AIMC Topic: Aged

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Prediction model for postoperative urinary retention in patients undergoing totally extraperitoneal groin hernia repair.

Surgery
BACKGROUND: Postoperative urinary retention remains a common complication after totally extraperitoneal groin hernia repair, often prolonging hospitalization and increasing patient discomfort. This study aimed to develop a prediction model using mach...

Deep learning for giant cell arteritis diagnosis on temporal artery biopsy.

Computers in biology and medicine
OBJECTIVES: Giant Cell Arteritis (GCA) is a vasculitis affecting large and medium-caliber arteries, requiring early and accurate diagnosis to prevent serious complications. Temporal artery biopsy (TAB) is the gold standard for histopathological diagn...

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...

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...

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...

Improving prediction of fragility fractures in postmenopausal women using random forest.

Computers in biology and medicine
Osteoporosis is a chronic disease characterized by a progressive decline in bone density and quality, leading to increased bone fragility and a higher susceptibility to fractures, even in response to minimal trauma. Osteoporotic fractures represent a...

Developing a Panel of Shared Susceptibility Genes as Diagnostic Biomarkers for chronic obstructive pulmonary disease and Heart Failure.

Computers in biology and medicine
AIM: Chronic obstructive pulmonary disease (COPD) and heart failure (HF) are closely intertwined comorbidities that present significant clinical challenges due to the poorly understood pathophysiological mechanisms driving their coexistence. In this ...

Transformer attention-based neural network for cognitive score estimation from sMRI data.

Computers in biology and medicine
Accurately predicting cognitive scores based on structural MRI holds significant clinical value for understanding the pathological stages of dementia and forecasting Alzheimer's disease (AD). Some existing deep learning methods often depend on anatom...