AIMC Topic: Female

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Prediction of Early Neoadjuvant Chemotherapy Response of Breast Cancer through Deep Learning-based Pharmacokinetic Quantification of DCE MRI.

Radiology. Artificial intelligence
Purpose To improve the generalizability of pathologic complete response prediction following neoadjuvant chemotherapy using deep learning-based retrospective pharmacokinetic quantification of early treatment dynamic contrast-enhanced MRI. Materials a...

Utilizing Predictive Analytics to Understand Neurogenic Bladder Symptom Score (NBSS) Variations in Adults With Acquired Spinal Cord Injury.

Neurourology and urodynamics
INTRODUCTION: Individuals with spinal cord injury (SCI) have varying bladder health trajectories after their injury. We explored whether a predictive machine learning model could identify which variables impact urinary symptoms.

Development of Artificial Intelligence-Assisted Lumbar and Femoral BMD Estimation System Using Anteroposterior Lumbar X-Ray Images.

Journal of orthopaedic research : official publication of the Orthopaedic Research Society
The early detection and treatment of osteoporosis and prevention of fragility fractures are urgent societal issues. We developed an artificial intelligence-assisted diagnostic system that estimated not only lumbar bone mineral density but also femora...

Efficient Ultrasound Breast Cancer Detection with DMFormer: A Dynamic Multiscale Fusion Transformer.

Ultrasound in medicine & biology
OBJECTIVE: To develop an advanced deep learning model for accurate differentiation between benign and malignant masses in ultrasound breast cancer screening, addressing the challenges of noise, blur, and complex tissue structures in ultrasound imagin...

Optimal multimodal feature combination and classifier selection for music-based EEG signal analysis.

Computers in biology and medicine
PURPOSE: Music perception is a fundamental human experience, integral to cognitive and emotional processing, making it a crucial area for neuroscientific investigation. This study examined the neural dynamics underlying music perception and identifie...

Machine learning models for predicting chemotherapy-induced adverse drug reactions in colorectal cancer patients.

Digestive and liver disease : official journal of the Italian Society of Gastroenterology and the Italian Association for the Study of the Liver
BACKGROUND: Chemotherapy-induced adverse drug reactions (ADRs) are common in patients with colorectal cancer. We developed four machine learning models to predict chemotherapy-induced ADRs and assessed the performance. These models leverage high-dime...

Predicting acute diarrhoea in rectal cancer chemoradiotherapy: Secondary analysis of the phase III ARISTOTLE trial.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
BACKGROUND: Neoadjuvant chemoradiotherapy is a standard treatment for locally advanced rectal cancer, but acute diarrhoea remains a significant side effect, affecting the completion of chemoradiotherapy treatment.

Improving risk assessment of local failure in brain metastases patients using vision transformers - A multicentric development and validation study.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
BACKGROUND AND PURPOSE: This study investigates the use of Vision Transformers (ViTs) to predict Freedom from Local Failure (FFLF) in patients with brain metastases using pre-operative MRI scans. The goal is to develop a model that enhances risk stra...

Leveraging machine learning and single-cell RNA sequencing strategies to develop a risk prognosis scoring based on liquid-liquid phase separation feature genes in pediatric hepatoblastoma.

Computers in biology and medicine
BACKGROUND: Considerable evidence highlights the intricate association between liquid-liquid phase separation (LLPS) and tumorigenesis, progression, and therapy resistance. However, there has been limited exploration of the role of LLPS in hepatoblas...