AIMC Topic: Female

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Insights Into the Patient Experience of Hormone Therapy for Early Breast Cancer Treatment Using Patient Forum Discussions and Natural Language Processing.

JCO clinical cancer informatics
PURPOSE: Understanding the real-world experience of patients with early breast cancer (eBC) is imperative for optimizing outcomes and evolving patient care. However, there is a lack of patient-level data, hindering clinical development. This social l...

Topographic Clinical Insights From Deep Learning-Based Geographic Atrophy Progression Prediction.

Translational vision science & technology
PURPOSE: To explore the contributions of fundus autofluorescence (FAF) topographic imaging features to the performance of convolutional neural network-based deep learning (DL) algorithms in predicting geographic atrophy (GA) growth rate.

Integrating single-cell transcriptomics and machine learning to predict breast cancer prognosis: A study based on natural killer cell-related genes.

Journal of cellular and molecular medicine
Breast cancer (BC) is the most commonly diagnosed cancer in women globally. Natural killer (NK) cells play a vital role in tumour immunosurveillance. This study aimed to establish a prognostic model using NK cell-related genes (NKRGs) by integrating ...

The diagnostic and triage accuracy of the GPT-3 artificial intelligence model: an observational study.

The Lancet. Digital health
BACKGROUND: Artificial intelligence (AI) applications in health care have been effective in many areas of medicine, but they are often trained for a single task using labelled data, making deployment and generalisability challenging. How well a gener...

DeepComBat: A statistically motivated, hyperparameter-robust, deep learning approach to harmonization of neuroimaging data.

Human brain mapping
Neuroimaging data acquired using multiple scanners or protocols are increasingly available. However, such data exhibit technical artifacts across batches which introduce confounding and decrease reproducibility. This is especially true when multi-bat...

Frontoparietal and salience network synchronizations during nonsymbolic magnitude processing predict brain age and mathematical performance in youth.

Human brain mapping
The development and refinement of functional brain circuits crucial to human cognition is a continuous process that spans from childhood to adulthood. Research increasingly focuses on mapping these evolving configurations, with the aim to identify ma...

Development and clinical validation of a deep learning-based knee CT image segmentation method for robotic-assisted total knee arthroplasty.

The international journal of medical robotics + computer assisted surgery : MRCAS
BACKGROUND: This study aimed to develop a novel deep convolutional neural network called Dual-path Double Attention Transformer (DDA-Transformer) designed to achieve precise and fast knee joint CT image segmentation and to validate it in robotic-assi...

Artificial Intelligence Readiness, Perceptions, and Educational Needs Among Dental Students: A Cross-Sectional Study.

Clinical and experimental dental research
OBJECTIVES: With Artificial Intelligence (AI) profoundly affecting education, ensuring that students in health disciplines are ready to embrace AI is essential for their future workforce integration. This study aims to explore dental students' readin...

An exploratory deep learning approach to investigate tuberculosis pathogenesis in nonhuman primate model: Combining automated radiological analysis with clinical and biomarkers data.

Journal of medical primatology
BACKGROUND: Tuberculosis (TB) kills approximately 1.6 million people yearly despite the fact anti-TB drugs are generally curative. Therefore, TB-case detection and monitoring of therapy, need a comprehensive approach. Automated radiological analysis,...

Stratifying heart failure patients with graph neural network and transformer using Electronic Health Records to optimize drug response prediction.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVES: Heart failure (HF) impacts millions of patients worldwide, yet the variability in treatment responses remains a major challenge for healthcare professionals. The current treatment strategies, largely derived from population based evidence...