AIMC Topic: Male

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Using artificial intelligence to predict post-operative outcomes in congenital heart surgeries: a systematic review.

BMC cardiovascular disorders
INTRODUCTION: Congenital heart disease (CHD) represents the most common group of congenital anomalies, constitutes a significant contributor to the burden of non-communicable diseases, highlighting the critical need for improved risk assessment tools...

Spatially-resolved analyses of muscle invasive bladder cancer microenvironment unveil a distinct fibroblast cluster associated with prognosis.

Frontiers in immunology
BACKGROUND: Muscle-invasive bladder cancer (MIBC) is a prevalent cancer characterized by molecular and clinical heterogeneity. Assessing the spatial heterogeneity of the MIBC microenvironment is crucial to understand its clinical significance.

Artificial Intelligence-Empowered Multistep Integrated Radiation Therapy Workflow for Nasopharyngeal Carcinoma.

International journal of radiation oncology, biology, physics
PURPOSE: To establish an artificial intelligence (AI)-empowered multistep integrated (MSI) radiation therapy (RT) workflow for patients with nasopharyngeal carcinoma (NPC) and evaluate its feasibility and clinical performance.

Evaluation of a deep learning prostate cancer detection system on biparametric MRI against radiological reading.

European radiology
OBJECTIVES: This study aims to evaluate a deep learning pipeline for detecting clinically significant prostate cancer (csPCa), defined as Gleason Grade Group (GGG) ≥ 2, using biparametric MRI (bpMRI) and compare its performance with radiological read...

Machine learning to predict the decision to perform surgery in hepatic echinococcosis.

HPB : the official journal of the International Hepato Pancreato Biliary Association
BACKGROUND: Cystic echinococcosis (CE) is a significant public health issue, primarily affecting the liver. While several management strategies exist, there is a lack of predictive tools to guide surgical decisions for hepatic CE. This study aimed to...

Reliability of post-contrast deep learning-based highly accelerated cardiac cine MRI for the assessment of ventricular function.

Magnetic resonance imaging
OBJECTIVE: The total examination time can be reduced if high-quality two-dimensional (2D) cine images can be collected post-contrast to minimize non-scanning time prior to late gadolinium-enhanced imaging. This study aimed to assess the equivalency o...

Personalized deep learning auto-segmentation models for adaptive fractionated magnetic resonance-guided radiation therapy of the abdomen.

Medical physics
BACKGROUND: Manual contour corrections during fractionated magnetic resonance (MR)-guided radiotherapy (MRgRT) are time-consuming. Conventional population models for deep learning auto-segmentation might be suboptimal for MRgRT at MR-Linacs since the...

Augmented Effect of Combined Robotic Assisted Gait Training and Proprioceptive Neuromuscular Facilitation-irradiation Technique on Muscle Activation and Ankle Kinematics in Hemiparetic Gait: A Preliminary Study.

NeuroRehabilitation
BackgroundProprioceptive neuromuscular facilitation (PNF) alone has limited effectiveness in restoring gait, while robotic-assisted gait training (RAGT) improves motor relearning through repetitive, task-specific movements. Combining PNF with robotic...