of care for TNBC with 50-60% of patients achieving pathologic complete response (pCR). We i...

Using deep learning to predict survival outcome in non-surgical cervical cancer patients based on pathological images.

Journal of cancer research and clinical oncology
PURPOSE: We analyzed clinical features and the representative HE-stained pathologic images to predict 5-year overall survival via the deep-learning approach in cervical cancer patients in order to assist oncologists in designing the optimal treatment...

Deep Learning for Differentiation of Breast Masses Detected by Screening Ultrasound Elastography.

Ultrasound in medicine & biology
Recently, deep learning using convolutional neural networks (CNNs) has yielded consistent results in image-pattern recognition. This study was aimed at investigating the effectiveness of deep learning using CNNs to differentiate benign and malignant ...

Image Noise Removal in Ultrasound Breast Images Based on Hybrid Deep Learning Technique.

Sensors (Basel, Switzerland)
Rapid improvements in ultrasound imaging technology have made it much more useful for screening and diagnosing breast problems. Local-speckle-noise destruction in ultrasound breast images may impair image quality and impact observation and diagnosis....