AIMC Topic: Female

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Analysis of robot-assisted nipple-sparing mastectomy using the da Vinci SP system.

Journal of surgical oncology
BACKGROUND: As patients tend to be diagnosed with breast cancer at an early stage, the demand for better cosmetic outcomes has increased. Several studies revealed that robot-assisted nipple-sparing mastectomy (RNSM) shows favorable outcomes. The aim ...

Robotically performed diagnostic coronary angiography.

Catheterization and cardiovascular interventions : official journal of the Society for Cardiac Angiography & Interventions
OBJECTIVE: This study was performed to investigate the efficacy and safety of robotic diagnostic coronary angiography.

Using deep learning to safely exclude lesions with only ultrafast breast MRI to shorten acquisition and reading time.

European radiology
OBJECTIVES: To investigate the feasibility of automatically identifying normal scans in ultrafast breast MRI with artificial intelligence (AI) to increase efficiency and reduce workload.

Fear Detection in Multimodal Affective Computing: Physiological Signals versus Catecholamine Concentration.

Sensors (Basel, Switzerland)
Affective computing through physiological signals monitoring is currently a hot topic in the scientific literature, but also in the industry. Many wearable devices are being developed for health or wellness tracking during daily life or sports activi...

Dual-energy CT based mass density and relative stopping power estimation for proton therapy using physics-informed deep learning.

Physics in medicine and biology
Proton therapy requires accurate dose calculation for treatment planning to ensure the conformal doses are precisely delivered to the targets. The conversion of CT numbers to material properties is a significant source of uncertainty for dose calcula...

Deep learning prediction of quantitative coronary angiography values using myocardial perfusion images with a CZT camera.

Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology
PURPOSE: Evaluate the prediction of quantitative coronary angiography (QCA) values from MPI, by means of deep learning.

Efficient fetal ultrasound image segmentation for automatic head circumference measurement using a lightweight deep convolutional neural network.

Medical physics
PURPOSE: Fetal head circumference (HC) is an important biometric parameter that can be used to assess fetal development in obstetric clinical practice. Most of the existing methods use deep neural network to accomplish the task of automatic fetal HC ...

Reliable quality assurance of X-ray mammography scanner by evaluation the standard mammography phantom image using an interpretable deep learning model.

European journal of radiology
OBJECTIVE: Mammography is the initial examination to detect breast cancer symptoms, and quality control of mammography devices is crucial to maintain accurate diagnosis and to safeguard against degradation of performance. The objective of this study ...

Impact of a Deep Learning Model for Predicting Mammographic Breast Density in Routine Clinical Practice.

Journal of the American College of Radiology : JACR
OBJECTIVE: Legislation in 38 states requires patient notification of dense mammographic breast tissue because increased density is a marker of breast cancer risk and can limit mammographic sensitivity. Because radiologist density assessments vary wid...

Training, validation, and clinical implementation of a deep-learning segmentation model for radiotherapy of loco-regional breast cancer.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
AIM: To train and validate a comprehensive deep-learning (DL) segmentation model for loco-regional breast cancer with the aim of clinical implementation.